Life-saving system

Abstract

Organ transplantation improves the efficiency of life-saving medical care. [Solution] The life-saving system 1 is a system for providing organs from a donor to a recipient. The life-saving system 1 includes a potential recipient prediction unit 20 that predicts potential recipients, a potential recipient information management unit 30 that manages information on potential recipients, a potential donor information management unit 40 that manages information on potential donors, a transplant condition suitability detection unit 50 that detects potential donors that meet the transplant conditions for each potential recipient based on the information on potential recipients and potential donors, a health condition monitoring unit 60 that monitors the health condition of each potential donor, and a donor selection unit 70 that selects potential donors who meet the transplant conditions and have reached a predetermined health condition as donors.

Description

Technical Field

[0001] The present invention relates to applications such as multi-agent technology in life-saving medicine.

Background Art

[0002] Organ transplantation is one of the options for life-saving medicine. For the success of organ transplantation, when a provider (donor) with a compatible organ appears, it is essential to have a system that can quickly remove the organ and provide it to the transplant recipient (recipient) in a transplantable state.

Prior Art Documents

Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem to be solved by the present invention is to provide a life-saving system that can improve the efficiency of life-saving medicine by organ transplantation.

Means for Solving the Problems

[0005] One embodiment is A life-saving system for providing a donor's organ to a recipient, A recipient information management unit that manages the information of the recipient, A potential donor information management unit that manages the information of potential donors, A transplant condition matcher detection unit that detects potential donors who match the transplant conditions for each recipient based on the information of the recipient and the information of the potential donors, A health status monitoring unit that monitors the health status of each potential donor, The life-saving system includes a donor selection unit that selects a potential donor who meets the transplant conditions and is in a predetermined state of health as the donor.

[0006] Another embodiment is, A life-saving system that provides organs from a donor to a recipient, A potential recipient prediction unit that predicts potential recipients, A potential recipient information management unit manages information on the aforementioned potential recipients, The Potential Donor Information Management Department manages information on potential donors, A transplant condition suitability detection unit detects a potential donor that is suitable for the transplant conditions of each potential recipient, based on the information of the potential recipient and the information of the potential donor. A health status monitoring unit that monitors the health status of each potential donor, The life-saving system includes a donor selection unit that selects a potential donor who meets the transplant conditions and is in a predetermined state of health as the donor. [Effects of the Invention]

[0007] The life-saving system of the present invention can make life-saving medical treatment through organ transplantation more efficient. [Brief explanation of the drawing]

[0008] [Figure 1] This is a diagram showing the configuration of the life-saving system according to the first embodiment. [Figure 2] This is a functional block diagram of the organ donation management system. [Figure 3] This is the hardware configuration of the emergency call center system. [Figure 4] This is the hardware configuration of an emergency call device. [Figure 5] This is the hardware configuration of the organ donation management system. [Figure 6] This figure shows an example of processing by an emergency call center system and an emergency call device. [Figure 7]It is a diagram showing an example of processing by an emergency reporting center device. [Figure 8] It is a diagram showing an example of processing by an emergency reporting center device. [Figure 9] It is a diagram showing an example of processing by an emergency reporting center device. [Figure 10] It is a diagram showing an example of processing by an emergency reporting center device. [Figure 11] It is a diagram showing an example of processing by an emergency reporting center device. [Figure 12] It is a diagram showing an example of processing by an emergency reporting device. [Figure 13] It is a configuration diagram of the lifesaving system of the second embodiment. [Figure 14] It is a configuration diagram of the lifesaving system of the third embodiment. [Figure 15] It is a diagram showing an example of processing by an emergency reporting center device and an emergency responder device. [Figure 16] It is a diagram showing an example of processing by an emergency responder device. [Figure 17] It is a diagram showing an example of an emergency response request content display screen. [Figure 18] It is a diagram showing an example of processing by the emergency reporting center device of the fourth embodiment. [Figure 19] It is a diagram showing an example of processing by the emergency reporting center device of the fifth embodiment. [Figure 20] It is a functional block diagram of an organ donation management device in the sixth embodiment. [Figure 21] It is a diagram showing an example of processing by the emergency reporting center device of the sixth embodiment.

Modes for Carrying Out the Invention

[0009] Embodiments of the present invention will be described below with reference to the drawings. This embodiment relates to a life-saving system that dramatically accelerates the organ donation process and improves the success rate of organ transplants, based on an emergency notification system (life-saving system) that applies multi-AI agent technology. Specifically, by monitoring the health status of potential donors in real time and evaluating the compatibility with recipients and potential recipients in advance, it becomes possible to start a rapid organ donation process as soon as a donor is found.

[0010] 1. About Terminology In this embodiment, the meanings of the following terms are generally as follows. Note that duplicate descriptions of the same (similar) content are permitted in the following description.

[0011] A donor database is a system for registering and managing information about people who may be able to donate organs. The donor database registers and manages information on an individual basis for people who have expressed their intention to donate organs. More specifically, the donor database registers and manages information on the types of organs that can be donated after brain death (heart, lungs, liver, kidneys, pancreas, small intestine, eyeballs, etc.) and the types of organs that can be donated after death when the heart has stopped (kidneys, pancreas, eyeballs, etc.), as information for each individual who has expressed their intention to donate organs.

[0012] A potential donor refers to an individual who could potentially become a donor (organ provider, organ source). Potential donors include both those registered in the donor database and those not registered in the donor database.

[0013] A recipient refers to an individual who is registered on a waiting list as someone who wishes to receive an organ transplant.

[0014] A waiting list is a list of recipients registered to await organ donation. An example of a waiting list is the one used by the Japan Organ Transplant Network (JOTNW). JOTNW centrally manages recipient information and fairly and impartially selects candidates for organ transplants based on medical criteria, urgency, and waiting period.

[0015] Potential recipients refer to individuals or groups of individuals who are not currently on the waiting list but are expected to be admitted to the waiting list within a certain period of time in the future after meeting the criteria for diagnosis, disease progression, and eligibility. The purpose of predicting potential recipients is to facilitate long-term clinical resource allocation, policy effectiveness evaluation, hospital-level capacity planning, and the design of early intervention strategies.

[0016] Referral refers to the act or process of sending a patient who may require organ transplantation from a general medical institution or specialist to a facility or department specializing in organ transplantation for specialized treatment and evaluation. Information regarding referrals is used as input data for potential recipient prediction models (hereinafter also simply referred to as "prediction models"), events in modeling approaches, and items for bias correction of prediction models, etc.

[0017] A predictive model is a model used to calculate (predict / estimate) potential recipients. In predictive models, information on how often patients are referred to specialists is an important factor in estimating the probability of subsequent disease progression and being placed on a waiting list. For example, in the microsimulation (individual-based) probability rule, one of the predictive models, "referral" means that the patient receives evaluation from a specialist as a step before being registered on the transplant waiting list. The "bias" in bias correction of predictive models refers to the practical bias that patients are not registered on the waiting list unless they are "referred" to a specialist facility.

[0018] The organ donation process is the process of providing organs from a donor to a recipient. The organ donation process can include both immediate donation and on-demand donation. Immediate donation involves preserving organs from a donor using standard preservation methods (simple refrigeration or mechanical perfusion) and providing them to the recipient as quickly as possible. On-demand donation involves preserving organs from a donor until a suitable recipient becomes available, then reviving (restoring, recovering) them to a transplantable state and providing them to the recipient as quickly as possible. Preservation methods used in on-demand donation include, for example, ice freezing, supercooling, and vitrification.

[0019] Ice-temperature freezing is a preservation method that cools organs to the lowest temperature possible without causing damage from freezing. Supercooling is a preservation method that supercools organs (cooling them below their freezing point without solidifying them). Ice-temperature freezing and supercooling are effective in suppressing organ damage. In other words, ice-temperature freezing and supercooling can prevent the physical destruction of cells and tissues caused by ice crystals that occur during normal freezing.

[0020] Vitrification is a preservation method that involves perfusing tissue with a high concentration of cryoprotective agents (CPAs) instead of water, followed by cooling, thereby transitioning the tissue to a solid state without the formation of ice crystals. In this preservation method, after vitrification is achieved, the organ is cooled to extremely low temperatures, such as liquid nitrogen temperature, to ensure further physical stability. Therefore, vitrification is suitable for the long-term preservation of organs. On the other hand, applying vitrification as a preservation method requires advanced technologies such as measures to suppress organ damage due to ice crystals and cracks, measures to repair organ damage, and measures to suppress chemical damage due to CPA toxicity. In this regard, molecular nanotechnology (MNT), in which molecular-sized autonomous robots (nanomachines) explore the body's vascular network and cells to detect and repair ischemic damage, chemical damage due to CPA toxicity, physical damage due to ice crystals and cracks, and the patient's underlying disease at the molecular or atomic level, may be able to revive (restore, recover) vitrified organs to a transplantable state.

[0021] EHR stands for "Electronic Health Record," a system that digitizes patients' medical information and shares it among multiple healthcare institutions. EHR information is managed by national systems (e.g., the Information-technology Promotion Agency, Ministry of Health, Labour and Welfare in Japan) and the systems of collaborating organizations. These organizations may include national and international organizations. EHRs centrally manage each patient's medical information throughout their life using a patient ID. The patient ID may include a nationwide common identifier (medical ID) used to link insurance medical information on an individual basis in the medical, nursing care, and health fields.

[0022] The recipient ID is an identifier used by the life-saving system to identify the recipient. A potential donor ID is an identifier used by life-saving systems to identify a potential donor. The recipient ID and potential donor ID are linked to the patient ID in the EHR, respectively.

[0023] The linking process between recipient IDs and potential donor IDs and patient IDs in EHRs managed by external EHR management organizations is carried out under strict security protocols. During the linking process, the patient ID itself is not stored within the life-saving system 1 network; instead, a one-way, irreversible hash value (or cryptographically pseudo-anonymized identifier) ​​generated by the EHR management organization is used as an intermediary key. This intermediary key is, for example, linked to the life-saving system ID using strong AES-256 encryption and stored in an isolated compartment within the memory units of the emergency call center device 100 and the organ donation management device 600. Highly sensitive information, such as biometric data and risk indicators, is processed while linked to this pseudo-anonymized life-saving system ID, and personally identifiable information is disclosed only to authorized operators and medical teams to the minimum extent necessary for life-saving measures. This ensures both highly efficient system operation and robust protection of potential donor personal information.

[0024] The organ preservation specialist team is a team of medical professionals within a life-saving system that provides organ transplantation to quickly and efficiently retrieve, preserve, and transport donor organs.

[0025] Hypothetical information is not definitive, real information at the present time, but rather information that estimates future possibilities.

[0026] A call means conversing using a means of communication (e.g., a telephone). The process for making a call may include the exchange of information other than the call itself between the emergency call device that is the source of the emergency call and the emergency call center device that receives the emergency call. The source of the call may be read as the sender or originator. The exchange of information other than the call itself may include, for example, the exchange of GPS information detected by GPS (Global Positioning System). GPS information is information indicating the coordinate position on Earth, specifically longitude and latitude. GPS information may also include information such as altitude and speed of movement.

[0027] Automatic emergency calls are emergency calls made without human intervention when an emergency or anomaly is detected through automated mechanisms such as sensors or systems. Manual emergency calls are emergency calls made proactively by a human who recognizes an emergency or anomaly and uses a telephone or dedicated application.

[0028] Automated conversation is a type of conversation in which a system or AI autonomously generates a conversation using natural language processing technology and provides contextually appropriate responses, without requiring user input or operation. Automated conversation includes conversations between systems or AI (Artificial Intelligence) and other systems or AIs, as well as conversations between systems or AIs and humans. An automated conversation function is a function that enables automated conversation.

[0029] Direct conversation is a conversation that takes place between people. A direct conversation function is a function that enables direct conversation.

[0030] Emergency call information is essential for responding quickly and accurately to emergencies. This information may include the location of the emergency call device used, the device's unique information, the caller's location, the content of the call, and the caller's contact information. The content of the call may include the location of the person being reported to and information about their condition. Types of emergencies include incidents, accidents, fires, and medical emergencies. Those being reported to emergency services may include injured or ill individuals. Those being reported to emergency services may also be elderly or disabled individuals who require protection during disasters.

[0031] Emergency response request information is provided when necessary responses are requested in an emergency, and it is information that enables a swift and appropriate response. It also serves as information that allows the recipient to decide whether or not to comply with the request. Emergency response request information may include the current location of the person requiring emergency response, information about their condition, and information about the necessary emergency response. Necessary emergency responses may include life-saving and rescue. Depending on the necessary emergency response, the person requiring emergency response may be referred to as a person needing life-saving, a person needing rescue, or a patient.

[0032] 2. First Embodiment 2-1. Composition (Life-saving system 1) As shown in Figure 1, the life-saving system 1 includes an emergency call center device 100, an emergency call device 200, an emergency responder device 300, an organ donation management device 600, and an organ preservation device 700.

[0033] (Emergency call center device 100) The emergency call center device 100 is installed in the emergency call center (also called the emergency communication command center or disaster emergency information center) 1a. The emergency call center device 100 has a communication unit 110.

[0034] (Telephone section 110) The call unit 110 includes an automatic conversation unit 111 having an automatic conversation function and a direct conversation unit 112 having a direct conversation function.

[0035] (Automated conversation unit 111) The automated conversation unit 111 has a function (automatic emergency call determination function) that determines whether an emergency call is an automated emergency call (hereinafter also referred to as "automatic call") or a manual emergency call (hereinafter also referred to as "manual call").

[0036] Emergency calls include 110 (to the police), 119 (to the fire and ambulance services), and 118 (to the Japan Coast Guard). Emergency calls may also include #7119 (emergency telephone consultation), #8000 (pediatric emergency telephone consultation), and #9910 (road emergency hotline). Furthermore, emergency calls may also include emergency contact with power companies (electrical equipment failure, broken power lines, etc.) and gas companies (gas leaks, etc.).

[0037] The method by which the automated conversation unit 111 determines whether an emergency call is an automated or manual call is arbitrary.

[0038] The automated conversation unit 111 can determine whether an emergency call is automated or manual based on the content of the conversation with the caller of the emergency call. More specifically, the automated conversation unit 111 can send a question voice to the caller of the emergency call asking whether it is automated or manual (for example, "Is this an automated call?", "Are you an AI agent?", etc.), and determine whether an emergency call is automated or manual based on the response to the question (for example, "Yes," "No," "It is an automated call," "It is not an automated call," "I am an AI agent," "I am not an AI agent," "I am a human," etc.).

[0039] The automated conversation unit 111 may determine that an emergency call is an automated call if, for example, the emergency call contains information in a predefined format. Information in a predefined format is, for example, information in the format of { "event": "fire", "location": { "lat": 35.6895, "lon": 139.6917}, "time": "2025-03-24T21:45:00Z"}.

