system

Abstract

The system according to this embodiment aims to support a rapid and appropriate response in emergency situations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects emergency information from the user. The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. The provision unit provides the countermeasures generated by the analysis unit to the user. The communication unit contacts medical institutions and specialists based on the information generated by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to make a quick and appropriate response in an emergency, and there is room for improvement.

[0005] The system according to the embodiment aims to assist in making a quick and appropriate response in an emergency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects emergency information from users. The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. The provision unit provides the countermeasures generated by the analysis unit to the user. The communication unit contacts medical institutions and specialists based on the information generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can support a rapid and appropriate response in an emergency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Lifeguard System according to an embodiment of the present invention is a system that, when an emergency occurs, interviews the user to understand the situation and quickly generates and provides appropriate advice and action plans. The AI ​​Lifeguard System not only guides the user in an emergency to the correct initial response but also has the function of contacting appropriate parties such as medical institutions and specialists. First, when a user faces an emergency, the AI ​​Lifeguard System collects information from the user. For example, if an accident occurs in front of the user or someone suddenly collapses in front of them, the user reports the situation to the AI ​​Lifeguard System. Based on the information from the user, the AI ​​Lifeguard System analyzes the situation and generates appropriate countermeasures. Next, the AI ​​Lifeguard System provides the user with the generated advice and action plans. For example, it guides the user through specific initial response procedures, such as the procedure for cardiopulmonary resuscitation (CPR) or how to stop bleeding. This allows the user to calmly and quickly take the correct action. Furthermore, the AI ​​Lifeguard System simultaneously contacts medical institutions and specialists. For example, it contacts hospitals and emergency services near the location where the emergency occurred and reports the situation. This allows specialists to respond quickly. This system is designed to solve problems such as a lack of judgment ability and insufficient expertise in emergency situations. The AI ​​lifeguard system supports users in responding calmly in emergencies and provides a mechanism to prevent unhappiness for both those providing and receiving rescues.

[0029] The AI ​​lifeguard system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects emergency information from the user. For example, when a user faces an emergency, the collection unit can collect information through voice input or text input. The collection unit can use AI to analyze the user's input and identify the type and situation of the emergency. The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. For example, the analysis unit can use AI to determine the type and situation of the emergency based on the collected information and generate appropriate countermeasures. The analysis unit can generate specific first-response procedures, such as cardiopulmonary resuscitation (CPR) procedures or methods for stopping bleeding. The provision unit provides the user with the countermeasures generated by the analysis unit. For example, the provision unit can guide the user through specific first-response procedures via voice guidance or text messages. The provision unit can use AI to provide the optimal countermeasure according to the user's situation. The communication unit contacts medical institutions and specialists based on the information generated by the analysis unit. The liaison unit can, for example, contact hospitals or emergency services near the location where the emergency occurred and report the situation. The liaison unit can use AI to automatically report the emergency situation, enabling experts to respond quickly. In this way, the AI ​​lifeguard system according to this embodiment can support users in responding calmly in an emergency and provide a mechanism to prevent unhappiness for both those who rescue and those who are rescued.

[0030] The data collection unit collects emergency information from users. For example, when a user faces an emergency, the data collection unit can collect information through voice input or text input. Specifically, the user can send voice commands or text messages using a smartphone or dedicated device to collect details of the emergency. In the case of voice input, the data collection unit uses speech recognition technology to convert the user's utterance into text data, and in the case of text input, it receives it as text data directly. The data collection unit can use AI to analyze the user's input and identify the type and situation of the emergency. For example, by combining speech recognition technology and natural language processing (NLP), it can identify the type of emergency (e.g., heart attack, bleeding, fire, etc.) from the user's utterance and grasp the situation in detail (e.g., consciousness level, extent of bleeding, scale of fire, etc.). Furthermore, the data collection unit can collect the user's location information and device sensor data (e.g., accelerometer and GPS data) to understand the location of the emergency and the user's movements. This allows the data collection unit to quickly and accurately collect diverse information from users and grasp the overall picture of the emergency. The collected information is transmitted to the analysis unit in real time and used to generate appropriate countermeasures.

[0031] The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. For example, the analysis unit can use AI to determine the type and situation of an emergency based on the collected information and generate appropriate countermeasures. Specifically, the AI ​​uses machine learning algorithms and deep learning models to analyze the collected data and identify patterns in emergencies. For example, in the case of a heart attack, it generates cardiopulmonary resuscitation (CPR) procedures based on the user's statements, location information, and past health data. In the case of bleeding, it identifies the extent and location of the bleeding and provides methods for stopping the bleeding and compression procedures. Furthermore, the analysis unit can predict the progression of an emergency based on the collected information and update necessary countermeasures in real time. For example, in the case of a fire, it predicts the spread of the fire and evacuation routes and provides the user with the optimal evacuation route. The analysis unit can also utilize past data and statistical information to perform risk assessments and trend analysis of emergencies and plan future countermeasures. As a result, the analysis unit can quickly and accurately analyze the collected information and provide the user with the optimal countermeasures.

[0032] The service provider provides users with response strategies generated by the analysis unit. For example, the service provider can guide users through specific initial response procedures via voice guidance or text messages. Specifically, it can use voice guidance to explain each step of cardiopulmonary resuscitation (CPR) and instruct users to perform chest compressions and artificial respiration at the appropriate times. It can also use text messages to provide detailed instructions on specific procedures, such as how to stop bleeding or how to evacuate during a fire. The service provider can use AI to provide optimal response strategies tailored to the user's situation. For example, it can update and provide users with optimal evacuation routes and first-aid procedures in real time based on the user's location and the progress of the emergency. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the response strategies it provides. For example, it can collect the results of users following the provided procedures and provide feedback to the analysis unit to improve the accuracy of future response strategies. This allows the service provider to provide users with quick and appropriate response strategies, supporting them in responding calmly during emergencies.