[0040] The automated conversation unit 111 can determine, for example, that an emergency call is an automated call if it includes trigger information from a sensor or program. Trigger information may include, for example, "SensorID:67890 has detected smoke."

[0041] The automated conversation unit 111 can determine, for example, that an emergency call is a manual call if it includes a call log indicating that the emergency call is a manual call. A call log indicating that it is a manual call is, for example, log information such as "UserID:12345 pressed the call button".

[0042] The automated conversation unit 111 can determine whether an emergency call is automated or manual, for example, based on the caller's emotions inferred from the words included in the emergency call. More specifically, the automated conversation unit 111 can determine that an emergency call is manual if it includes words that express the caller's feelings, such as "scared" or "anxious."

[0043] The automated conversation unit 111 can determine whether an emergency call is automated or manual, for example, based on the tone and tempo of the words included in the emergency call. More specifically, the automated conversation unit 111 may determine that an emergency call is manual if it is presumed that the caller is in a state of tension or fear. Examples of signs that indicate tension or fear include a trembling voice, rapid speech, and an increase in emphasized expressions.

[0044] The automated conversation unit 111 may determine that an emergency call is a manual call if, for example, the words included in the emergency call are subjectively interpreted descriptions of the situation. More specifically, the automated conversation unit 111 may determine that an emergency call is a manual call if the words included in the emergency call are a mixture of objective facts and the speaker's personal feelings or emotions. Examples of a mixture of objective facts and the speaker's personal feelings or emotions include, "There was a really loud noise, like I've never heard anything like it before," or "There's a fire burning, and it's really scary."

[0045] The automatic conversation unit 111 has a function to communicate with the emergency call device 200 that made the automatic emergency call in high-speed conversation mode when the emergency call is an automatic emergency call. "High-speed conversation mode" may be read as "conversation time reduction mode".

[0046] The automated conversation unit 111 has a function (emergency call information notification function) that notifies a designated emergency responder device 300 of emergency call information based on a conversation in high-speed conversation mode with the emergency call device 200.

[0047] The automated conversation unit 111 has a function (emergency call information notification function) that notifies a designated emergency responder device 300 of emergency call information based on a normal conversation with the emergency call device 200.

[0048] The method by which the automated conversation unit 111 notifies the emergency responder device 300 of emergency call information is arbitrary. The automated conversation unit 111 can notify the emergency responder device 300, which is the recipient of the emergency call information, in a manner appropriate to the device. Such appropriate methods may include notification by automated conversation, notification by text, notification by image, etc.

[0049] The automated conversation unit 111 has a function (person-to-person conversation function) that allows it to communicate with the caller (person) who made the manual emergency call in normal conversation mode when the emergency call is a manual call.

[0050] The automated conversation unit 111 has a function (response instruction function) that, based on a normal conversation with the caller (person), instructs the caller (person) on how to deal with the emergency using machine voice (natural language voice). The instructions for dealing with the emergency may include instructions for initial firefighting, first aid, evacuation, etc.

[0051] The automated conversation unit 111 has a function (telephone exchange function, conversation method change function) that, when an emergency call is a manual call, transfers the call with the caller (person) to the direct conversation unit 112.

[0052] The automated conversation unit 111 has a service-side AI agent (hereinafter also referred to as the "operator agent function" or simply the "operator agent") 101.

[0053] (Direct conversation section 112) The direct conversation unit 112 is a functional unit that allows a human operator to have a direct telephone conversation with the caller (person) who made the emergency call when the emergency call is a manual call. Direct conversation may include the intervention of an interpreter between the two people talking on the telephone. Interpretation may include human interpretation and computer interpretation (machine interpretation).

[0054] The direct conversation unit 112 relays voice communication between the emergency call device 200 and the operator terminal 300D in order to enable direct telephone conversation between the caller (person) and the human operator. The operator terminal 300D is a terminal used by the human operator (person) of the emergency call center 1a.

[0055] (Fast conversation mode) High-speed conversation modes may include rapid-fire mode, abbreviation mode, machine language mode, etc.

[0056] Fast-talk mode is a mode that allows natural language voice calls to be completed in a shorter time than usual. Fast-talk mode can be achieved, for example, by performing processes such as removing silent parts of the conversation, blocking parts of the conversation, or thinning out parts of the conversation before converting the content of the conversation to be transmitted into audio data. Fast-talk mode can also be called high-speed playback mode.

[0057] Abbreviation mode is a mode in which communication is conducted using abbreviations, shortened words, compound words, etc. Abbreviations, shortened words, compound words, etc., may be expressions that are incomprehensible to humans, as long as a conversation can be established between the emergency call device 200 and the automatic conversation unit 111.

[0058] The machine language mode is a mode in which a conversation is established exclusively between the emergency call device 200 and the automated conversation unit 111. Examples of machine language modes include the giver link mode and the mathematical data sharing mode.

[0059] GiverLink mode is a high-speed conversation mode that uses a voice-based protocol for efficient communication between AI agents. In GiverLink mode, AI agents use a proprietary language that they can understand. This language can be binary code, a data-compressed format, or a special algorithm based on voice signals. GiverLink mode improves communication speed and accuracy, and also ensures noise immunity.

[0060] The mathematical data sharing mode is a high-speed conversation mode in which data exchange and sharing are based on mathematical methods and formats. Through conversations using the mathematical data sharing mode, AI agents can efficiently perform tasks by exchanging, for example, the weights of language models in matrix or vector format. The mathematical data sharing mode enables the transmission of structured information that is useful for coordination between machine learning models in the life-saving system 1 as a multi-AI agent system.

[0061] The mathematical data sharing mode is a high-speed conversation mode that significantly reduces call time by structuring and exchanging the weights and intermediate representations of the language model used for inference in matrix or vector form between the caller agent 201 and the operator agent 101, without using redundant natural language. Specifically, the caller agent 201 performs local inference regarding emergency response (e.g., determining the need for cardiopulmonary resuscitation) based on information integrating the situation at the scene, the patient's vital signs (biometric data stream), and the environmental context. It then encodes the inference result as the difference ΔW of the weight parameters or a feature vector V (e.g., a 1024-dimensional vector showing features extracted from the noise level at the scene and the speaker's emotional tone), and transmits this to the emergency call center device 100 in binary format or a data-compressed high-speed protocol. The operator agent 101 applies the weights WC of the central collaborative model to the received matrix / vector data to make an integrated decision. This data sharing enables critical information exchange with millisecond-order low latency, even under high load conditions such as congested communication bandwidth or large-scale disasters.

[0062] This mathematical data sharing mode, compared to conventional natural language voice communication, reduces communication delay to the millisecond order by exchanging the amount of information necessary for transmitting biometric data and environmental context in emergencies in a matrix-vector format that eliminates redundancy. This low-latency data exchange is an essential technology for critically accelerating the detection of signs of serious conditions in the health condition monitoring unit 60 and minimizing ischemic time from organ retrieval to restoration of blood flow.

[0063] (Emergency call device 200) Examples of emergency call devices 200 include automobiles 200A, smartphones 200B, smartwatches 200C, smart glasses 200D, etc. Emergency call devices 200 also include monitored person terminals (referred to as child GPS, GPS Talk, etc.) carried by the person being monitored (e.g., a child). An automobile (in other words, an in-vehicle computer) 200A is an example of a car device. Smartphones 200B and monitored person terminals are examples of mobile devices. Smartwatches 200C and smart glasses 200D are examples of wearable devices. Emergency call devices 200 may also include autonomous mobile robots (not shown). Autonomous mobile robots may include humanoid robots, animal-type robots, etc.

[0064] (Informant Agent 201) Each emergency call device 200 has a caller agent 201. The caller agent 201 is an AI agent (software agent) that operates within each emergency call device 200. The caller agent 201 is composed of multiple sub-agents and can handle complex tasks and dynamic environments. The caller agent 201 can automatically perform predetermined processes. These predetermined processes include automatic emergency call processing, which involves making an automatic call, and high-speed conversation processing, which involves making a call in high-speed conversation mode.

[0065] (Operator Agent 101) Operator agent 101 is an AI agent (software agent) that operates in the call unit 110. Operator agent 101 is composed of multiple sub-agents and can handle complex tasks and dynamic environments. Operator agent 101 can automatically perform predetermined processes related to emergency response requests. These predetermined processes include emergency call reception processing to receive emergency calls from the emergency call device 200, automatic call determination processing to determine whether the emergency call is an automatic call, call processing in high-speed conversation mode, call processing in normal conversation mode, emergency call information notification processing to notify a predetermined emergency responder device 300 of the emergency call information, response instruction processing to instruct the caller (person) on how to deal with the emergency, and conversation method change processing to change the conversation method.

[0066] The call processing in high-speed conversation mode includes pre-processing to initiate a call in high-speed conversation mode. This pre-processing includes sending a high-speed conversation request and receiving a high-speed conversation acceptance notification. Sending a high-speed conversation request is the process of sending a request for a call in high-speed conversation mode (hereinafter also referred to as "high-speed conversation request") to the emergency call device 200. Receiving a high-speed conversation acceptance notification is the process of receiving an acceptance notification from the emergency call device 200 indicating that the high-speed conversation request has been accepted.

[0067] The response instruction process is the process of instructing the caller (person) who made a manual emergency call to take action in the emergency situation, based on a phone call with the caller. These instructions may include, for example, instructions regarding cardiopulmonary resuscitation, first aid, firefighting, evacuation, accident response, etc.

[0068] (Emergency responder device 300) Examples of emergency response devices 300 include ambulances 300A, fire engines 300B, patrol cars 300C, operator terminals 300D, and terminals installed in police stations and police boxes. Ambulances (in other words, the on-board terminals in ambulances) 300A are an example of emergency vehicle devices. Fire engines (in other words, the on-board terminals in fire engines) 300B are an example of fire vehicle devices. Patrol cars (in other words, the on-board terminals in patrol cars) 300C are an example of police vehicle devices. Operator terminals 300D and terminals installed in police stations and police boxes are examples of emergency command system devices. Emergency response devices 300 may include autonomous mobile robots (not shown). Autonomous mobile robots may include humanoid robots, animal-type robots, etc. Emergency response devices 300 are also devices for organ preservation teams.

[0069] (Organ donation management device 600) As shown in Figure 2, the organ donation management device 600 includes a recipient information management unit 10, a potential recipient prediction unit 20, a potential recipient information management unit 30, a potential donor information management unit 40, a transplant suitability detection unit 50, a health status monitoring unit 60, and a donor selection unit 70. The recipient information management unit 10 has a recipient information management agent 11. The potential recipient prediction unit 20 has a potential recipient prediction agent 21. The potential recipient information management unit 30 has a potential recipient information management agent 31. The potential donor information management unit 40 has a potential donor information management agent 41. The transplant condition match detection unit 50 has a match detection agent 51. The health status monitoring unit 60 has a health status monitoring agent 61. The donor selection unit 70 has a donor selection agent 71.

[0070] The recipient information management unit 10, the potential recipient prediction unit 20, the potential recipient information management unit 30, the potential donor information management unit 40, the transplant suitability detection unit 50, the health status monitoring unit 60, and the donor selection unit 70 are realized, for example, through the cooperation of the hardware and software of the organ donation management device 600 (see Figure 5). The recipient information management unit 10, the potential recipient prediction unit 20, the potential recipient information management unit 30, the potential donor information management unit 40, the transplant suitability detection unit 50, the health status monitoring unit 60, and the donor selection unit 70 can access the EHR database of the EHR management organization and the databases of other information management organizations.

[0071] (Recipient Information Management Department 10) The Recipient Information Management Unit 10 is a functional unit that manages recipient information. The information managed includes the recipient's personal information and the recipient's medical information. The recipient's personal information includes age, gender, contact information, recipient ID, etc. The recipient's medical information includes information necessary for organ compatibility assessment, such as the underlying disease, complications, co-existing diseases, medical condition, medical urgency, blood type, physique (height, weight), HLA type, and presence or absence of infectious diseases.

[0072] (Recipient Information Management Agent 11) The recipient information management agent 11 is a component of the AI ​​agent that functions as part of the recipient information management unit 10. The recipient information management agent 11 provides the recipient detection agent 51 with information about the recipients it manages.

[0073] (Potential recipient prediction unit 20) The potential recipient prediction unit 20 is a functional unit that predicts potential recipients. The potential recipient prediction unit 20 has the function of predicting a group of individuals who are not currently registered on the organ transplant waiting list but who may be diagnosed, experience disease progression, or meet the eligibility criteria and be added to the waiting list within a certain period in the future. The function of predicting potential recipients can also be described as an organ demand prediction function. Therefore, the potential recipient prediction unit may be read as an organ demand prediction unit.

[0074] The potential recipient prediction unit 20 predicts potential recipients by combining multiple specific data with established statistical and computational methods such as hazard models, Markov models, and personalized machine learning.

[0075] Here, we will briefly explain examples of the data set input to the potential recipient prediction unit 20, the processing method (modeling approach) of the potential recipient prediction unit, and methods for ensuring prediction accuracy.

[0076] (Data set input to the potential recipient prediction unit 20) The potential recipient prediction unit 20 can use the following data as input data. Epidemiological data: Incidence and prevalence of diseases (e.g., CKD progression rate, end-stage heart failure prevalence, cirrhosis incidence), as well as disease trends by age, sex, and region. Medical utilization data: Outpatient and inpatient visits, referral rates to specialized departments such as nephrology and cardiac surgery, number of scheduled surgeries, and frequency of examinations (e.g., eGFR trends, liver function indicators). Diagnostic and severity indicators: Clinical scores (e.g., NYHA, MELD, eGFR stage), presence or absence of complications, and medication history. Medical access and system data: Transplant eligibility criteria, application of waiting list registration criteria (selection protocols), number of specialized facilities per region, OPO (organ donor organization) system, insurance coverage status, and referral pathways to the waiting list. Time-series registry data: historical trends in the waiting list, distribution of registered ages, average waiting time, etc. (e.g., national transplant registries). Sociodemographic data: demographic trends (progress of aging), socioeconomic conditions, and lifestyle factors (e.g., obesity rates, diabetes rates). Public health interventions and exogenous shocks: Data to reflect the impact of new treatments, implementation of screening policies, natural disasters, infectious disease outbreaks, and economic factors.

[0077] (Processing method in the potential recipient prediction unit 20) The potential recipient prediction unit 20 makes predictions based on the above data set using one or more of the following specific and feasible modeling approaches.

[0078] Cohort transformation + multistate survival analysis: The study uses local residents and patient populations as the starting point for the patient cohort and estimates the probability of transitions from "healthy → chronic disease → severe illness → waiting list registration" (multiple states) using a hazard model. In this process, age, sex, underlying diseases, and treatment history are incorporated as covariates, and the cumulative number of registrations for each period is estimated.

[0079] Machine learning-based prediction of disease onset and progression risk (personalized): This method utilizes time-series features from EHRs to predict rapid disease progression and transplant suitability in the short to medium term. For example, it can capture a sharp decline in eGFR in CKD patients and estimate the risk of dialysis initiation or transplant waiting list registration within one year. To ensure reliability for policy and clinical use, the interpretability of the model is ensured by using feature importance analysis methods (such as SHARP).

[0080] The potential recipient prediction unit 20 aims to individually predict the probability of an individual being placed on the organ transplant waiting list within a certain future period (e.g., within one year) based on the patient's EHR time-series data. This prediction model employs a hybrid ensemble AI model that combines a recurrent neural network (RNN) type, such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit), with a gradient boosting decision tree (XGBoost or LightGBM) in order to integrally process dynamic data (time-series data) and static data (baseline information) obtained from the EHR.

[0081] The features input to the prediction model shall include the following dynamic (time-series) and static data. Dynamic features are extracted from EHR records over the past year, with particular emphasis on the rate of change over the most recent three months. Specifically, in the renal domain (CKD), the absolute value of eGFR (estimated glomerular filtration rate) and the average rate of change over the most recent three months, the fluctuation pattern of serum creatinine levels, and the frequency and positive rate of urine protein and occult blood tests are used. In the cardiac domain (heart failure), the history of deterioration of the NYHA (New York Heart Association) score, the trends in BNP (brain natriuretic peptide) and NT-proBNP levels, and the rate of recent rapid increases are used. In the hepatic domain (cirrhosis), the trends in MELD / MELD-Na scores (end-stage liver disease model), bilirubin levels, and albumin levels are used. In addition, information on medical interventions, such as the history of initiation and discontinuation of specific critical disease medications, and changes in the frequency of hospitalization / outpatient visits, are also incorporated as features. Static features used include age, sex, presence or absence of underlying diseases (such as diabetes, hypertension, and dyslipidemia), HLA type information (if available), and geographical access information (distance to the transplant facility).

[0082] The latent recipient prediction unit 20 uses a hybrid ensemble AI model. This model consists of a first layer (LSTM / GRU layer) and a second layer (XGBoost layer). The first layer takes the dynamic (time-series) features mentioned above as input and generates a "latent vector representation of disease progression" from the time-series pattern. This layer plays a role in capturing the accelerating changes in disease progression. The second layer combines the latent vector representation obtained from the first layer with static features as input. This integrates the time-series progression pattern with the individual's fixed medical and demographic risk factors to make the final prediction.

[0083] In this model, the target variable is defined as "positive" (at risk) for individuals who have been newly registered on an organ-specific waiting list within one year of the prediction reference date. Regarding the loss function, considering the data imbalance where the number of registered individuals on the waiting list is significantly smaller than the number of unregistered individuals, the model uses Focal Loss instead of the usual binary cross-entropy to improve the prediction accuracy of the minority positive sample (patients who are actually registered). For validation, considering the time-series nature of the data, time-series cross-validation (a method that trains on historical data and validates on more recent data) is employed to ensure the model's practical future prediction performance. Furthermore, to ensure the clinical reliability of the prediction results, the importance of features is analyzed using methods such as SHAP (SHapley Additive exPlanations), ensuring interpretability so that physicians and administrators can understand which clinical indicators the model based its predictions on.

[0084] The output of this model includes the following predictive information about individual potential recipient candidates (patients who are not currently registered): The model outputs the risk probability of being placed on the waiting list within one year (from 0 to 100%) as its primary predictive metric. Furthermore, it identifies the top three dynamic / static features that had the greatest impact on the prediction probability (e.g., a sharp drop in eGFR change rate, deterioration of NYHA score) and outputs them as a list ranked by their primary contributing features.

[0085] The model is trained using large-scale linked registry data and anonymized historical EHR data. The training dataset is constructed as time-series data including static features (age, sex, blood type, HLA type, presence or absence of underlying diseases), dynamic features consisting of absolute values ​​of test results over the past three years and the mean rate of change and standard deviation of fluctuation over the most recent year, as well as event history (referral to a specialist, initiation / discontinuation of drug administration). The training data uses the fact that a patient was newly registered on the organ transplant waiting list within one year from the training target as the ground truth label. Training is performed using a two-stage transfer learning approach in which the LSTM / GRU unit learns time-series dependencies and extracts patterns of progression of disease severity, and then the XGBoost unit combines the output of the LSTM and static features to predict the final registration risk probability. Model performance is evaluated based on maintaining an AUC (Area Under Curve) of 0.85 or higher, and accuracy is ensured through periodic retraining.

[0086] The potential recipient prediction unit 20 employs microsimulation (individual-based) as one of its prediction methods. Microsimulation (individual-based) simulates each event, such as disease progression, diagnosis, referral, and registration, on an individual basis based on probability rules. This makes it possible to compare scenarios to see how policy changes, such as enhanced screening or improved referral flows, affect the increase or decrease in the potential list.

[0087] Furthermore, the potential recipient prediction unit 20 employs a combined synthetic cohort method and a Markov model (organ-specific) as another method for modeling the organ donation system. This method estimates the future pool of eligible recipients by using a Markov chain that reflects the eligibility criteria and transplant age for each organ. The system addresses the significant differences in transplant eligibility criteria across organs (e.g., dialysis-dependent pathways for kidneys, and being placed on a waiting list due to acute exacerbations in liver and heart) by modeling each organ separately.

[0088] (Method for ensuring prediction accuracy in the potential recipient prediction unit 20) As a method for ensuring prediction accuracy in the potential recipient prediction unit 20, firstly, registration bias correction is performed. Since registration to the waiting list is influenced by the referral rate and selection criteria, the observed number of registrations is only the lower limit of eligible individuals. Therefore, the distribution of the referral rate and selection criteria is incorporated into the model to correct for unobserved bias. Next, the prediction results are not simply point estimates, but are calculated and presented as prediction intervals (confidence intervals) using bootstrap and Bayesian posterior distributions to evaluate uncertainty. Furthermore, a mechanism for continuous monitoring and retraining is established to periodically evaluate prediction performance even after model implementation and to perform retraining to maintain and improve performance. In addition, to consider regional characteristics where there are large differences in the number of hospitals and OPO systems, a multi-layer model is used to estimate regional differences and a regional hierarchical structure is considered to ensure prediction accuracy for each region.

[0089] The integration of a large-scale linked registry with a real-time EHR pipeline enables the detection of early disease progression signals and more accurately predicts the risk of short-term waiting registration. Furthermore, molecular and biomarker integration, incorporating individual genetic risks and biomarkers (such as inflammation indicators), improves the accuracy of individualized disease progression risk predictions. In addition, automated screening functions, integrated with an operational flow that automatically detects patients exceeding risk thresholds within the EHR and encourages early referral, further enhance prediction accuracy.

[0090] (Potential recipient prediction agent 21) The potential recipient prediction agent 21 is a component of the AI ​​agent that functions as part of the potential recipient prediction unit 20. The potential recipient prediction agent 21 provides information on the predicted potential recipients to the predicted potential recipient information management agent 31.

[0091] (Potential Recipient Information Management Unit 30) The Potential Recipient Information Management Unit 30 is a functional unit that manages information on potential recipients. The information managed includes the personal information and medical information of potential recipients. The personal information of potential recipients includes age, gender, contact information, and potential recipient ID. The medical information of potential recipients includes information necessary for organ compatibility assessment, such as underlying disease, complications, co-existing diseases, medical condition, medical urgency, blood type, physique (height, weight), HLA type, and presence or absence of infectious diseases. The information managed by the Potential Recipient Information Management Unit 30 may be hypothetical information, as it is based on predictions. The function of managing information on potential recipients can also be said to be a function of managing information on organs for which demand is predicted (organs that will be needed in the future). Therefore, the Potential Recipient Information Management Unit may be reinterpreted as the Demand-Predicted Organ Information Management Unit.

[0092] (Potential recipient information management agent 31) The potential recipient information management agent 31 is a component of the AI ​​agent that functions as part of the potential recipient information management unit 30. The potential recipient information management agent 31 receives and manages the prediction information (demand) generated by the potential recipient prediction agent 21. The potential recipient information management agent 31 provides the information of the potential recipients it manages to the suitability detection agent 51.

[0093] (Potential Donor Information Management Department 40) The Potential Donor Information Management Unit 40 is a functional unit that manages information on potential donors who may become donors in the future. The information managed includes the personal and medical information of potential donors. The personal information of potential donors includes age, gender, contact information, and potential donor ID. The medical information of potential donors includes information necessary for organ compatibility assessment, such as blood type, physical characteristics (height, weight), HLA type, medical history, pre-existing conditions, presence of infectious diseases, and lymphocyte crossmatch results. The information of potential donors is updated regularly based on the individual's consent, including through data linkage with a personal AI agent (caller agent 201) via the emergency call device 200.

[0094] The collection of biometric data (heart rate, brain waves, etc.) by the potential donor information management unit 40 is contingent on electronic consent (e-Consent) based on the individual's clear expression of will. This consent is obtained through the interface of the emergency call device 200 (caller agent 201) held by the potential donor, verified by, for example, an electronic signature or equivalent authentication means, and recorded in a distributed ledger technology (DLT)-based consent management module in the storage unit 650 along with an immutable timestamp. The potential donor can confirm the scope of their consent and immediately withdraw it at any time by operating the emergency call device 200. If the consent is withdrawn, the potential donor information management unit 40 immediately stops collecting the relevant biometric data and anonymizes the existing data.

[0095] (Potential donor information management agent 41) The potential donor information management agent 41 is a component of the AI ​​agent that functions as part of the potential donor information management unit 40. Based on the individual's consent, the potential donor information management agent 41 periodically updates the information it manages through data linkage via the informant agent 201. The potential donor information management agent 41 provides the information of the potential donors it manages to the transplant suitability detection unit 50 (suitability detection agent 51).

[0096] (Transplantation condition suitability detection unit 50) The transplant compatibility detection unit 50 is a functional unit that detects potential donors who meet the transplant conditions for each recipient or potential recipient in advance, based on the recipient information managed by the recipient information management unit 10, the potential recipient information managed by the potential recipient information management unit 30, and the potential donor information managed by the potential donor information management unit 40. This detection is performed continuously by algorithmically evaluating factors such as blood type match, HLA type compatibility, and similarity in body size.

[0097] The transplant suitability detection unit 50 continuously calculates a Comprehensive Suitability Score (CSS) for all detected potential donor-recipient pairs to maximize the success rate of transplantation and long-term prognosis.

[0098] The CSS is determined by multiplying the scores of three key endpoints—the medical criteria score (S_Med), the organ / timing optimization score (S_Org), and the recipient prognosis score (S_Prog)—by weighting them according to their respective importance, and then summing them up.

[0099] The organ / timing optimization score (S_Org) evaluates the optimal timing of organ donation based on real-time dynamic data from the health monitoring unit 60, including the risk of sudden changes in the potential donor's health (low probability of transition) and the possibility of preserving the extracted organ (short ischemic time).

[0100] The recipient prognosis score (S_Prog) uses the AI ​​model of the potential recipient prediction unit 20 to predict and evaluate the recipient's long-term graft survival rate after transplantation, as well as the post-transplant prognosis considering the urgency and duration of the waiting period, if this organ is transplanted.

[0101] The Medical Reference Score (S_Med) assesses static, basic medical fit, such as blood type, HLA type, and body size similarity.

[0102] The donor selection unit 70 identifies the recipient with the highest CSS score as the best match when an opportunity for organ donation arises, such as when a potential donor reaches a critical health condition. This identification triggers the initiation of a rapid organ donation process.

[0103] (Suitable person detection agent 51) The match detection agent 51 is a component of the AI ​​agent that functions as part of the transplant condition match detection unit 50. The match detection agent 51 receives recipient information from the recipient information management agent 11. The match detection agent 51 receives potential recipient information from the potential recipient information management agent 31. The match detection agent 51 receives potential donor information from the potential donor information management agent 41. Based on the information received, the match detection agent 51 pre-detects potential donors that meet the transplant conditions for each recipient or potential recipient, and provides the detection results to the donor selection agent 71.

[0104] (Health Monitoring Department 60) The health monitoring unit 60 is a functional unit that continuously monitors the health status of each potential donor. The health monitoring unit 60 receives biometric data (heart rate, respiratory irregularities, blood pressure, etc.) transmitted from the caller agent 201 of the emergency call device 200 (car device, mobile device, wearable device, etc.) associated with the potential donor via the communication unit 160, and evaluates whether the potential donor is in a serious health condition.

[0105] If the emergency notification device 200 is a device (head-worn device) capable of measuring the brainwaves of a potential donor, the health monitoring unit 60 receives biometric data, including changes in the potential donor's brainwaves, via the communication unit 160. The health monitoring unit 60 then uses this biometric data, including changes in the potential donor's brainwaves, as vital signs to detect early signs of a serious condition that could lead to brain death in the potential donor.

[0106] (Health monitoring agent 61) The health status monitoring agent 61 is a component of the AI ​​agent that functions as part of the health status monitoring unit 60. The health status monitoring agent 61 works closely with the operator agent 101 and the caller agent 201. An advanced AI agent-to-agent communication protocol, including a high-speed conversation mode, is used between these agents to respond to particularly urgent situations. This protocol goes beyond mere data transfer, enabling the acquisition and analysis of information regarding the health status of potential donors in an extremely efficient and near real-time manner.

[0107] Specifically, the health monitoring agent 61 utilizes highly efficient information generated or collected while the operator agent 101 is communicating with the caller agent 201 in high-speed conversation mode.

[0108] In this embodiment, "highly efficient information" refers to structured real-time health risk data acquired and analyzed with low latency between the caller agent 201 mounted on the emergency call device 200 and the operator agent 101 of the emergency call center device 100 via a high-speed conversation mode (giver link mode, mathematical data sharing mode, etc.). The real-time health risk data is a collection of data obtained by AI that instantly structures voice analysis of the call content, transcribed conversation logs, and related biometric data, and converts them into potential health risk indicators.

[0109] The health monitoring agent 61 obtains structured information, which is acquired as features and situation context vectors extracted from the real-time biometric data stream, and evaluates it as an anomaly score (CSS_Org).

[0110] The features extracted from the real-time biometric data stream are derived from biosignals such as electroencephalograms (EEGs) and electrocardiograms (ECGs) of potential donors. Specifically, these include rapid fluctuations in low-frequency components indicating impaired brain function and aperiodicity in heart rate variability indicating a cardiovascular crisis.

[0111] The structured information obtained as a situation context vector includes the type of emergency self-diagnosed by the caller agent 201, and environmental sensor data (impact, acceleration, etc.).

[0112] The extracted features are input into an individualized risk model and converted into risk indicators such as the probability of difficulty in recovering brain function (P_BrainLoss) and the probability of irreversible organ damage (P_OrganDamage). The health status monitoring agent 61 integrates these risk indicators with the situational context to calculate an anomaly score (CSS_Org) indicating the life-threatening condition of potential donors in real time and transmits it to the donor selection unit 70.