[0033] The liaison unit contacts medical institutions and specialists based on information generated by the analysis unit. For example, the liaison unit can contact hospitals and emergency services near the location of an emergency and report the situation. Specifically, the liaison unit automatically contacts the nearest medical institutions and emergency services based on the user's location and details of the emergency. The liaison unit can use AI to automatically report the situation of an emergency, enabling specialists to respond quickly. For example, if a user has a heart attack, the liaison unit will report the user's location and the progress of cardiopulmonary resuscitation (CPR) to a medical institution and quickly arrange for an ambulance. The liaison unit can also monitor the progress of the emergency in real time and provide additional information as needed. For example, in the case of a fire, it will report the spread of the fire and the progress of evacuation to the fire department to encourage appropriate action. Furthermore, the liaison unit can automatically contact the user's emergency contacts and report the situation. This allows the liaison unit to make quick and appropriate contacts in an emergency and support specialists and medical institutions in responding quickly.

[0034] The service provider can guide the user through specific first-aid procedures, such as cardiopulmonary resuscitation (CPR) procedures and methods for stopping bleeding. For example, the service provider can guide the user through the procedures for cardiopulmonary resuscitation (CPR). For example, the service provider can also guide the user through methods for stopping bleeding. For example, the service provider can also guide the user through first aid procedures for fractures. This allows the user to quickly understand and perform specific first-aid procedures. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the procedures for cardiopulmonary resuscitation into a generating AI, and the generating AI can guide the user through those procedures.

[0035] The liaison unit can contact hospitals and emergency services near the location of the emergency and report the situation. For example, the liaison unit can contact a hospital near the location of the emergency and report the situation. The liaison unit can also contact emergency services near the location of the emergency and report the situation. The liaison unit can also contact the police near the location of the emergency and report the situation. This allows for quick contact with medical facilities and emergency services near the location of the emergency. Some or all of the above processes by the liaison unit may be performed using AI or not. For example, the liaison unit can input the details of the emergency into a generating AI, which can then report the details to medical facilities and emergency services.

[0036] The data collection unit can analyze the user's past emergency response history and select the optimal information collection method. For example, if the user has experience performing cardiopulmonary resuscitation (CPR), the data collection unit can collect detailed information based on that experience. For example, if the user has experience performing a bleeding stopping method, the data collection unit can prioritize collecting information related to that method. For example, if the user has never dealt with an emergency before, the data collection unit can start collecting basic information. This enables efficient information collection by selecting the optimal information collection method based on the user's past emergency response history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past emergency response history into a generating AI, which can then analyze that history and select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current health status and environment. For example, if the user is in poor health, the data collection unit can ask concise questions and quickly collect information. For example, if the user is in a noisy environment, the data collection unit can avoid voice input and prioritize text input. For example, if the user is in a dark place, the data collection unit can avoid visual information and provide audio guidance. This enables information collection tailored to the user's health status and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user health status and environment data into a generating AI, which can then filter the information collection based on that data.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in an urban area, the data collection unit can prioritize the collection of information on nearby hospitals and emergency services. For example, if the user is in a suburban area, the data collection unit can prioritize the collection of information on the nearest medical facilities. For example, if the user is traveling, the data collection unit can prioritize the collection of information on local emergency contacts and medical facilities. This allows for the priority collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, and the generating AI can prioritize the collection of highly relevant information based on that information.

[0039] The data collection unit can analyze the user's social media activity and collect relevant information when collecting information. For example, if a user posts about an emergency on social media, the data collection unit can collect information based on the content of that post. For example, if a user's social media friends share information about an emergency, the data collection unit can collect that information. For example, if a user shares location information on social media, the data collection unit can collect relevant information based on that location information. In this way, relevant information can be collected based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can analyze that data and collect relevant information.

[0040] The analysis unit can adjust the level of detail of its analysis based on the severity of the emergency. For example, if the emergency is life-threatening, the analysis unit can perform a detailed analysis and quickly provide countermeasures. If the emergency is minor, the analysis unit can perform a concise analysis and provide basic countermeasures. If the emergency is moderate, the analysis unit can perform an analysis with a moderate level of detail and provide appropriate countermeasures. In this way, by adjusting the level of detail of the analysis according to the severity of the emergency, appropriate countermeasures can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency severity data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on that data.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the emergency during analysis. For example, if cardiopulmonary resuscitation (CPR) is required, the analysis unit can apply an analysis algorithm specialized for CPR. For example, if there is bleeding, the analysis unit can apply an analysis algorithm specialized for hemostasis. For example, if a fracture is suspected, the analysis unit can apply an analysis algorithm specialized for first aid. By applying an analysis algorithm appropriate to the category of the emergency, more appropriate countermeasures can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency category data into a generating AI, and the generating AI can apply different analysis algorithms based on that data.

[0042] The analysis unit can determine the priority of analysis based on the timing of the emergency. For example, if the emergency is currently ongoing, the analysis unit will prioritize the analysis. If the emergency occurred in the past, the analysis unit can prioritize the analysis next. If an emergency is predicted, the analysis unit can perform the analysis in advance and prepare countermeasures. This enables a rapid response by determining the priority of analysis based on the timing of the emergency. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency timing data into a generating AI, and the generating AI can determine the priority of analysis based on that data.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the emergencies during the analysis. For example, if multiple emergencies occur, the analysis unit will start with the most relevant situation. For example, if emergencies occur consecutively, the analysis unit can perform the analysis based on a time series. For example, if emergencies belong to different categories, the analysis unit can adjust the order of analysis for each category. By adjusting the order of analysis based on the relevance of the emergencies, appropriate countermeasures can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency relevance data into a generating AI, and the generating AI can adjust the order of analysis based on that data.