[0113] The donor selection unit 70 determines that a "predetermined health condition" (donor selection trigger) has been reached when, in addition to the potential donor meeting the transplantation criteria, all of the following composite threshold conditions are met.

[0114] The composite threshold condition includes (1) to (3) below. (1) AI risk assessment: The probability of difficulty in recovering brain function (P_BrainLoss) continuously exceeds a predetermined threshold (e.g., 90%). (2) Confirmation of life-saving measures: The situation context is linked to whether life-saving measures are in progress at the scene or whether the predetermined time limit has already been exceeded. (3) Organ preservation conditions: The abnormal score (CSS_Org) is below the critical level that causes irreversible damage to organ function, and the organ is in a state where it is medically possible to donate the organ.

[0115] Objective judgments based on these combined conditions ensure the maximum continuation of life-saving efforts and a subsequent rapid and efficient transition to an organ donation process.

[0116] The donor selection unit 70 selects a potential donor as a donor only if all of the following composite threshold conditions are met: AI risk assessment that P_BrainLoss continuously exceeds a predetermined threshold, exceeding the limit time for on-site life-saving measures, and organ preservation conditions that CSS_Org is below the critical level for irreversible damage, based on the progress of life-saving measures and the results of biometric data analysis. This objective and comprehensive judgment criterion is at the core of this system, which balances bioethical considerations (maximum continuation of life-saving efforts) and medical efficiency (maximization of organ donation opportunities).

[0117] The "Probability of Impossible Brain Function Recovery (P_BrainLoss)" and "Probability of Irreversible Organ Damage (P_OrganDamage)" calculated by the health monitoring agent 61 are calculated in real time using the following algorithm. In calculating P_BrainLoss, a feature combining the power spectral density of alpha and theta waves extracted from electroencephalogram (EEG) data, and mean arterial pressure (MAP) and pupillary reflex data is input to a pre-trained recurrent neural network (RNN). The RNN is trained using data from past severe trauma patients and outputs a temporal probability distribution leading to irreversible brain function failure. P_BrainLoss is calculated as the complement of the "probability that brain function will recover within one hour if life-saving measures are continued at the scene." On the other hand, in the calculation of P_OrganDamage, the asymmetry of the periodicity of the QRS complex in the electrocardiogram (ECG), the rate of rapid decrease in blood pressure (BP), and ischemic time are used as the main input features. These features are input into a Markov decision process model that predicts the rate of progression of irreversible ischemic damage to a specific organ (e.g., kidney, liver), and P_OrganDamage is defined as the "probability that the organ's function at the time of retrieval reaches a critical level that makes it unsuitable for transplantation," as predicted by the model.

[0118] The health monitoring agent 61, by continuously monitoring and analyzing this information flow, has the ability to detect signs of a rapid deterioration in the health of a potential donor or a life-threatening situation much earlier than a human would notice. This early detection capability enables decisively accelerating the direction of rapid medical intervention and the next steps in the organ donation process (e.g., preparing to dispatch a specialized medical team, preparing to match a suitable donor and recipient), which is essential in maximizing the chances of saving a donor's life or preserving available organs. This entire collaborative and analytical system forms the foundation for achieving the best possible medical outcomes while ensuring that organ donation opportunities are not missed and while adhering to ethical constraints.

[0119] (Donor Selection Section 70) The donor selection unit 70 is a functional unit that selects potential donors who have been detected as suitable by the transplant suitability detection unit 50 and who have reached a predetermined health condition (e.g., a severe condition in which recovery of brain function is predicted to be extremely difficult) by the health condition monitoring unit 60. This selection serves as a trigger to initiate a rapid organ donation process and is used to request the emergency response device 300, etc., to prepare for appropriate organ preservation and transport.

[0120] (Donor Selection Agent 71)

[0121] The donor selection agent 71 is a component of the AI ​​agent that functions as part of the donor selection unit 70. The donor selection agent 71 receives information on suitability to transplant conditions from the match detection agent 51. The donor selection agent 71 receives evaluation results from the health status monitoring agent 61 to determine whether the potential donor has reached a predetermined health state. Based on the information received from the match detection agent 51 and the health status monitoring agent 61, the donor selection agent 71 makes a decision on donor selection. The donor selection agent 71 cooperates with the operator agent 101 (emergency call center device 100) to notify emergency response devices of emergency response request information containing information on the selected donor and matched recipient.

[0122] (Organ preservation device 700) The organ preservation device 700 (Figure 1) functions as an organ preservation unit 80 for preserving organs extracted from a donor in a way that allows them to be restored to a transplantable state. The organ preservation device 700 is located in an organ preservation facility separate from the emergency call center device 100.

[0123] The organ preservation device 700 is primarily used in the on-demand donation process (the process of preserving organs until a suitable recipient becomes available). In other words, the organ preservation device 700 is a device for preserving organs extracted from a donor in a state that can be revived (restored, recovered) to a transplantable state until a suitable recipient becomes available. Specifically, the organ preservation device 700 preserves organs in a state that can be revived (restored, recovered) to a transplantable state by methods such as ice freezing, supercooling, and vitrification.

[0124] (Configuration of the emergency call center device 100) As illustrated in Figure 3, the emergency call center device 100 is composed of a control unit 120, an information display unit 130, an input unit 140, a storage unit 150, a communication unit 160, and the like. Each functional part of the emergency call center device 100 is realized through the cooperation of hardware and software.

[0125] The control unit 120 includes a processor 121 and a memory 122. The processor 121 is responsible for the overall control of the emergency call center device 100. The processor 121 can be implemented by, for example, a CPU (Central Processing Unit), MCU (Micro Controller Unit), MPU (Micro Processor Unit), GPU (Graphics Processing Unit), NPU (Neural Processing Unit), or TPU (Tensor Processing Unit). The memory 122 includes, for example, ROM (Read Only Memory), RAM (Random Access Memory), and flash ROM. For example, flash ROM and ROM store various programs (including data used by the programs), and RAM is used as the work area of ​​the processor 121. Programs stored in memory 122 are loaded into the processor 121, causing the processor 121 to execute the coded processes. The programs stored in memory 122 include agent programs for implementing one or more operator agents 101. The agent programs include agent learning programs.

[0126] The information display unit 130 is a functional unit for presenting various information to operators of the emergency call center device 100 (hereinafter also referred to as "center administrators"). The information display unit 130 includes a display 131 and a speaker 132. The display 131 visually presents various information to the center administrators. The speaker 132 audibly presents various information to the center administrators. The display 131 displays the results of processing performed by the control unit 120, etc. The speaker 132 outputs the results of processing performed by the control unit 120, etc., as audio. The center administrators may include system administrators (people) responsible for monitoring and maintaining the emergency call center 1a system, data administrators (people) responsible for managing and analyzing call history and system logs, human operators responsible for responding to emergency calls, etc.

[0127] The input unit 140 is a functional unit for the center administrator to input information to the emergency call center device 100. The input unit 140 includes an operation input unit 141 that accepts operations from the center administrator, an image input unit (camera) 142 for inputting images, and an audio input unit (microphone) 143 for inputting voice. If a touch panel is used for the operation input unit 141, the operation input unit 141 can be integrated with the display 131. Furthermore, if the operation input unit 141 can accept voice operations, the operation input unit 141 and the audio input unit 143 can be integrated.

[0128] The storage unit 150 includes auxiliary storage devices such as an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various types of data. The storage unit 150 may also store programs executed by the processor 121 (including data used by the programs).

[0129] The communication unit 160 is a functional unit that transmits and receives data with the organ donation management device 600 and external devices via the communication network 400. The communication network 400 may include multiple types of networks of different scales, such as PAN (Personal Area Network), LAN (Local Area Network), MAN (Metropolitan Area Network), WAN (Wide Area Network), and the Internet. The communication network 400 may also include multiple types of networks using different communication methods, such as wired networks and wireless networks.

[0130] (Configuration of Emergency Call Device 200) As illustrated in Figure 4, the emergency call device 200 is composed of a control unit 220, an information display unit 230, an input unit 240, a storage unit 250, a communication unit 260, and the like. Each functional part of the emergency call device 200 is realized through the cooperation of hardware and software.

[0131] The control unit 220 includes a processor 221 and a memory 222. The processor 221 is responsible for the overall control of the emergency call device 200. The processor 221 is implemented by, for example, a CPU, MCU, MPU, GPU, NPU, TPU, etc. The memory 222 includes, for example, ROM, RAM, and flash ROM. For example, flash ROM and ROM store various programs (including data used by the programs), and RAM is used as the work area of ​​the processor 221. Programs stored in memory 222 are loaded into the processor 221, causing the processor 221 to execute the coded processes. The programs stored in memory 222 include an agent program for implementing the caller agent 201. The agent program includes an agent learning program.

[0132] The information display unit 230 is a functional unit for presenting various information to the user of the emergency call device 200. The information display unit 230 includes a display 231 and a speaker 232. The display 231 visually presents various information to the user of the emergency call device 200. The speaker 232 audibly presents various information to the user of the emergency call device 200. The display 231 displays the results of processing performed by the control unit 220, etc. The speaker 232 outputs the results of processing performed by the control unit 220, etc., as audio.

[0133] The input unit 240 is a functional unit for the user to input information to the emergency call device 200. The input unit 240 includes an operation input unit 241 that accepts user input, an image input unit (camera) 242 for inputting images, and an audio input unit (microphone) 243 for inputting voice. When a touch panel is used for the operation input unit 241, the operation input unit 241 is integrated with the display 231. Furthermore, if the operation input unit 241 can accept voice input, the operation input unit 241 and the audio input unit 243 can be integrated. Hereinafter, the operation input unit 241 integrated with the display 231 will be referred to as the touch panel 244.

[0134] The storage unit 250 includes, for example, an auxiliary storage device such as an HDD or SSD, and stores various types of data. The storage unit 250 may also store programs executed by the processor 221 (including data used by the programs).

[0135] The communication unit 260 is a functional unit that transmits and receives data with external devices via the communication network 400.

[0136] In addition to the functional units illustrated in Figure 3, the emergency call device 200 also has functional units to realize its intended function as a car device, mobile device, wearable device, etc. For example, if the emergency call device 200 is a car 200A, the emergency call device 200 has functional units to realize the function of a car device. If the emergency call device 200 is a smartphone 200B or a monitored person's terminal, the emergency call device 200 has functional units to realize the function of a mobile device. If the emergency call device 200 is a smartwatch 200C or smart glasses 200D, the emergency call device 200 has functional units to realize the function of a wearable device.

[0137] (Configuration of the organ donation management device 600) As illustrated in Figure 5, the organ donation management device 600 is composed of a control unit 620, an information display unit 630, an input unit 640, a storage unit 650, a communication unit 660, and the like. Each functional unit of the organ donation management device 600 is realized through the cooperation of hardware and software.

[0138] The control unit 620 includes a processor 621 and a memory 622. The processor 621 is responsible for the overall control of the organ donation management device 600. The processor 621 can be implemented by, for example, a CPU, MCU, MPU, GPU, NPU, TPU, etc. The memory 622 includes, for example, ROM, RAM, and flash ROM. For example, flash ROM and ROM store various programs (including data used by the programs), and RAM is used as the work area of ​​the processor 621. Programs stored in the memory 622 are loaded into the processor 621, causing the processor 621 to execute the coded processes.

[0139] The programs stored in memory 622 include a recipient information management program, a potential recipient prediction program, a potential recipient information management program, a potential donor information management program, a transplant suitability assessment program, a health status monitoring and biometric data analysis program, and a donor selection and process initiation trigger program.

[0140] The recipient information management program is a program for managing the recipient's personal information and medical information (blood type, HLA type, medical condition, urgency, etc.). In other words, the recipient information management program is a program that realizes the functions of the recipient information management unit 10.

[0141] The potential recipient prediction program is a program that uses statistical and computational methods such as hazard models, Markov models, and personalized machine learning to predict a group of individuals (potential recipients) who are likely to be added to the waiting list within a certain period in the future. In other words, the potential recipient prediction program is a program that implements the functions of the potential recipient prediction unit 20.

[0142] The potential recipient information management program is a program for managing predictive information (including hypothetical information) of potential recipients. In other words, the potential recipient information management program is a program for realizing the functions of the potential recipient information management unit 30.

[0143] The potential donor information management program manages the personal and medical information (blood type, HLA type, medical history, etc.) of potential donors and periodically updates this information through data linkage via the emergency call device 200 (caller agent 201), etc. In other words, the potential donor information management program is a program that realizes the functions of the potential donor information management unit 40.

[0144] The transplant compatibility evaluation program is a program that algorithmically and continuously evaluates and detects the compatibility (blood type match, HLA type match, similarity in physique, etc.) between recipients, potential recipients, and potential donors. In other words, the transplant compatibility evaluation program is a program that realizes the functions of the transplant compatibility detection unit 50.

[0145] The health status monitoring and biometric data analysis program receives and analyzes biometric data (heart rate, blood pressure, electroencephalogram, etc.) transmitted from the potential donor's emergency call device 200, and continuously monitors and evaluates whether or not the donor is in a serious health condition. In other words, the health status monitoring and biometric data analysis program is a program that enables the functions of the health status monitoring unit 60.

[0146] The donor selection and process initiation trigger program is a program that activates a trigger to select a potential donor who has been confirmed to be suitable and has reached a predetermined critical health condition, and to initiate a rapid organ donation process. In other words, the donor selection and process initiation trigger program is a program that realizes the functions of the donor selection unit 70.

[0147] Furthermore, the programs stored in memory 622 include a recipient information management agent program, a potential recipient prediction agent program, a potential recipient information management agent program, a potential donor information management agent program, a suitable candidate detection agent program, a health status monitoring agent program, a donor selection agent program, and the like.

[0148] The recipient information management agent program is a program that implements the functions of the recipient information management agent 11. The potential recipient prediction agent program is a program that implements the functions of the potential recipient prediction agent 21. The potential recipient information management agent program is a program that implements the functions of the potential recipient information management agent 31. The potential donor information management agent program is a program that implements the functions of the potential donor information management agent 41. The conformer detection agent program is a program that implements the functions of the conformer detection agent 51. The health status monitoring agent program is a program that enables the functions of the health status monitoring agent 61 (including high-speed data exchange with operator agent 101, etc.). The donor selection agent program is a program designed to implement the functions of donor selection agents 71.

[0149] Furthermore, in order for the organ donation management device 600 to function as part of the life-saving system 1, the memory 622 also stores AI agent communication protocol programs, system linkage and data access programs, and other similar programs.

[0150] The AI ​​agent-to-agent communication protocol program is designed to enable efficient data exchange between the operator agent 101, the caller agent 201, and other agents. If an emergency response agent (e.g., emergency response agent 301) is present, the AI ​​agent-to-agent communication protocol program includes a program to enable efficient data exchange between the operator agent 101, the caller agent 201, and the emergency response agent.

[0151] System integration and data access programs are programs that enable interfaces and processes for accessing databases of external EHR management organizations and other information management organizations.

[0152] The information display unit 630 is a functional unit for presenting various information to operators of the organ donation management device 600 (hereinafter also referred to as "organ donation managers"). The information display unit 630 includes a display 631 and a speaker 632. The display 631 visually presents various information to organ donation managers. The speaker 632 audibly presents various information to center administrators. The display 631 displays the results of processing performed by the control unit 620, etc. The speaker 632 outputs the results of processing performed by the control unit 620, etc., as sound. Organ donation managers may include system administrators (people) responsible for monitoring and maintaining the organ donation management device 600 system, data administrators (people) responsible for managing and analyzing various histories and system logs related to organ donation, and human operators responsible for various actions to realize organ donation.

[0153] The input unit 640 is a functional unit for organ donation managers to input information to the organ donation management device 600. The input unit 640 includes an operation input unit 641 that accepts operations from the organ donation manager, an image input unit (camera) 642 for inputting images, and an audio input unit (microphone) 643 for inputting voice. If a touch panel is used for the operation input unit 641, the operation input unit 641 can be integrated with the display 631. Furthermore, if the operation input unit 641 can accept voice operations, the operation input unit 641 and the audio input unit 643 can be integrated.

[0154] The storage unit 650 includes, for example, an auxiliary storage device such as an HDD or SSD, and stores various types of data. The storage unit 650 may also store programs executed by the processor 621 (including data used by the programs).

[0155] The communication unit 660 is a functional unit that transmits and receives data with the emergency call center device 100 and external devices via the communication network 400.

[0156] (Processes performed by the emergency call center device 100 and the emergency call device 200) Referring to Figures 6 to 12, an example of the processing performed by the emergency call center device 100 and the emergency call device 200 will be described.

[0157] (Processed by the emergency call center system) As shown in Figure 6, the emergency call center device 100 receives an emergency call (step S11) and performs emergency call response processing (step S12).

[0158] As shown in Figure 7, in the emergency call response process (step S12), the emergency call center device 100 determines whether the emergency call is an automatic call or not (step S13: automatic emergency call determination process). If the emergency call center device 100 determines that the emergency call is an automatic call (YES in step S13), it executes the automatic call response process (step S100, Figure 8). On the other hand, if it determines that the emergency call is a manual call (NO in step S13), the emergency call center device 100 executes the manual call response process (step S110, Figure 11).

[0159] As shown in Figure 8, in the automatic notification response process (step S100), the emergency notification center device 100 requests the emergency notification device 200 to communicate in high-speed conversation mode (step S101: high-speed conversation request process).

[0160] The emergency call center device 100 determines whether the emergency call device 200 has accepted the request based on the response from the emergency call device 200 (step S102).

[0161] If the emergency call center device 100 determines that the emergency call device 200 has accepted the request (YES in step S102), it performs high-speed conversation processing (step S120, Figure 9).

[0162] On the other hand, if the emergency call device 200 determines that it will not accept the request (NO in step S102), it performs normal conversation processing (step S130, Figure 10).

[0163] As shown in Figure 9, in high-speed conversation processing (step S120), the emergency call center device 100 communicates with the emergency call device 200 in high-speed conversation mode (step S121: conversation processing in high-speed conversation mode).

[0164] Then, based on the communication with the emergency call center device 200, the emergency call center device 100 notifies a designated emergency responder device 300 of the emergency call information (Step S122: Emergency call information notification process). As mentioned above, the method of notifying the emergency call information is arbitrary.

[0165] For example, if a fire truck or ambulance is needed, the emergency call center device 100 can, based on communication with the emergency call device 200, automatically notify a human operator at the emergency call center 1a via operator terminal 300D of emergency call information such as the location and situation at the scene and the condition of the injured or ill person.

[0166] Furthermore, the emergency call center device 100 can, for example, notify emergency response devices 300 (such as ambulances 300A, fire trucks 300B, patrol cars 300C, etc.) located closer to the current location of the emergency call device 200 using any method of choice.

[0167] As shown in Figure 10, in normal conversation processing (step S130), the emergency call center device 100 communicates with the emergency call device 200 in normal conversation mode (step S131: conversation processing in normal conversation mode).

[0168] Then, based on the communication with the emergency call center device 200, the emergency call center device 100 notifies a designated emergency responder device 300 of the emergency call information (Step S132: Emergency call information notification process).

[0169] As shown in Figure 11, in the manual call response process (step S110), the emergency call center device 100 communicates with the caller (person) who made the manual call using the emergency call device 200 in normal conversation mode (step S111: person-to-person conversation process).

[0170] Then, based on the content of the conversation with the caller (person), the emergency call center device 100 attempts to give the caller instructions on how to deal with the emergency using an automated voice (Step S112: Action instruction processing).

[0171] Next, the emergency call center device 100 determines, based on the response from the caller (person), whether the caller understood the automated voice instructions regarding how to deal with the emergency (Step S113: Understanding Determination Process).

[0172] The method by which the emergency call center device 100 determines whether the caller (person) understood the machine-voiced instructions regarding how to respond to the emergency is arbitrary. For example, the determination can be based on whether the caller (person) performed the expected actions in accordance with the machine-voiced instructions. Alternatively, the determination can be based on the caller's (person's) response to confirmation questions after the instructions. The caller's (person's) response can also be analyzed using natural language processing technology, and the determination can be based on the analysis results.

[0173] If the emergency call center device 100 determines that the caller (person) does not understand the automated voice instructions on how to deal with the emergency (NO in step S113), it changes the communication method with the caller to a direct conversation method (step 114) (conversation method change process).

[0174] More specifically, the emergency call center device 100 relays voice communication between the emergency call device 200 and the operator terminal 300D. This allows the caller who made the emergency call to communicate directly with the human operator at the emergency call center 1a.

[0175] (Processing by emergency call device 200) As shown in Figure 6, the emergency call device 200 makes an emergency call (step S21: emergency call processing) and performs emergency call device-side call processing (step S22, Figure 12).

[0176] As shown in Figure 12, in the emergency call device side call processing (step S22), the emergency call device 200 determines whether the emergency call it made is an automatic call or not (step S23).

[0177] If it is determined that the emergency call is an automatic call (YES in step S23), the emergency call device 200 determines whether or not it has received a high-speed conversation request from the emergency call center device 100 (step S24: high-speed conversation request reception determination process).

[0178] If it is determined that no request for high-speed conversation has been received (NO in step S24), the emergency call device 200 communicates with the emergency call center device 100 in normal conversation mode (step S27: normal conversation processing).

[0179] On the other hand, if it is determined that a request for high-speed conversation has been received (YES in step S24), the emergency call device 200 determines whether or not it is possible to make a call in high-speed conversation mode (step S25: high-speed conversation feasibility determination process).

[0180] If the emergency call device 200 determines that it is possible to make a call in high-speed conversation mode (YES in step S25), it will make a call with the emergency call center device 100 in high-speed conversation mode (step S26: high-speed conversation processing). When initiating a call in high-speed conversation mode, the emergency call device 200 sends an acceptance notification to the emergency call center device 100 and then starts the call in high-speed conversation mode.

[0181] On the other hand, if it is determined that it is not possible to make a call in high-speed conversation mode (NO in step S25), the emergency call device 200 will make a call with the emergency call center device 100 in normal conversation mode (step S27: normal conversation processing).

[0182] Furthermore, if it is determined that the emergency call is a manual call (NO in step S23), the emergency call device 200 changes the communication method with the emergency call center device 100 to a direct communication method (step S28: direct conversation processing). In this case, the caller (person) who made the emergency call using the emergency call device 200 and the human operator at the emergency call center 1a can communicate directly.

[0183] 2-3. Operation of the First Embodiment The first embodiment of the life-saving system 1 is based on multi-AI agent technology and exerts the following effects to improve the efficiency of life-saving medical care, including organ transplantation.

[0184] (Expediting responses to emergency calls) In the first embodiment of the life-saving system 1, the emergency call center device 100 can communicate with the emergency call device 200 that made the automatic emergency call (step S121 in Figure 9) in high-speed conversation mode when the emergency call is an automatic call (YES in step 13 in Figure 7). By communicating in high-speed conversation mode, it is possible to receive the emergency call in a shorter time and notify the designated emergency responder device 300 of the emergency call information in a shorter time compared to communicating in normal mode (step S122 in Figure 9).

[0185] Therefore, this life-saving system 1 can suppress the decrease in the response speed to emergency calls when the number of automatic calls increases.

[0186] More specifically, in the event of a large-scale disaster such as an earthquake, tsunami, or typhoon, a very large number of automatic notification devices issue automatic notifications almost simultaneously. Conventional life-saving systems cannot efficiently process these automatic notifications due to the concentration of emergency calls and a shortage of operators (communication commanders) to respond to them. In contrast, this life-saving system 1 can efficiently process such a large number of automatic notifications even when a very large number of automatic notification devices issue automatic notifications almost simultaneously, through high-speed serial processing by a high-capacity operator agent 101 or parallel processing by multiple operator agents 101.

[0187] Furthermore, in the first embodiment of the life-saving system 1, the emergency call center device 100 can communicate in normal conversation mode with the caller (person) who has made a manual call using the emergency call device 200 (step S111 in Figure 11).

[0188] The emergency call center device 100 can then attempt to give instructions to the caller (person) regarding how to deal with the emergency situation using an automated voice, based on the content of the conversation with the caller (person) (step S112 in Figure 11).

[0189] Therefore, according to the first embodiment of the life-saving system 1, in normal conversation mode, it is possible to give instructions to the caller (person) on how to deal with the emergency using machine voice.

[0190] Furthermore, in the first embodiment of the life-saving system 1, if the emergency call center device 100 determines that the caller (person) does not understand the machine-voiced instructions (explanation, persuasion) on how to deal with the emergency (NO in step S113 of Figure 11), it changes the communication method with the caller to a direct conversation method (step S114 of Figure 11).

[0191] Therefore, according to the first embodiment of the life-saving system 1, for callers (people) who cannot understand machine voice instructions in normal conversation mode, a human operator at the emergency call center 1a can give instructions (explanations, persuasion) in human voice (the voice of the human operator).

[0192] Thus, in the first embodiment of the life-saving system 1, the emergency call center device 100 can communicate with the emergency call device 200 that made the automatic emergency call (YES in step S13 of Figure 7) in high-speed conversation mode (step S121 of Figure 9), and if the emergency call is a manual emergency call, the person who made the manual emergency call can communicate directly with the human operator (step S114 of Figure 11).

[0193] Therefore, according to the first embodiment of the life-saving system 1, while suppressing a decrease in the response speed to emergency calls when automatic calls increase, when an emergency call is a manual call, the caller (person) and the human operator can communicate directly.

[0194] Incidentally, the life-saving system 1 of the first embodiment can be considered as a multi-AI agent learning system that includes one or more (a small or large number) operator agents 101 and multiple (a large number) caller agents 201.

[0195] Therefore, by using, for example, a mathematical data sharing mode for communication between AI agents in the life-saving system 1 as a multi-AI agent learning system, data exchange between AI agents can be made more efficient, distributed learning can be accelerated, and the overall performance of the life-saving system 1 can be rapidly improved. This makes it possible to more effectively suppress the decrease in response speed to notifications when the number of automated notifications increases.

[0196] (Maintaining up-to-date information) The recipient information management unit 10, the potential recipient information management unit 30, and the potential donor information management unit 40 always maintain up-to-date information.

[0197] (Forecasting potential demand and continuous hypothetical suitability assessment) The potential recipient prediction unit 20 continuously predicts future organ demand (potential recipients) based on machine learning models (such as cohort transformation models and personalized machine learning models) using real-time EHRs and large-scale registry data.

[0198] The transplant suitability detection unit 50 continuously evaluates the compatibility between a large number of recipients and potential donors based on recipient information managed by the recipient information management unit 10 and potential donor information managed by the potential donor information management unit 40, and selects pairs with high compatibility.

[0199] Furthermore, the transplant suitability detection unit 50 continuously evaluates the suitability of potential donors to future recipients, even if there are no recipients at present, based on information on potential recipients managed by the potential recipient information management unit 30 and information on potential donors managed by the potential donor information management unit 40, and selects pairs with high suitability.

[0200] (Advanced monitoring of health status) When an abnormal situation occurs in the emergency call device 200 of a potential donor, and the caller agent 201 issues an automatic emergency call, the operator agent 101 of the emergency call center device 100 communicates with the caller agent 201 in high-speed conversation mode. The health status monitoring agent 61 works closely with the operator agent 101 and the caller agent 201 in high-speed conversation mode to collect emergency call information, including biometric data (EEG, heart rate, etc.), location information, and circumstances of the incident of the seriously injured or ill person, in an extremely short time without human intervention.

[0201] The health monitoring unit 60 immediately analyzes the collected emergency call information and determines whether the potential donor has reached a predetermined health condition (e.g., severe trauma, cardiac arrest, severe decline in brain function).

[0202] (Rapid donor selection and initiation of the organ donation process) If a potential donor falls into a critical health condition (a specified health condition) and their suitability is confirmed by the transplant suitability detection unit 50, the donor selection unit 70 selects that potential donor as a donor. This selection decision is made instantaneously through high-speed data exchange and judgment between AI agents, without waiting for manual judgment by a human operator.

[0203] After selection, the operator agent 101 of the emergency call center device 100 quickly notifies designated emergency responder devices 300 (e.g., ambulance devices, emergency command system devices, organ preservation team devices, etc.) of emergency response request information containing information on the selected donor and suitable recipients (including potential recipients). This allows preparations for organ retrieval, preservation, and transport of the donor to begin quickly and efficiently, simultaneously with the occurrence of a donor (or a condition likely to lead to death).

[0204] (Organ preservation) If there is no suitable recipient for the extracted organ, or if the most suitable recipient is a potential recipient (future patient), the donation process is initiated as needed, and the organ is transported to the organ storage unit 80. In the organ storage unit 80, the organ is stored in a state where it can be restored (resuscitated) to a transplantable state until a predicted recipient is registered on the waiting list.

[0205] 2-4. Effects of the First Embodiment According to the first embodiment of the life-saving system 1, the following effects can be obtained in life-saving medical care, including organ transplantation.

[0206] (Minimizing ischemic time and improving survival rates) AI instantly detects sudden changes in the health status of potential donors and automatically completes the matching and donor selection process with highly compatible recipients, significantly reducing ischemia time (the time from donor identification to restoration of blood flow) compared to traditional processes that rely on human judgment. This improves recipient survival rates and organ graft survival rates.

[0207] (Maximizing the use of organ resources and meeting future demand) The potential recipient prediction unit 20 predicts future organ demand, the transplant suitability detection unit 50 continuously evaluates potential suitability in advance, and the organ preservation unit 80 preserves organs in a transplantable state until recipients predicted as potential recipients are registered on the waiting list. This maximizes organ donation opportunities and enables an optimal matching strategy that takes into account not only current but also future recipient demand.