[0044] The service provider can adjust the level of detail of the advice based on the severity of the emergency at the time of delivery. For example, if the emergency is life-threatening, the service provider will provide detailed advice. For example, if the emergency is minor, the service provider can provide concise advice. For example, if the emergency is moderate, the service provider can provide advice with an appropriate level of detail. This allows for the provision of appropriate countermeasures by adjusting the level of detail of the advice according to the severity of the emergency. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency severity data into a generating AI, and the generating AI can adjust the level of detail of the advice based on that data.

[0045] The service provider can apply different advice algorithms depending on the category of the emergency at the time of delivery. For example, if cardiopulmonary resuscitation (CPR) is required, the service provider can apply an advice algorithm specifically for CPR. For example, if there is bleeding, the service provider can apply an advice algorithm specifically for hemostasis. For example, if a fracture is suspected, the service provider can apply an advice algorithm specifically for first aid. This allows for the provision of more appropriate countermeasures by applying an advice algorithm appropriate to the category of the emergency. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency category data into a generating AI, and the generating AI can apply different advice algorithms based on that data.

[0046] The service provider can determine the priority of advice based on when the emergency occurred. For example, if the emergency is currently ongoing, the service provider will provide advice with the highest priority. If the emergency occurred in the past, the service provider can provide advice with the next highest priority. If an emergency is anticipated, the service provider can provide advice in advance and prepare countermeasures. This enables a rapid response by determining the priority of advice based on when the emergency occurred. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency occurrence timing data into a generating AI, and the generating AI can determine the priority of advice based on that data.

[0047] The service provider can adjust the order of advice based on the relevance of the emergencies at the time of delivery. For example, if multiple emergencies occur, the service provider will provide advice starting with the most relevant situation. For example, if emergencies occur consecutively, the service provider can provide advice based on chronological order. For example, if emergencies belong to different categories, the service provider can adjust the order of advice for each category. This allows for the provision of appropriate countermeasures by adjusting the order of advice based on the relevance of the emergencies. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency relevance data into a generating AI, and the generating AI can adjust the order of advice based on that data.

[0048] The liaison department can adjust the level of detail in its communications based on the severity of the emergency. For example, if the emergency is life-threatening, the liaison department can provide detailed information. If the emergency is minor, the liaison department can provide concise information. If the emergency is moderate, the liaison department can provide information with an appropriate level of detail. By adjusting the level of detail in communications according to the severity of the emergency, appropriate information can be provided. Some or all of the above processing in the liaison department may be performed using AI or not. For example, the liaison department can input emergency severity data into a generating AI, and the generating AI can adjust the level of detail in communications based on that data.

[0049] The liaison unit can apply different communication algorithms depending on the category of the emergency when making a call. For example, if cardiopulmonary resuscitation (CPR) is required, the liaison unit can apply a communication algorithm specialized for CPR. For example, if there is bleeding, the liaison unit can apply a communication algorithm specialized for hemostasis. For example, if a fracture is suspected, the liaison unit can apply a communication algorithm specialized for first aid. By applying a communication algorithm according to the category of the emergency, more appropriate countermeasures can be provided. Some or all of the above processing in the liaison unit may be performed using AI or not. For example, the liaison unit can input emergency category data into a generating AI, and the generating AI can apply different communication algorithms based on that data.

[0050] The liaison department can select the most appropriate contact based on the location of the emergency when making a call. For example, if the emergency occurs in an urban area, the liaison department will contact a nearby hospital or emergency service. If the emergency occurs in a suburban area, the liaison department can contact the nearest medical facility. If the emergency occurs while traveling, the liaison department can contact a local emergency contact. This allows for a quick and appropriate response by selecting the most appropriate contact based on the location of the emergency. Some or all of the above processes in the liaison department may be performed using AI or not. For example, the liaison department can input emergency location data into a generating AI, which can then select the most appropriate contact based on that data.

[0051] The liaison department can improve the accuracy of its communications by referring to relevant information about the emergency when making contact. For example, the liaison department can select the most suitable contact based on detailed information about the emergency. For example, the liaison department can select the most suitable contact based on the time the emergency occurred. For example, the liaison department can select the most suitable contact based on the location where the emergency occurred. This improves the accuracy of communications by referring to relevant information about the emergency. Some or all of the above processes in the liaison department may be performed using AI or not. For example, the liaison department can input relevant information about the emergency into a generating AI, and the generating AI can select the most suitable contact based on that information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The AI ​​lifeguard system can further generate response strategies by considering the user's health history. For example, if a user has a history of heart disease, it can specifically emphasize and guide the user through cardiopulmonary resuscitation (CPR) procedures. If a user has allergies, it can prioritize providing first aid for allergic reactions. Furthermore, if a user has previously experienced a fracture, it can provide detailed instructions on fracture first aid based on that experience. This enables personalized responses based on the user's health history, resulting in more effective emergency response.

[0054] The AI ​​lifeguard system can further utilize the user's current location information to provide optimal emergency response measures. For example, if the user is in an urban area, it can provide information on nearby hospitals and emergency services to facilitate a quick response. If the user is in a suburban area, it can provide information on the nearest medical facilities and guide them to appropriate actions. Furthermore, if the user is traveling, it can provide information on local emergency contacts and medical facilities to support a quick response. This enables the provision of optimal response measures based on the user's location information, facilitating a rapid response in emergencies.

[0055] The AI ​​lifeguard system can further analyze a user's past emergency response history to provide the most appropriate response. For example, if a user has previously performed cardiopulmonary resuscitation (CPR), it can provide detailed instructions based on that experience. Similarly, if a user has experience stopping bleeding, it can prioritize providing information related to that method. Furthermore, if a user has no prior experience dealing with emergencies, it can begin by guiding them through basic procedures. This enables personalized responses based on the user's past experience, resulting in more effective emergency response.

[0056] The AI ​​lifeguard system can also generate response strategies considering the user's current health condition. For example, if the user is in poor health, it can provide concise instructions to encourage a quick response. If the user is in good health, it can provide detailed instructions and guide the user through more complex first aid procedures. Furthermore, if the user has a specific pre-existing medical condition, it can prioritize providing first aid related to that condition. This enables personalized responses based on the user's health condition, resulting in more effective emergency response.

[0057] The AI ​​lifeguard system can further analyze users' social media activity and provide relevant information. For example, if a user posts about an emergency on social media, it can provide countermeasures based on the content of that post. Similarly, if a user's social media friends share information about an emergency, it can provide countermeasures based on that information. Furthermore, if a user shares their location on social media, it can provide the most appropriate countermeasures based on that location. This enables personalized responses based on users' social media activity, resulting in more effective emergency response.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects emergency information from the user. For example, when a user faces an emergency, the collection unit can collect information through voice input or text input. The collection unit can use AI to analyze the user's input and identify the type and circumstances of the emergency. Step 2: The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. For example, the analysis unit can use AI to determine the type and situation of an emergency based on the collected information and generate appropriate countermeasures. The analysis unit can generate specific first-response procedures, such as cardiopulmonary resuscitation (CPR) procedures and methods for stopping bleeding. Step 3: The service provider provides the user with the countermeasures generated by the analysis unit. The service provider can guide the user through specific initial response steps, for example, via voice guidance or text messages. The service provider can use AI to provide the optimal countermeasures tailored to the user's situation. Step 4: The liaison unit contacts healthcare facilities and specialists based on the information generated by the analysis unit. For example, the liaison unit can contact hospitals and emergency services near the location of the emergency and report the situation. The liaison unit can use AI to automatically report the situation of the emergency, enabling specialists to respond quickly.

[0060] (Example of form 2) The AI ​​Lifeguard System according to an embodiment of the present invention is a system that, when an emergency occurs, interviews the user to understand the situation and quickly generates and provides appropriate advice and action plans. The AI ​​Lifeguard System not only guides the user in an emergency to the correct initial response but also has the function of contacting appropriate parties such as medical institutions and specialists. First, when a user faces an emergency, the AI ​​Lifeguard System collects information from the user. For example, if an accident occurs in front of the user or someone suddenly collapses in front of them, the user reports the situation to the AI ​​Lifeguard System. Based on the information from the user, the AI ​​Lifeguard System analyzes the situation and generates appropriate countermeasures. Next, the AI ​​Lifeguard System provides the user with the generated advice and action plans. For example, it guides the user through specific initial response procedures, such as the procedure for cardiopulmonary resuscitation (CPR) or how to stop bleeding. This allows the user to calmly and quickly take the correct action. Furthermore, the AI ​​Lifeguard System simultaneously contacts medical institutions and specialists. For example, it contacts hospitals and emergency services near the location where the emergency occurred and reports the situation. This allows specialists to respond quickly. This system is designed to solve problems such as a lack of judgment ability and insufficient expertise in emergency situations. The AI ​​lifeguard system supports users in responding calmly in emergencies and provides a mechanism to prevent unhappiness for both those providing and receiving rescues.

[0061] The AI ​​lifeguard system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects emergency information from the user. For example, when a user faces an emergency, the collection unit can collect information through voice input or text input. The collection unit can use AI to analyze the user's input and identify the type and situation of the emergency. The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. For example, the analysis unit can use AI to determine the type and situation of the emergency based on the collected information and generate appropriate countermeasures. The analysis unit can generate specific first-response procedures, such as cardiopulmonary resuscitation (CPR) procedures or methods for stopping bleeding. The provision unit provides the user with the countermeasures generated by the analysis unit. For example, the provision unit can guide the user through specific first-response procedures via voice guidance or text messages. The provision unit can use AI to provide the optimal countermeasure according to the user's situation. The communication unit contacts medical institutions and specialists based on the information generated by the analysis unit. The liaison unit can, for example, contact hospitals or emergency services near the location where the emergency occurred and report the situation. The liaison unit can use AI to automatically report the emergency situation, enabling experts to respond quickly. In this way, the AI ​​lifeguard system according to this embodiment can support users in responding calmly in an emergency and provide a mechanism to prevent unhappiness for both those who rescue and those who are rescued.

[0062] The data collection unit collects emergency information from users. For example, when a user faces an emergency, the data collection unit can collect information through voice input or text input. Specifically, the user can send voice commands or text messages using a smartphone or dedicated device to collect details of the emergency. In the case of voice input, the data collection unit uses speech recognition technology to convert the user's utterance into text data, and in the case of text input, it receives it as text data directly. The data collection unit can use AI to analyze the user's input and identify the type and situation of the emergency. For example, by combining speech recognition technology and natural language processing (NLP), it can identify the type of emergency (e.g., heart attack, bleeding, fire, etc.) from the user's utterance and grasp the situation in detail (e.g., consciousness level, extent of bleeding, scale of fire, etc.). Furthermore, the data collection unit can collect the user's location information and device sensor data (e.g., accelerometer and GPS data) to understand the location of the emergency and the user's movements. This allows the data collection unit to quickly and accurately collect diverse information from users and grasp the overall picture of the emergency. The collected information is transmitted to the analysis unit in real time and used to generate appropriate countermeasures.