[0208] (Ensuring high efficiency and objectivity in emergency response) Even in the event of an increase in automated calls or a large-scale disaster, the AI ​​agent's high-speed conversation mode and parallel processing can suppress a decrease in the response speed to emergency calls. Furthermore, by continuously evaluating compatibility such as blood type and HLA type in advance and selecting donors based on objective data, a rapid and objective organ donation process is ensured, leading to increased efficiency in life-saving medical care.

[0209] 2-5. Example Operation Scenario An example of an operational scenario for the life-saving system 1 of the first embodiment will be described.

[0210] (Premise) The assumptions for this operational scenario are as follows: Recipient Information: Information on recipient A, who has a high level of urgency, is registered in the recipient information management unit 10. Pre-selection of potential donors: The transplant suitability detection unit 50 pre-selects potential donors (donor candidates) B, C, and D that are suitable for recipient A. Initial status of the donor candidate: Potential donor B is healthy. Post-donor candidate's condition: Potential donor D later progresses through bradycardia (a condition where the heart rate is significantly below the normal range) to a critical condition in which recovery of brain function is predicted to be extremely difficult. Post-donor status: Potential donor C later becomes medically unsuitable (e.g., elevated body temperature, high likelihood of active infection), but does not reach the severe condition seen in candidate D.

[0211] (process) The process performed by this life-saving system 1 is as follows: Continuous monitoring of biometric data: The health status monitoring unit 60 continuously monitors biometric data from the emergency notification devices 200 associated with potential donors B, C, and D. Detection of changes in health status: The health status monitoring unit 60 detects that potential donor B's vital signs are stable. The health status monitoring unit 60 detects that potential donor C has become medically unsuitable. The health status monitoring unit 60 detects a sign of a serious health condition, bradycardia, in potential donor D. Suspension and exclusion of the donor selection process: The donor selection unit 70 immediately excludes potential donor C, who is medically unsuitable, from the selection process. The donor selection unit 70 immediately suspends the donor selection process in order to prioritize life-saving measures for potential donor D. Notification to emergency responders and initiation of life-saving measures: Operator agent 101 prioritizes saving the life of potential donor D, and while gathering detailed information in high-speed conversation mode, immediately notifies the nearest emergency responder device 300 of D's location and condition. Based on this notification, the emergency response team immediately rushes to the scene and begins life-saving measures such as cardiopulmonary resuscitation (CPR) on candidate D. Continuous monitoring of condition and confirmation of transition to critical condition: While the emergency response team is carrying out life-saving measures, the health monitoring unit 60 continuously monitors the condition of potential donor D. Based on the monitoring results, it is confirmed from changes in biometric data such as electroencephalograms that, despite life-saving measures, the condition of potential donor D has further deteriorated and has transitioned to a critical condition in which recovery of brain function is predicted to be extremely difficult. Donor selection: At this point, the donor selection unit 70 confirms that potential donor D meets the transplant criteria and is in a predetermined severe health condition that exceeds the limits of life-saving measures, and selects potential donor D as the donor for recipient A. (effect) This process ensures that the opportunity for organ donation is only realized after every possible effort has been made to save the life of a potential donor.

[0212] More specifically, a "predetermined severe health condition beyond the limits of life-saving measures" refers to a technical state in which, after maximum medical intervention to save the life of a potential donor has been exhausted, recovery is deemed extremely difficult based on objective AI analysis. This determination is automatically made by a combination of threshold conditions constructed in accordance with current legal and medical guidelines (e.g., brain death determination protocols and standard time limits for cardiopulmonary resuscitation). Specifically, the predetermined health condition is determined to have been reached when the probability of difficulty in brain function recovery (P_BrainLoss), calculated in real time by the health status monitoring agent 61, continuously exceeds a predetermined threshold (e.g., 90%) despite attempts at resuscitation by the emergency response team, and the time elapsed for life-saving measures linked from the emergency responder device exceeds the time limit of the resuscitation protocol. As a result, the donor selection unit 70 automatically triggers the donor selection and organ donation process for highly suitable recipients (including potential recipients) by the transplant suitability detection unit 50, based on objective AI judgment, without waiting for a final human decision, thereby minimizing ischemic time and increasing the efficiency of life-saving medical care.

[0213] The composite threshold conditions for "specified health conditions" that trigger donor selection are set to ensure a balance between maximum life-saving efforts and organ preservation, and the specific thresholds are based on the following medical and engineering rationale. The threshold of 90% for the probability of difficulty in recovering brain function (P_BrainLoss) is based on the engineering of the statistical limit of resuscitation success rates in current life-saving medical protocols (data where the resuscitation success rate drops to less than 10%), and the technical basis for determining "extremely difficult recovery" is when the recovery probability calculated by the AI ​​exceeds 90%, which correlates with this limit. The specified limit time for "exceeding the limit time for life-saving measures" is based on the standard resuscitation attempt time recommended in cardiopulmonary resuscitation (CPR) guidelines (e.g., 20 to 30 minutes), and is dynamically adjusted by an individualized algorithm that takes into account the age of the potential donor and the risk of the underlying disease, depending on the situational context (e.g., hypothermia). Furthermore, the organ preservation criteria (CSS_Org) define a probability of irreversible organ damage (P_OrganDamage) of less than 5% as "below critical level." This 5% threshold is a medical and engineering criterion derived from past clinical data on the functional prognosis of organs in the field of transplant surgery, and is calculated backward from the maximum acceptable range of ischemic injury to ensure long-term graft survival.

[0214] 3. Second Embodiment In the following explanation, components that are functionally common to those already described will be denoted by the same symbols, and their explanations will be omitted as appropriate.

[0215] The emergency call center device 100 of the life-saving system 1 shown in Figure 13 has a communication unit 110 and an emergency call determination unit 170. The emergency call determination unit 170 has a function to determine whether the emergency call is an automatic or manual call. The automatic conversation unit 111 does not need to have a function to determine whether the emergency call is an automatic or manual call.

[0216] The call unit 110 makes a call using either the automated conversation unit 111 or the direct conversation unit 112, based on the result of the emergency call determination unit 170.

[0217] According to this second embodiment, similar to the first embodiment, while suppressing a decrease in the response speed to emergency calls when automated calls increase, it is possible for the caller (person) and the human operator to communicate directly when the emergency call is a manual call.

[0218] 4. Third Embodiment 4-1. Composition The overall configuration of the third embodiment of the life-saving system 1 shown in Figure 14 is the same as that of the first embodiment. However, in the third embodiment of the life-saving system 1, the emergency call device 200 also functions as an emergency responder device 500. That is, each emergency call device 200 in the third embodiment of the life-saving system 1 has a caller agent 201 and an emergency responder agent 301. The program for realizing the emergency responder agent 301 is stored in the memory 222, storage unit 250, etc., of the emergency call device 200 as the emergency responder device 500.

[0219] In the third embodiment, the emergency call center device (in other words, the operator agent 101) 100 has a function to transmit emergency response request information to a predetermined emergency responder device 500.

[0220] The designated emergency responder device 500 is the emergency responder device 500 (in other words, the emergency call device 200) of a user who is registered as having the skills required for emergency response (hereinafter also referred to as a "skilled worker registered user"). Information on the designated emergency responder device 500 and its skilled worker registered user is stored in a database (not shown) accessible by the emergency call center device 100.

[0221] The types of users who can register as having the skills required for emergency response vary depending on the type (category) of emergency response. For example, if the emergency response involves saving lives, medical professionals such as doctors and nurses, or individuals who have completed and been certified in life-saving training, can register as life-saving technicians. Similarly, if the emergency response involves rescue, individuals with rescue-related qualifications such as water rescuer qualifications, mountain rescuer qualifications, international rescue qualifications, or firefighter qualifications, or those with experience in these fields, can register as rescue technicians.

[0222] For example, if the emergency response involves saving a life, the emergency call center device 100 (in other words, the operator agent 101) will transmit emergency response request information to a predetermined emergency responder device 500 if it detects such a device within a predetermined distance from the location of the person in need of assistance.

[0223] The emergency response device 500 (in other words, the emergency response agent 301) has the function of receiving emergency response request information from the emergency call center device 100 and notifying the registered user of the emergency response device 500 of the contents of that emergency response request information.

[0224] (Operator Agent 101) The operator agent 101 can automatically perform predetermined processes related to emergency response requests. These predetermined processes include an emergency response request information transmission process that sends emergency response request information to one or more predetermined emergency response device 500; a response availability determination process that determines whether or not a response (hereinafter referred to as "response available") has been received from one or more emergency response device 500 that are recipients of the emergency response request information; and a retransmission process that repeats the emergency response request information transmission process until at least one response is available.

[0225] (Emergency Response Agent 301) The emergency response agent 301 is an AI agent (software agent) that operates in each emergency response device 500. The emergency response agent 301 is composed of multiple sub-agents and can handle complex tasks and dynamic environments. The emergency response agent 301 can automatically execute predetermined processes related to emergency response. These predetermined processes include emergency response request information reception processing, emergency response related processing, location information acquisition processing, emergency response request content presentation processing, response feasibility determination processing, response feasibility transmission processing, etc. Hereinafter, the emergency response device 500 on which the agent 301 operates will also be referred to as the device 500.

[0226] The emergency response request information reception process is the process of receiving emergency response request information. The emergency response-related processing is the process of enabling emergency response based on the emergency response request information. The location information acquisition process is the process of acquiring the current location information of the device 500. The emergency response request content presentation process is the process of presenting the content of the emergency response request to the registered skilled user of the device 500 using the information presentation unit 230. The response feasibility determination process is the process of determining whether the registered skilled user of the device 500 will respond to the emergency response request. The response feasibility response transmission process is the process of transmitting a response feasibility response to the emergency notification center device 100.

[0227] 4-2. Processes performed by the emergency call center system and emergency call device. (Emergency call center device 100) In addition to the processes shown in Figures 6 to 12, the emergency call center device 100 can also perform the processes illustrated in Figure 15 (processing by the emergency call center device).

[0228] As shown in Figure 15, the emergency call center device 100 transmits emergency response request information to a predetermined emergency responder device 500 (Step S141: Emergency response request information transmission process). The emergency call center device 100 preferentially transmits the emergency response request information to one or more predetermined emergency responder devices 500 located closer to the location of the person requiring emergency response (hereinafter referred to as "person in need of life" in the description of the third embodiment).

[0229] Next, the emergency call center device 100 determines whether or not there is a response from the emergency responder device 500 to which the emergency response request information has been sent (Step S142: Determination of whether or not a response is possible). For example, if there is a response within a predetermined time (for example, within 15 seconds) after sending the emergency medical assistance request information, the emergency call center device 100 determines that there is a response, and if there is no response within the predetermined time, it determines that there is no response.

[0230] If it is determined that there was no response (NO in step S142), the emergency call center device 100 performs a process to resend the emergency response request information (step S141: retransmission process). The retransmission process includes the process of resending the emergency response request information to the emergency responder device 500 to which the emergency response request information has already been sent, and the process of sending the emergency response request information to an emergency responder device 300 other than the emergency responder device 500 to which the emergency response request information has already been sent.

[0231] If it is determined that a response is possible (YES in step S142), the emergency call center device 100 terminates the current process (END).

[0232] In the third embodiment, since the predetermined emergency responder device 500 is also a predetermined emergency notification device 200, the emergency response request information transmission process (step S141) can be included, for example, in at least one of the emergency notification information notification process (step S122) in Figure 9 and the emergency notification information notification process (step S132) in Figure 10.

[0233] Alternatively, the human operator who has spoken directly with the caller through the direct conversation processing (step S28) in Figure 12 may operate a predetermined device in the emergency call center 1a to transmit emergency response request information to a predetermined emergency responder device 500.

[0234] (Emergency responder device 500) In addition to the emergency call device-side call processing (step S22) shown in Figure 12, the emergency responder device 500 can also perform the processing illustrated in Figure 16 (processing by the emergency responder device).

[0235] As shown in Figure 15, the emergency responder device 500 receives emergency response request information (step S31: emergency response request information reception process) and performs emergency response-related processing (step S32).

[0236] As shown in Figure 16, in the emergency response-related processing (step S32), the emergency responder device 500 acquires its current location information using its GPS function (step S321: location information acquisition processing).

[0237] Next, the emergency responder device 500 performs the emergency response request content presentation process (step S322). The emergency response request content presentation process (step S322) includes the process of displaying the emergency response request content display screen G, as illustrated in Figure 17, on the display 231 (touch panel 244) based on the current location information and emergency response request information of the device 500.

[0238] Next, the emergency responder device 500 determines whether the user of the device 500 will respond to the emergency response request (step S323: response availability determination process). If the user of the device 500 determines that they will respond to the emergency response request (YES in step S323), it sends a response availability message to the emergency call center device 100 (step S324).

[0239] As illustrated in Figure 17, the emergency response request display screen G shows a map M indicating the current location P1 of the device 500 and the current location P2 of the person in need of rescue, text information T about the person in need of rescue, and a button B that allows the user of the device 500 to select whether or not to respond to this emergency response request. The map M also shows the location P3 of the AED.

[0240] Button B includes a YES button B1 and a NO button B2. The YES button B1 is an operator that the user operates (e.g., a tap operation) when responding to an emergency response request. The NO button B2 is an operator that the user operates when not responding to an emergency response request.

[0241] When the YES button B1 is operated, the emergency responder device 500 determines that the user of the device 500 responds to the emergency response request (YES in step S323, FIG. 16), and transmits a corresponding response to the emergency call center device 100 (step S324, FIG. 16).

[0242] 4-3. Function According to the third embodiment, emergency response request information can be transmitted to a predetermined emergency responder device 500. Therefore, according to the third embodiment, in addition to the function of the first embodiment, the following functions are realized.

[0243] (Quick identification and request of skilled registered users) The emergency call center device 100 (operator agent 101) detects a sudden change of a potential donor (person in need of rescue), and generates emergency response request information. At this time, emergency response request information is transmitted to the emergency responder device 500 (emergency call device 200) of a user (skilled registered user, e.g., doctor, nurse, person who has completed a first aid training) registered as a person having skills required for rescue, etc., existing within a predetermined distance from the position of the person in need of rescue.

[0244] (Acceleration of initial first aid response) When the emergency responder device 500 receives the emergency response request information, it presents to the user the content including the current position of the person in need of rescue and the necessary emergency response (e.g., first aid treatment, location of AED). Thereby, it becomes possible to quickly respond to an emergency situation by obtaining the cooperation of skilled registered users near the potential donor without waiting for donor selection or the arrival of a specialized team.

[0245] 4-4. Effects related to organ transplantation According to the third embodiment, in addition to accelerating donor selection, the following effects can be obtained by strengthening the response in the initial stages.