[0063] The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. For example, the analysis unit can use AI to determine the type and situation of an emergency based on the collected information and generate appropriate countermeasures. Specifically, the AI ​​uses machine learning algorithms and deep learning models to analyze the collected data and identify patterns in emergencies. For example, in the case of a heart attack, it generates cardiopulmonary resuscitation (CPR) procedures based on the user's statements, location information, and past health data. In the case of bleeding, it identifies the extent and location of the bleeding and provides methods for stopping the bleeding and compression procedures. Furthermore, the analysis unit can predict the progression of an emergency based on the collected information and update necessary countermeasures in real time. For example, in the case of a fire, it predicts the spread of the fire and evacuation routes and provides the user with the optimal evacuation route. The analysis unit can also utilize past data and statistical information to perform risk assessments and trend analysis of emergencies and plan future countermeasures. As a result, the analysis unit can quickly and accurately analyze the collected information and provide the user with the optimal countermeasures.

[0064] The service provider provides users with response strategies generated by the analysis unit. For example, the service provider can guide users through specific initial response procedures via voice guidance or text messages. Specifically, it can use voice guidance to explain each step of cardiopulmonary resuscitation (CPR) and instruct users to perform chest compressions and artificial respiration at the appropriate times. It can also use text messages to provide detailed instructions on specific procedures, such as how to stop bleeding or how to evacuate during a fire. The service provider can use AI to provide optimal response strategies tailored to the user's situation. For example, it can update and provide users with optimal evacuation routes and first-aid procedures in real time based on the user's location and the progress of the emergency. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the response strategies it provides. For example, it can collect the results of users following the provided procedures and provide feedback to the analysis unit to improve the accuracy of future response strategies. This allows the service provider to provide users with quick and appropriate response strategies, supporting them in responding calmly during emergencies.

[0065] The liaison unit contacts medical institutions and specialists based on information generated by the analysis unit. For example, the liaison unit can contact hospitals and emergency services near the location of an emergency and report the situation. Specifically, the liaison unit automatically contacts the nearest medical institutions and emergency services based on the user's location and details of the emergency. The liaison unit can use AI to automatically report the situation of an emergency, enabling specialists to respond quickly. For example, if a user has a heart attack, the liaison unit will report the user's location and the progress of cardiopulmonary resuscitation (CPR) to a medical institution and quickly arrange for an ambulance. The liaison unit can also monitor the progress of the emergency in real time and provide additional information as needed. For example, in the case of a fire, it will report the spread of the fire and the progress of evacuation to the fire department to encourage appropriate action. Furthermore, the liaison unit can automatically contact the user's emergency contacts and report the situation. This allows the liaison unit to make quick and appropriate contacts in an emergency and support specialists and medical institutions in responding quickly.

[0066] The service provider can guide the user through specific first-aid procedures, such as cardiopulmonary resuscitation (CPR) procedures and methods for stopping bleeding. For example, the service provider can guide the user through the procedures for cardiopulmonary resuscitation (CPR). For example, the service provider can also guide the user through methods for stopping bleeding. For example, the service provider can also guide the user through first aid procedures for fractures. This allows the user to quickly understand and perform specific first-aid procedures. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the procedures for cardiopulmonary resuscitation into a generating AI, and the generating AI can guide the user through those procedures.

[0067] The liaison unit can contact hospitals and emergency services near the location of the emergency and report the situation. For example, the liaison unit can contact a hospital near the location of the emergency and report the situation. The liaison unit can also contact emergency services near the location of the emergency and report the situation. The liaison unit can also contact the police near the location of the emergency and report the situation. This allows for quick contact with medical facilities and emergency services near the location of the emergency. Some or all of the above processes by the liaison unit may be performed using AI or not. For example, the liaison unit can input the details of the emergency into a generating AI, which can then report the details to medical facilities and emergency services.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is in a state of panic, the data collection unit can immediately begin collecting information to encourage a quick response. For example, if the user is calm, the data collection unit can take its time to collect detailed information. For example, if the user is confused, the data collection unit can ask concise questions and quickly collect the minimum necessary information. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection based on those emotions.

[0069] The data collection unit can analyze the user's past emergency response history and select the optimal information collection method. For example, if the user has experience performing cardiopulmonary resuscitation (CPR), the data collection unit can collect detailed information based on that experience. For example, if the user has experience performing a bleeding stopping method, the data collection unit can prioritize collecting information related to that method. For example, if the user has never dealt with an emergency before, the data collection unit can start collecting basic information. This enables efficient information collection by selecting the optimal information collection method based on the user's past emergency response history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past emergency response history into a generating AI, which can then analyze that history and select the optimal information collection method.

[0070] The data collection unit can filter information based on the user's current health status and environment. For example, if the user is in poor health, the data collection unit can ask concise questions and quickly collect information. For example, if the user is in a noisy environment, the data collection unit can avoid voice input and prioritize text input. For example, if the user is in a dark place, the data collection unit can avoid visual information and provide audio guidance. This enables information collection tailored to the user's health status and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user health status and environment data into a generating AI, which can then filter the information collection based on that data.

[0071] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is in a state of panic, the data collection unit will prioritize collecting the most important information. For example, if the user is calm, the data collection unit can take its time to collect detailed information. For example, if the user is confused, the data collection unit can ask concise questions and quickly collect the minimum necessary information. This allows for the priority collection of important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of information based on those emotions.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in an urban area, the data collection unit can prioritize the collection of information on nearby hospitals and emergency services. For example, if the user is in a suburban area, the data collection unit can prioritize the collection of information on the nearest medical facilities. For example, if the user is traveling, the data collection unit can prioritize the collection of information on local emergency contacts and medical facilities. This allows for the priority collection of highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, and the generating AI can prioritize the collection of highly relevant information based on that information.

[0073] The data collection unit can analyze the user's social media activity and collect relevant information when collecting information. For example, if a user posts about an emergency on social media, the data collection unit can collect information based on the content of that post. For example, if a user's social media friends share information about an emergency, the data collection unit can collect that information. For example, if a user shares location information on social media, the data collection unit can collect relevant information based on that location information. In this way, relevant information can be collected based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can analyze that data and collect relevant information.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is panicking, the analysis unit can use concise and clear language. If the user is calm, the analysis unit can use language that includes detailed explanations. If the user is confused, the analysis unit can provide the analysis results using visual guidance. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis based on those emotions.

[0075] The analysis unit can adjust the level of detail of its analysis based on the severity of the emergency. For example, if the emergency is life-threatening, the analysis unit can perform a detailed analysis and quickly provide countermeasures. If the emergency is minor, the analysis unit can perform a concise analysis and provide basic countermeasures. If the emergency is moderate, the analysis unit can perform an analysis with a moderate level of detail and provide appropriate countermeasures. In this way, by adjusting the level of detail of the analysis according to the severity of the emergency, appropriate countermeasures can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency severity data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on that data.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the emergency during analysis. For example, if cardiopulmonary resuscitation (CPR) is required, the analysis unit can apply an analysis algorithm specialized for CPR. For example, if there is bleeding, the analysis unit can apply an analysis algorithm specialized for hemostasis. For example, if a fracture is suspected, the analysis unit can apply an analysis algorithm specialized for first aid. By applying an analysis algorithm appropriate to the category of the emergency, more appropriate countermeasures can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency category data into a generating AI, and the generating AI can apply different analysis algorithms based on that data.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is panicking, the analysis unit can perform a short, concise analysis. If the user is calm, for example, the analysis unit can perform a detailed analysis. If the user is confused, for example, the analysis unit can perform a concise, visual analysis. By adjusting the length of the analysis according to the user's emotions, appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the length of the analysis based on that emotion.

[0078] The analysis unit can determine the priority of analysis based on the timing of the emergency. For example, if the emergency is currently ongoing, the analysis unit will prioritize the analysis. If the emergency occurred in the past, the analysis unit can prioritize the analysis next. If an emergency is predicted, the analysis unit can perform the analysis in advance and prepare countermeasures. This enables a rapid response by determining the priority of analysis based on the timing of the emergency. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency timing data into a generating AI, and the generating AI can determine the priority of analysis based on that data.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the emergencies during the analysis. For example, if multiple emergencies occur, the analysis unit will start with the most relevant situation. For example, if emergencies occur consecutively, the analysis unit can perform the analysis based on a time series. For example, if emergencies belong to different categories, the analysis unit can adjust the order of analysis for each category. By adjusting the order of analysis based on the relevance of the emergencies, appropriate countermeasures can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input emergency relevance data into a generating AI, and the generating AI can adjust the order of analysis based on that data.

[0080] The service provider can estimate the user's emotions and adjust the way it expresses the advice based on the estimated emotions. For example, if the user is panicking, the service provider can provide concise and clear advice. If the user is calm, the service provider can provide advice that includes detailed explanations. If the user is confused, the service provider can provide advice using visual guidance. This allows for more easily understood advice by adjusting the way it is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can then adjust the way it expresses the advice based on those emotions.

[0081] The service provider can adjust the level of detail of the advice based on the severity of the emergency at the time of delivery. For example, if the emergency is life-threatening, the service provider will provide detailed advice. For example, if the emergency is minor, the service provider can provide concise advice. For example, if the emergency is moderate, the service provider can provide advice with an appropriate level of detail. This allows for the provision of appropriate countermeasures by adjusting the level of detail of the advice according to the severity of the emergency. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency severity data into a generating AI, and the generating AI can adjust the level of detail of the advice based on that data.

[0082] The service provider can apply different advice algorithms depending on the category of the emergency at the time of delivery. For example, if cardiopulmonary resuscitation (CPR) is required, the service provider can apply an advice algorithm specifically for CPR. For example, if there is bleeding, the service provider can apply an advice algorithm specifically for hemostasis. For example, if a fracture is suspected, the service provider can apply an advice algorithm specifically for first aid. This allows for the provision of more appropriate countermeasures by applying an advice algorithm appropriate to the category of the emergency. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency category data into a generating AI, and the generating AI can apply different advice algorithms based on that data.

[0083] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is panicking, the service provider can provide short, concise advice. If the user is calm, the service provider can provide detailed advice. If the user is confused, the service provider can provide concise, visual advice. This allows the service provider to provide appropriate information by adjusting the length of the advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can then adjust the length of the advice based on those emotions.

[0084] The service provider can determine the priority of advice based on when the emergency occurred. For example, if the emergency is currently ongoing, the service provider will provide advice with the highest priority. If the emergency occurred in the past, the service provider can provide advice with the next highest priority. If an emergency is anticipated, the service provider can provide advice in advance and prepare countermeasures. This enables a rapid response by determining the priority of advice based on when the emergency occurred. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency occurrence timing data into a generating AI, and the generating AI can determine the priority of advice based on that data.

[0085] The service provider can adjust the order of advice based on the relevance of the emergencies at the time of delivery. For example, if multiple emergencies occur, the service provider will provide advice starting with the most relevant situation. For example, if emergencies occur consecutively, the service provider can provide advice based on chronological order. For example, if emergencies belong to different categories, the service provider can adjust the order of advice for each category. This allows for the provision of appropriate countermeasures by adjusting the order of advice based on the relevance of the emergencies. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input emergency relevance data into a generating AI, and the generating AI can adjust the order of advice based on that data.