[0246] (Accelerating initial life-saving measures before ischemic time occurs) To minimize ischemic time (the time from donor identification to the restoration of blood flow), which significantly impacts the success rate of organ transplantation, early response before donor identification is crucial. In the third embodiment, in parallel with a rapid donor selection process using AI, initial life-saving measures before ischemic time occurs can be accelerated with the cooperation of nearby registered skilled users. This increases the likelihood that essential initial treatments for maintaining organ function can be performed before the organ preservation specialist team arrives at the scene.

[0247] (Improving the efficiency and ensuring safety of the entire life-saving process) Emergency response requests are sent to the devices of individuals with the necessary skills for emergency response (registered skilled users), enabling rapid response to emergencies with the cooperation of a wide range of skilled individuals, not just firefighters and police officers. This early intervention contributes to the survival of potential donors, ultimately maximizing organ transplant opportunities and achieving greater efficiency in life-saving medical care.

[0248] 5. Fourth Embodiment 5-1. Composition The overall configuration of the life-saving system 1 in the fourth embodiment is the same as in the third embodiment. However, in the life-saving system 1 of the fourth embodiment, the operator agent 101 functions as a meta-AI (meta-agent). The operator agent 101 monitors, coordinates, manages, optimizes, etc., other AI agents (e.g., health status monitoring agent 61, donor selection agent 71, caller agent 201) in the life-saving system 1 as a multi-AI agent system.

[0249] More specifically, the operator agent 101 has functions such as behavior monitoring, evaluation, higher-order judgment, adjustment, risk management, learning process optimization, and correction. These functions work in conjunction with each other.

[0250] The behavior monitoring function is a feature that monitors the behavior of other AI agents in real time.

[0251] The evaluation function analyzes the behavior of other AI agents and assesses risk factors. For example, the evaluation function assesses the possibility of goal inconsistencies or conflicts between AI agents, and the likelihood of AI agents deviating from their respective set goals. These possibilities are evaluated as values ​​for predetermined risk factors.

[0252] The higher-order decision function is a function that makes sophisticated decisions to optimize the life-saving system 1. More specifically, the main role of the higher-order decision function is to make various decisions from the perspective of optimizing the life-saving system 1, based on the analysis and evaluation results from the evaluation function. For example, if problems arise such as inconsistencies or conflicts in goals among AI agents, or if there are AI agents whose likelihood of deviating from the goal is greater than a threshold (risk threshold), the higher-order decision function makes decisions regarding the redistribution of goals and tasks to each AI agent, intervention, adaptive enhancement, and modification in order to resolve these problems. The adjustment function, risk management function, learning process optimization function, and modification function operate based on the decisions made by the higher-order decision function.

[0253] The coordination function adjusts the goals and tasks of each AI agent so that multiple AI agents can work together and cooperate with one another. More specifically, the coordination function sets the task priorities of each AI agent and strengthens the coordination between AI agents to ensure harmonious operation, in order to maximize the probability of achieving the goals in the life-saving system 1. This coordination function distributes processing power and computing resources among multiple AI agents, improving the efficiency of the life-saving system 1.

[0254] The risk management function is designed to prevent malfunctions by limiting the operating range of each AI agent to prevent other AI agents from taking inappropriate actions. Specifically, the risk management function immediately intervenes in other AI agents that have detected predetermined behavior (AI agents whose predetermined risk factors are judged to be greater than the risk threshold). Intervention in other AI agents may include intervention through communication with other AI agents (persuasion, training), modification of algorithms and parameters through hacking of other AI agents, and termination of agent functions.

[0255] The learning process optimization function is a feature that evolves the learning algorithms of other AI agents to lead to better results. The main roles of the learning process optimization function are learning support, environmental adaptation promotion, and knowledge sharing. Through the learning process optimization function, the adaptive capabilities of other AI agents are promoted, and other AI agents evolve to respond more quickly to new environments and challenges. It also promotes the improvement of the adaptive capabilities of operator agent 101, which is an element of life-saving system 1.

[0256] The correction function identifies malfunctions and errors that occur in the life-saving system 1 and attempts to correct them automatically. The main roles of the correction function are self-diagnosis, fault recovery, and stability maintenance.

[0257] 5-2. Processing by the Operator Agent In addition to the processing already described, the operator agent 101 can perform the meta-processing shown in Figure 18 (step S300: processing by the meta-agent, processing by the emergency call center device 100). As shown in Figure 18, the operator agent 101 performs a behavior monitoring process (step S301) to monitor the behavior of other AI agents. Based on the results of the behavior monitoring process (step S301), operator agent 101 determines whether or not there are other AI agents that have detected a predetermined behavior (step S302: Intervention Target Determination Process). If operator agent 101 determines that there are other AI agents that have detected a predetermined behavior (YES in step S302), it performs an intervention process to intervene in the other AI agent and suppress the predetermined behavior (step S303).

[0258] For example, operator agent 101 audits data sharing between informant agent 201, which notifies of the availability of a potential donor, and health status monitoring agent 61, which collects physiological data. The purpose of this is to detect fraudulent information manipulation or sharing (fraudulent sharing) that unfairly accelerates the organ donation process rather than providing life-saving treatment to potential donors. Fraudulent sharing can result from human intent or algorithmic bias in the system and can lead to ethical risks such as delays in life-saving treatment or inappropriate decisions. If operator agent 101 detects that data indicating the possibility of recovery is being deliberately ignored and that the transition to organ donation is being forced, it immediately determines this to be an ethical risk and performs a mandatory intervention (S303).

[0259] This intervention is an emergency measure to prevent data distorted or accelerated processes due to fraudulent collaboration from influencing the decisions of downstream donor selection agents 71. Specifically, operator agent 101 automatically filters or invalidates fraudulent data and forces donor selection agents 71 to re-evaluate based on sound data. Operator agent 101 also temporarily and completely halts the organ donation process by donor selection agents 71 to allow time for a detailed investigation and re-evaluation.

[0260] Also, for example, when the operator agent 101 determines that the reporter agent 201 is performing an inappropriate operation that prioritizes the communication speed over the integrity of information while the reporter agent 201 is executing the high-speed conversation mode, the operator agent 101 executes an intervention process (S303) on the reporter agent 201. In this case, the operator agent 101 sends a control signal to the reporter agent 201 to switch the mode so that the necessary data points are surely transmitted even if the call time is increased somewhat, and modifies / suppresses its behavior.

[0261] Also, for example, the operator agent 101 detects an ethical risk of ignoring the principle of priority for saving lives and inappropriately accelerating the organ donation process between the emergency responder agent 301 and the agent on the organ donation management device 600 side. When detecting this ethical risk, the operator agent 101 immediately executes an intervention process (S303) as a meta-AI. This process is performed by modifying or suppressing the operation of the donor selection agent 71, which is the final decision-making body for unauthorized cooperation.

[0262] Also, for example, when the operator agent 101 determines that the emergency responder agent 301 is performing an operation that prioritizes resources such as an AED nearby over a responder with more appropriate specialized skills from the perspective of the overall suitability of the emergency response, the operator agent 101 executes an intervention process (S303) on the emergency responder agent 301. In this case, the operator agent 101 sends a control signal to the emergency responder agent 301 to change the priority of the emergency response request from a resource nearby but with low skills to a more distant but highly specialized appropriate responder, and modifies / suppresses its behavior.

[0263] Also, for example, when the health status monitoring agent 61 or the donor selection agent 71 detects an operation that overly prioritizes organ donation (e.g., when trying to prioritize the start of the donor selection process before sufficient life-saving measures are taken for a potential donor), the operator agent 101 evaluates this as a predetermined behavior (ethical risk factor).

[0264] When operator agent 101 detects this ethical risk, it immediately performs an intervention process (S303) to modify or suppress the actions of health status monitoring agent 61 and donor selection agent 71 (S303). For example, operator agent 101 temporarily suspends or suppresses the notification of monitoring results by health status monitoring agent 61 to donor selection unit 70. Operator agent 101 also prompts escalation to a human operator if necessary.

[0265] Furthermore, if, for example, the evaluation algorithm in the transplant suitability detection agent 51 is found to have been influenced by human or data-driven bias, and a risk is detected that ethical standards such as medical urgency and fair waiting periods may be compromised, the operator agent 101 will suppress the suitability detection process (S303).

[0266] Furthermore, if, for example, the prediction model of the potential recipient prediction agent 21 is influenced by medically inappropriate factors such as socioeconomic status or regional characteristics, resulting in ethical bias where a specific group of individuals is unfairly excluded from the benefits of the prediction, the operator agent 101 will suppress the generation of the prediction results and their provision to the potential recipient information management agent 31 (S303).

[0267] 5-3. Effects According to the life-saving system 1 of the fourth embodiment, the following effects and benefits can be obtained. (1) Minimizing ischemic time and ensuring absolute priority is given to saving lives. In the process from detecting sudden changes in potential donors to selecting suitable donors, the meta-AI (operator agent 101) monitors and adjusts the behavior of other agents in real time, maximizing the accuracy and speed of decision-making, thereby significantly reducing ischemia time (the time from donor identification to restoration of blood flow). Furthermore, the ethical risk management function immediately corrects and suppresses the behavior of agents that deviate from the priority principles of life-saving measures, ensuring not only an improvement in recipient survival rates but also absolute priority in life-saving efforts for donors.

[0268] (2) Optimal utilization of organ resources and strengthening response to future demand The potential recipient prediction agent 20 and the transplantation criterion suitability detection agent 51 continuously perform predictions and evaluations based on real-time EHRs and large-scale registry data. In addition, meta-AI optimizes the learning process of each agent and corrects data bias, improving prediction accuracy and the objective fairness of suitability evaluation. This strengthens the feasibility of realizing an optimal matching strategy that takes into account not only current but also future recipient demand.

[0269] (3) Extreme efficiency and ethical assurance of objectivity in emergency response. Even during automated notifications or large-scale disasters, Meta AI coordinates and optimizes the coordination and communication between multiple AI agents, maximizing the efficiency of the high-speed conversation mode and maximizing the speed of response to emergency calls. In addition to selection based on objective data such as blood type and HLA type, Meta AI's function of actively detecting and correcting ethical risks (such as fraudulent collaboration and inappropriate prioritization) ensures the fairness and objectivity of the entire organ donation process from an ethical standpoint.

[0270] Thus, the fourth embodiment makes it possible to achieve the goal of "saving more lives" while ensuring the safety and ethical integrity (minimizing ethical issues) of the entire life-saving system 1.

[0271] Furthermore, if a multi-AI agent system (humanitarian system) aiming to "save more lives," as in the fourth embodiment, can be realized as ASI (Artificial Super Intelligence) faster than, for example, a multi-AI agent system (inhumane system) aiming to "take more lives," then it may be possible to preemptively suppress or restrain the latter (inhumane system) with the former (humanitarian system). This has the potential to help address problems such as multi-AI agent technology being used in war or terrorism, resulting in the loss of human lives. According to the fourth embodiment, in addition to the effects and advantages of the third embodiment, the safety of the life-saving system 1 as a multi-AI agent system is improved.

[0272] 6. Fifth Embodiment 6-1. Composition (Lifesaving method) The fifth embodiment of the life-saving method is a life-saving method using a multi-AI agent system for providing donor organs to a recipient. In this life-saving method, agents included in the multi-AI agent system (life-saving system 1) perform the processes shown in Figure 19.

[0273] Recipient information management step (S401) in which the recipient information management agent 11 manages the recipient information. The potential recipient prediction step (S402) involves the potential recipient prediction agent 21 predicting the potential recipient. Potential recipient information management step (S403): The potential recipient information management agent 31 manages information about potential recipients. Potential donor information management step (S404) involves the potential donor information management agent 41 managing the information of potential donors. Match detection step (S405): The match detection agent 51 detects potential donors (matched individuals) that meet the transplantation conditions for each recipient or for each potential recipient, based on the recipient information, potential recipient information, and potential donor information. Health status monitoring step (S406): Health status monitoring agent 61 monitors the health status of each potential donor. The donor selection step (S407) involves a donor selection agent 71 selecting potential donors who meet the transplant criteria and are in a specified state of health. Emergency response request information transmission step (S408): Operator agent 101 transmits emergency response request information, including information on selected donors and suitable recipients and / or potential recipients, to emergency response agent 301 working on a device associated with the emergency responder, based on a call with caller agent 201.

[0274] 6-2. Action and Effects (Pre-assessment of suitability based on future demand and maximization of resources) Firstly, based on the future transplant demand forecast by the potential recipient prediction agent 21 (S402) and the management of the latest information by the information management agents 11, 31, and 41 (S401, S403, S404), the match detection agent 51 continuously and proactively evaluates the suitability of current and future recipient candidates (S405). This allows for a decision to transition to the donation process (organ preservation) at any time, even if there are no current recipients with high urgency when a donor becomes available, as long as the existence of a potential recipient who will be suitable in the future is confirmed. This prevents the waste of valuable organ resources and maximizes their utilization.

[0275] (Minimizing ischemic time through multi-AI agent collaboration) Secondly, based on the real-time monitoring of the potential donor's health status by the health status monitoring agent 61 (S406) and the results of the pre-evaluation by the match detection agent 51, the donor selection agent 71 immediately performs donor selection the moment a potential donor reaches a predetermined critical health condition (S407). Since the selection is completed through high-speed data exchange between AI agents without human judgment, the waiting time from donor discovery to organ retrieval is shortened to the absolute minimum, and the recipient survival rate and transplant organ graft survival rate are improved by minimizing ischemic time.

[0276] (Immediate processing through autonomous emergency response commands) Thirdly, after the selection by the donor selection agent 71 is completed, the operator agent 101 immediately and automatically transmits emergency response request information, including information on the selected donor and the most suitable recipient (including potential recipients), to the emergency response agent 301 (S408). This ensures a seamless process from donor selection to dispatching the on-site response team, allowing preparations for organ retrieval, preservation, and transport to begin without delay, thereby enabling a rapid and efficient response in time-constrained emergency medical care.

[0277] 7. Sixth Embodiment 7-1. Composition (Life-saving system 1) The sixth embodiment encompasses the life-saving method of the fifth embodiment. In the sixth embodiment, blockchain technology is applied to the life-saving system 1 of the first embodiment. More specifically, the life-saving system 1 of the first embodiment generates a digital identifier on the blockchain corresponding to each organ (heart, liver, kidney, etc.) when a decision is made to remove an organ or when potential donor information is registered in the system, in order to manage the organs of potential donors "organ by organ".

[0278] Organs are managed on an organ-by-organ basis using organ NFTs. Organ NFTs are non-fungible tokens (NFTs) that serve as digital identifiers linking unique attributes and history to each organ. Organ NFTs can also be interpreted as "organ IDs."

[0279] As shown in Figure 20, the organ donation management device 600 of the sixth embodiment of the life-saving system 1 includes, in addition to the configuration of the first embodiment, a blockchain recording unit 81, an identifier generation unit 82, a history recording unit 83, and a storage treatment data recording unit 84.