[0086] The communication unit can estimate the user's emotions and adjust the method of communication based on the estimated emotions. For example, if the user is in a state of panic, the communication unit can quickly and automatically contact the user. For example, if the user is calm, the communication unit can contact the user while providing detailed information. For example, if the user is confused, the communication unit can contact the user while providing concise information. This allows for quick and appropriate communication by adjusting the method of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can adjust the method of communication based on that emotion.

[0087] The liaison department can adjust the level of detail in its communications based on the severity of the emergency. For example, if the emergency is life-threatening, the liaison department can provide detailed information. If the emergency is minor, the liaison department can provide concise information. If the emergency is moderate, the liaison department can provide information with an appropriate level of detail. By adjusting the level of detail in communications according to the severity of the emergency, appropriate information can be provided. Some or all of the above processing in the liaison department may be performed using AI or not. For example, the liaison department can input emergency severity data into a generating AI, and the generating AI can adjust the level of detail in communications based on that data.

[0088] The liaison unit can apply different communication algorithms depending on the category of the emergency when making a call. For example, if cardiopulmonary resuscitation (CPR) is required, the liaison unit can apply a communication algorithm specialized for CPR. For example, if there is bleeding, the liaison unit can apply a communication algorithm specialized for hemostasis. For example, if a fracture is suspected, the liaison unit can apply a communication algorithm specialized for first aid. By applying a communication algorithm according to the category of the emergency, more appropriate countermeasures can be provided. Some or all of the above processing in the liaison unit may be performed using AI or not. For example, the liaison unit can input emergency category data into a generating AI, and the generating AI can apply different communication algorithms based on that data.

[0089] The communication unit can estimate the user's emotions and determine the priority of communication based on the estimated emotions. For example, if the user is in a state of panic, the communication unit will prioritize contacting the most important contacts. For example, if the user is calm, the communication unit can provide detailed information during the communication. For example, if the user is confused, the communication unit can provide concise information during the communication. This enables quick and appropriate communication by determining the priority of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can determine the priority of communication based on that emotion.

[0090] The liaison department can select the most appropriate contact based on the location of the emergency when making a call. For example, if the emergency occurs in an urban area, the liaison department will contact a nearby hospital or emergency service. If the emergency occurs in a suburban area, the liaison department can contact the nearest medical facility. If the emergency occurs while traveling, the liaison department can contact a local emergency contact. This allows for a quick and appropriate response by selecting the most appropriate contact based on the location of the emergency. Some or all of the above processes in the liaison department may be performed using AI or not. For example, the liaison department can input emergency location data into a generating AI, which can then select the most appropriate contact based on that data.

[0091] The liaison department can improve the accuracy of its communications by referring to relevant information about the emergency when making contact. For example, the liaison department can select the most suitable contact based on detailed information about the emergency. For example, the liaison department can select the most suitable contact based on the time the emergency occurred. For example, the liaison department can select the most suitable contact based on the location where the emergency occurred. This improves the accuracy of communications by referring to relevant information about the emergency. Some or all of the above processes in the liaison department may be performed using AI or not. For example, the liaison department can input relevant information about the emergency into a generating AI, and the generating AI can select the most suitable contact based on that information.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The AI ​​lifeguard system can further generate response strategies by considering the user's health history. For example, if a user has a history of heart disease, it can specifically emphasize and guide the user through cardiopulmonary resuscitation (CPR) procedures. If a user has allergies, it can prioritize providing first aid for allergic reactions. Furthermore, if a user has previously experienced a fracture, it can provide detailed instructions on fracture first aid based on that experience. This enables personalized responses based on the user's health history, resulting in more effective emergency response.

[0094] The AI ​​lifeguard system can estimate the user's emotions and customize emergency response strategies based on those emotions. For example, if the user is panicking, it can provide simple and clear instructions and guide the user through breathing techniques to regain composure. If the user is calm, it can provide detailed procedures and guide the user through more complex first aid. Furthermore, if the user is confused, it can use visual guides to explain the procedures and aid understanding. This allows for flexible responses tailored to the user's emotions, providing effective support in emergencies.

[0095] The AI ​​lifeguard system can further utilize the user's current location information to provide optimal emergency response measures. For example, if the user is in an urban area, it can provide information on nearby hospitals and emergency services to facilitate a quick response. If the user is in a suburban area, it can provide information on the nearest medical facilities and guide them to appropriate actions. Furthermore, if the user is traveling, it can provide information on local emergency contacts and medical facilities to support a quick response. This enables the provision of optimal response measures based on the user's location information, facilitating a rapid response in emergencies.

[0096] The AI ​​lifeguard system can estimate the user's emotions and prioritize emergency response measures based on those emotions. For example, if the user is panicking, it can prioritize providing the most important response measures and guide the user through steps to regain their composure. If the user is calm, it can provide detailed instructions and guide the user through multiple response measures sequentially. Furthermore, if the user is confused, it can provide concise instructions and quickly deliver the minimum necessary information. This enables prioritization according to the user's emotions, resulting in an effective response in emergencies.

[0097] The AI ​​lifeguard system can further analyze a user's past emergency response history to provide the most appropriate response. For example, if a user has previously performed cardiopulmonary resuscitation (CPR), it can provide detailed instructions based on that experience. Similarly, if a user has experience stopping bleeding, it can prioritize providing information related to that method. Furthermore, if a user has no prior experience dealing with emergencies, it can begin by guiding them through basic procedures. This enables personalized responses based on the user's past experience, resulting in more effective emergency response.

[0098] The AI ​​lifeguard system can estimate the user's emotions and provide step-by-step emergency response strategies based on those emotions. For example, if the user is panicking, it can first provide simple steps and gradually guide them through more detailed steps. If the user is calm, it can provide detailed steps from the beginning to encourage a quick response. Furthermore, if the user is confused, it can use visual guides to explain the steps step by step to aid understanding. This enables a step-by-step response tailored to the user's emotions, providing effective support in emergency situations.

[0099] The AI ​​lifeguard system can also generate response strategies considering the user's current health condition. For example, if the user is in poor health, it can provide concise instructions to encourage a quick response. If the user is in good health, it can provide detailed instructions and guide the user through more complex first aid procedures. Furthermore, if the user has a specific pre-existing medical condition, it can prioritize providing first aid related to that condition. This enables personalized responses based on the user's health condition, resulting in more effective emergency response.

[0100] The AI ​​lifeguard system can estimate a user's emotions and visually provide emergency response strategies based on those emotions. For example, if the user is panicking, it can explain the steps using simple illustrations and icons. If the user is calm, it can explain the steps using detailed diagrams to enhance understanding. Furthermore, if the user is confused, it can use animations and videos to visually explain the steps and aid understanding. This enables visual responses tailored to the user's emotions, providing effective support in emergency situations.

[0101] The AI ​​lifeguard system can further analyze users' social media activity and provide relevant information. For example, if a user posts about an emergency on social media, it can provide countermeasures based on the content of that post. Similarly, if a user's social media friends share information about an emergency, it can provide countermeasures based on that information. Furthermore, if a user shares their location on social media, it can provide the most appropriate countermeasures based on that location. This enables personalized responses based on users' social media activity, resulting in more effective emergency response.

[0102] The AI ​​lifeguard system can estimate a user's emotions and provide emergency response strategies via voice based on those emotions. For example, if a user is panicking, it can provide concise instructions in a calm voice, guiding the user through steps to regain composure. If the user is calm, it can provide detailed instructions via voice to encourage a quick response. Furthermore, if the user is confused, it can explain the steps via voice in conjunction with visual guidance to aid understanding. This enables voice responses tailored to the user's emotions, providing effective support in emergency situations.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The collection unit collects emergency information from the user. For example, when a user faces an emergency, the collection unit can collect information through voice input or text input. The collection unit can use AI to analyze the user's input and identify the type and circumstances of the emergency. Step 2: The analysis unit analyzes the information collected by the collection unit and generates appropriate countermeasures. For example, the analysis unit can use AI to determine the type and situation of an emergency based on the collected information and generate appropriate countermeasures. The analysis unit can generate specific first-response procedures, such as cardiopulmonary resuscitation (CPR) procedures and methods for stopping bleeding. Step 3: The service provider provides the user with the countermeasures generated by the analysis unit. The service provider can guide the user through specific initial response steps, for example, via voice guidance or text messages. The service provider can use AI to provide the optimal countermeasures tailored to the user's situation. Step 4: The liaison unit contacts healthcare facilities and specialists based on the information generated by the analysis unit. For example, the liaison unit can contact hospitals and emergency services near the location of the emergency and report the situation. The liaison unit can use AI to automatically report the situation of the emergency, enabling specialists to respond quickly.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects emergency information from the user using the microphone 38B and touch panel 38A of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates appropriate countermeasures based on the collected information. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides countermeasures to the user through voice guidance or text messages. The communication unit is implemented in the specific processing unit 290 of the data processing unit 12 and contacts medical institutions and specialists. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and communication unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects emergency information from the user using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates appropriate countermeasures based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides countermeasures to the user through voice guidance or text messages. The communication unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and contacts medical institutions or specialists. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects emergency information from the user using the microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates appropriate countermeasures based on the collected information. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides countermeasures to the user through voice guidance or text messages. The communication unit is implemented in the specific processing unit 290 of the data processing unit 12 and contacts medical institutions or specialists. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and communication unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects emergency information from the user using the microphone 238 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates appropriate countermeasures based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides countermeasures to the user through voice guidance or text messages. The communication unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and contacts medical institutions or specialists. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A collection unit that collects emergency information from users, An analysis unit analyzes the information collected by the aforementioned collection unit and generates appropriate countermeasures, A provisioning unit that provides the user with the countermeasures generated by the analysis unit, The system includes a communication unit that contacts medical institutions and specialists based on the information generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, The system guides users through specific first-aid procedures, such as cardiopulmonary resuscitation (CPR) and methods for stopping bleeding. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned liaison department, Contact a hospital or emergency service near where the emergency occurred and report the situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past emergency response history and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current health status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the severity of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the emergency occurred. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis will be adjusted based on the relevance of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the severity of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, different advice algorithms are applied depending on the category of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, we prioritize it based on when the emergency occurred. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, the order of advice will be adjusted based on the relevance of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned liaison department, It estimates the user's emotions and adjusts the method of communication based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned liaison department, When contacting someone, adjust the level of detail based on the severity of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned liaison department, When contacting someone, different contact algorithms are applied depending on the category of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned liaison department, It estimates the user's emotions and determines the priority of communication based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned liaison department, When contacting someone, the most appropriate contact person will be selected based on the location of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned liaison department, When making contact, refer to relevant emergency information to improve the accuracy of the communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects emergency information from users, An analysis unit analyzes the information collected by the aforementioned collection unit and generates appropriate countermeasures, A provisioning unit that provides the user with the countermeasures generated by the analysis unit, The system includes a communication unit that contacts medical institutions and specialists based on the information generated by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, The system guides users through specific first-aid procedures, such as cardiopulmonary resuscitation (CPR) and methods for stopping bleeding. The system according to feature 1.

3. The aforementioned liaison department, Contact a hospital or emergency service near where the emergency occurred and report the situation. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze the user's past emergency response history and select the most suitable information gathering method. The system according to feature 1.

6. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current health status and environment. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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