[0280] The hardware configuration of the organ donation management device 600 in the sixth embodiment is the same as in Figure 5. Each functional part of the organ donation management device 600 is realized through the cooperation of hardware and software.

[0281] The blockchain recording unit 81 is a functional unit for recording the fact of donor selection by the donor selection unit 70 and the information that forms the basis of that objective judgment in an immutable format.

[0282] The identifier generation unit 82 is a functional unit that generates a unique digital identifier, including a nonfungible token (NFT), for each type of organ provided by a potential donor and registers it on the blockchain.

[0283] The history recording unit 83 is a functional unit for recording the results of suitability detection, the results of donor selection, and the entire physical and medical history from organ extraction to donation to the recipient, linked to a digital identifier and recorded on the blockchain.

[0284] The preservation treatment data recording unit 84 is a functional unit for recording preservation treatment data, including the history of temperature, pressure, or drug application during long-term preservation treatment of organs by the organ preservation device (organ preservation unit) 700, on a blockchain, linked to a digital identifier assigned to the organ.

[0285] To elaborate on the hardware implementation of each functional unit in the sixth embodiment, the blockchain recording unit 81, identifier generation unit 82, history recording unit 83, and storage treatment data recording unit 84 shown in Figure 20 are realized through the cooperation of the control unit 620, storage unit 650, and communication unit 660 shown in Figure 5. Specifically, the memory 622 additionally stores dedicated programs for executing communication protocols with the blockchain network and smart contracts, and the processor 621 executes these programs to perform hash value calculation and NFT generation processing as the identifier generation unit 82. Furthermore, the transaction issuance process to the distributed ledger by the blockchain recording unit 81 is realized by the communication unit 660 transmitting the signed data generated by the processor 621 to an external blockchain node via the communication network 400. In addition, a private key and wallet information that manages the right to write to the blockchain are stored in an isolated secure area within the storage unit 650 and are used for authentication when the history recording unit 83 and storage treatment data recording unit 84 record data. This allows for the physical support of the sixth embodiment, which requires tamper-proof recording and high security, while maintaining a general-purpose hardware configuration.

[0286] (Effect 1) With the above configuration, the life-saving system 1 of the sixth embodiment provides the following functions and effects. By recording the facts and criteria for donor selection (suitability, serious health conditions) on an immutable blockchain, it is possible to contribute to ensuring ethical objectivity and improving social trust in the rapid AI-driven donor selection process.

[0287] By generating a unique digital identifier, including NFTs, for each organ type and linking and recording the entire physical and medical history from extraction to donation, ultimate traceability in the organ donation process can be achieved, eliminating the risk of data tampering and dramatically improving organ quality control and transparency.

[0288] By recording and verifying detailed data such as temperature and chemical solutions used in long-term organ preservation procedures on a blockchain, it becomes possible to objectively prove the quality assurance of long-term preserved organs. This will increase the reliability of transplant decisions for future recipients and allow for the management of the risks of long-term organ preservation in the ongoing donation process.

[0289] (Operation of life-saving system 1) The life-saving system 1 of the sixth embodiment performs, for example, the organ NFT lifecycle recording process (S500) shown in Figure 21. The organ NFT lifecycle recording process (S500) is a process to record the history from organ extraction to transplantation using NFTs and to prevent tampering with the history. The organ NFT lifecycle recording process (S500) includes identifier generation processing (S501), suitability assessment recording processing (S502), ethical transparency assurance processing (S503), and provision / storage recording processing (S504).

[0290] Various agents, such as the potential donor information management agent 41, the donor selection agent 71, the match detection agent 51, the health status monitoring agent 61, the operator agent 101, and the emergency response agent 301, work together to perform the necessary processing by coordinating the functions of the blockchain recording unit 81, the identifier generation unit 82, the history recording unit 83, and the storage treatment data recording unit 84.

[0291] Identifier generation process (S501): The potential donor information management agent 41 and / or donor selection agent 71 generate organ NFTs for each organ (e.g., kidney R / L, liver) based on the potential donor information (whether consent was given, HLA type, blood type, etc.) and record the first timestamp.

[0292] In other words, the identifier generation unit 82 generates unique identifiers (organ NFTs) for each organ (heart, kidney, etc.) based on information about potential donors. The history recording unit 83 creates these NFTs and an initial timestamp indicating the date and time of generation as recorded data. The blockchain recording unit 81 writes this initial recorded data to the blockchain, initiating the tracking history of the organs.

[0293] Suitability assessment record processing (S502): The suitability detection agent 51 records the results of the suitability assessment (e.g., suitability scores for recipient A and potential recipient X) in the organ NFT, and persists the basis for its objective judgment.

[0294] In other words, the history recording unit 83 adds the evaluation results, such as the compatibility score with the recipient calculated by the transplantation condition suitability detection unit 50, the evaluation criteria, and the ID of the suitable recipient, to the recorded data as an event log. The blockchain recording unit 81 adds this history of evaluation results to the blockchain, serving as evidence of the fairness and objectivity of the suitability evaluation.

[0295] Ethical Transparency Assurance Process (S503): Health status monitoring agent 61 and / or donor selection agent 71 record the facts of the detection of a serious health condition that triggered organ harvesting (e.g., S405) and the final decision on donor selection (e.g., S406), thereby demonstrating that the ethical process was objectively carried out.

[0296] In other words, the history recording unit 83 adds the final donor selection decision by the donor selection unit 70 and proof that ethical processes such as prioritizing life-saving measures were properly implemented (such as monitoring records by operator agent 101) as important events in the recorded data. The blockchain recording unit 81 writes this record proving ethical transparency to the blockchain, irreversibly ensuring the legitimacy of the process.

[0297] Provision / Storage Record Processing (S504): Operator agent 101 and / or emergency responder agent 301 record the entire history of physical events and procedures in real time, including the time of organ retrieval, storage method, transport time, start and completion of resuscitation, and the receiving medical institution.

[0298] In other words, after organ retrieval, the organ preservation data recording unit 84 collects and records detailed data of the preservation procedures performed by the organ preservation specialist team (e.g., application of cryogenic freezing / supercooling / vitrification, temperature history, CPA concentration, etc.) as technical data. The history recording unit 83 records the event that the organ was provided and the preservation process was initiated. The blockchain recording unit 81 writes both this technical preservation data and the procedure event to the blockchain, ensuring the quality control history of the organ. This record is later used to determine whether the organ is suitable for resuscitation and transplantation.

[0299] (Effect 2) The life-saving system 1 of the sixth embodiment, which operates as described above, achieves the following effects through the application of blockchain. Achieving ultimate traceability: Organ NFTs record the entire history of an organ's movement, preservation procedures, and suitability assessments, from transplantation from the donor to the recipient, or long-term storage in the Organ Preservation Device 700, in an unalterable manner. This enables highly transparent management that leaves no room for doubt.

[0300] Ensuring objectivity and ethical integrity in the process: Because the time and facts of when the objective criteria of "transplant suitability" and "specified health condition" are met by the donor selection agent 71 are recorded, ethical concerns and the legitimacy of decisions in the organ donation process are objectively proven, and social trust is enhanced.

[0301] Risk management for long-term storage (on-demand provision process): By recording all data on storage and resuscitation processes in the organ storage unit 80 (temperature, application status of resuscitation agents, etc.), the quality of the organs can be guaranteed, and the reliability of future transplant decisions for predicted potential recipients can be maximized.

[0302] Enhanced information security: By recording only the hash values ​​(encrypted data fingerprints) of highly sensitive patient medical information (such as EHR linked data) on the blockchain, and limiting actual data access to authorized healthcare institutions, it is possible to achieve both data security and privacy.

[0303] 8. Alternative Examples The present invention is not limited to the embodiments described above. It will be obvious to those skilled in the art that various modifications or alterations can be conceived within the scope of the claims, and these will naturally also fall within the technical scope of the present invention. Furthermore, the components of the above embodiments may be combined in any way without departing from the spirit of the invention.

[0304] (Fit scoring based on dynamic information) In the first embodiment, the transplant suitability detection unit 50 may be configured to score the degree of suitability between a recipient or potential recipient and a potential donor, taking into account not only static information such as blood type and HLA type, but also dynamic information such as the current use of immunosuppressants by the recipient or potential recipient and the current functional status of the donor's organs (e.g., kidney function, liver function).

[0305] (Donor selection trigger combining on-site information) The health status monitoring unit 60 can also use a combination of biometric information from potential donors and on-site information from traffic accidents and disasters (e.g., car collision sensor data, disaster simulation results) as a trigger for donor selection.

[0306] (Extraction of vital signs through video analysis) The health status monitoring unit 60 can monitor the healthy state of potential donors based on data acquired from a non-contact vital sign sensing system that uses images from surveillance cameras and other sources. This system uses video analysis to detect subtle color changes, body movements, and posture changes of the potential donor's face and skin, and continuously monitors non-contact vital signs such as heart rate, respiratory rate, and state of wakefulness.

[0307] More specifically, in vital sign sensing using video analysis, the skin area (mainly the face and hands) of the subject (potential donor) is identified from video devices such as surveillance cameras and smartphones. The average brightness and color information of that area is analyzed over time, and noise is removed to extract periodic signals associated with the heartbeat. This makes it possible to measure heart rate (pulse rate) and respiratory rate from breathing movements accompanied by body movements, non-contact and continuously.

[0308] Vital sign sensing using video analysis allows for simultaneous monitoring of multiple potential donors without their awareness, for example, by using multiple surveillance cameras installed in a large space, to check for any signs of abnormality. In addition, by acquiring brainwave data from potential donors using devices capable of measuring brainwaves (such as head-mounted devices), and having AI recognize changes in the potential donor's posture, apnea, and abnormal facial color, it becomes possible to detect early signs of abnormality.

[0309] (Acquisition of detailed medical information through integration with electronic medical records) In addition to acquiring data from wearable devices, the health monitoring unit 60 may be configured to link with the electronic medical record system of the medical institution where the potential donor regularly receives treatment, and to acquire detailed medical information such as blood test data and imaging diagnostic results.

[0310] This allows for the acquisition of detailed and objective clinical information, such as blood test data and imaging results, in addition to biometric data (heart rate, blood pressure, etc.) from wearable devices. This significantly improves the accuracy of evaluating the health status of potential donors, particularly in determining organ condition and compatibility. Furthermore, it enables comprehensive medical monitoring using information from normal times as well as in emergencies, based on the long-term medical history and health trends of potential donors, which can contribute to more reliable donor selection and the preservation of organ donation opportunities.

[0311] (Donor selection considering geographical distance) The donor selection unit 70 may have a function that considers the geographical distance between the potential donor and the recipient, in addition to the compatibility score and health status. This makes it possible to shorten the organ transport time and further shorten the ischemic time.

[0312] (Centralized information management through an integrated database) The recipient information management unit 10 or the potential recipient information management unit 30 and the potential donor information management unit 40 may be configured as a management unit that uses a single integrated database, and the transplant condition match detection unit 50 may access this integrated database and select a highly matched pair.

[0313] By centrally managing recipient, potential recipient, and potential donor information in a single integrated database, the information retrieval and matching process by the transplant suitability detection unit 50 is accelerated compared to when multiple systems are involved. This improves the performance of the life-saving system 1, enabling rapid organ extraction from donors and provision of those organs to recipients in a transplantable state.

[0314] Furthermore, since all relevant information (such as the current recipient's medical urgency, predictive information for potential recipients, and the latest biometric data of potential donors) is managed on a single platform, data duplication, inconsistencies, and discrepancies in freshness can be prevented. As a result, information on sudden changes in potential donors detected by the health status monitoring unit 60 is reflected in the transplant suitability assessment without delay and reliably, improving the accuracy and reliability of the assessment.

[0315] Furthermore, by handling future organ demand forecasts (hypothetical information) from the potential recipient prediction unit 20, current waiting list information (actual demand), and a list of potential donors on the same platform, it is possible to dynamically determine the optimal organ resource allocation strategy that spans both the immediate donation process and the on-demand donation process. This maximizes opportunities for organ donation and makes life-saving medical care through organ transplantation extremely efficient.

[0316] Furthermore, because the AI ​​agent group that forms the core of the life-saving system 1 (operator agent, caller agent, health status monitoring agent, etc.) can directly exchange and reference highly efficient information (such as real-time health risk indicators) via a single database, the data flow in complex AI agent communication protocols (such as giverlink mode) is simplified, improving the overall system coordination and response speed.

[0317] (Global centralized management of EHRs) The life-saving system 1 will be linked to a global, centralized management system for EHRs. Specifically, it will be possible to realize a highly reliable medical data linkage platform using My Number (Japan), National Health Insurance Number (UK), Social Security Number (US), etc. Establishing such a global medical data linkage platform will enable the realization of a more efficient life-saving system that takes into account the global supply and demand balance of organs. Through this linkage, the organ donation management device 600 will be able to continuously grasp and process virtual information regarding the global demand and supply of organs.

[0318] Specifically, the potential recipient prediction unit 20 utilizes global EHR and registry data to predict with extremely high accuracy the risk and demand for future cross-border waiting list registrations for specific organ types (global demand forecasting).

[0319] The transplant suitability detection unit 50 continuously matches demand information of recipients and potential recipients worldwide with information on potential donor supply candidates. This makes it possible to identify the most suitable supply-demand pairs at a global level, transcending regional constraints, and contributes to maximizing transplant opportunities and improving transplant outcomes.

[0320] The organ preservation unit 80 enables the long-term storage of extracted organs in a transplantable state, especially when there are no suitable recipients or when potential recipients are anticipated in the future. This plays a crucial role in preventing the waste of globally scarce organ resources and increasing the certainty of supply to meet future demand.

[0321] (Real-time multilingual communication using predictive translation functionality) In the first embodiment, the interpretation function in the call unit 110 has an advanced real-time interpretation function (predictive interpretation function) that operates when the language of the caller and the language of the human operator differ. This interpretation function is executed by the AI ​​agent predicting (predicting) the content of the utterance before a unit of conversation (utterance) is completed. This minimizes call delays and enables rapid and smooth communication that overcomes language barriers, contributing to accurate information transmission and faster response in emergencies. This is one specific example of how "computer-based interpretation (machine interpretation)" in the direct conversation unit 112 can be efficiently realized by applying multi-AI agent technology.

[0322] (Grief care function provided by AI agent) The operator agent 101 may have a grief care function that, when interacting with the donor's family in connection with the donor selection process, estimates the family's emotional state and level of grief from their tone of voice and speech content, and dynamically adjusts the speed of the conversation, tone of voice, and vocabulary used according to the estimation results. This function allows the operator agent 101 to smoothly convey necessary information and confirm intentions while being mindful of the family's psychological distress, thereby increasing the family's acceptance of the system.

[0323] (Utilization of organs and cells as foundational resources for regenerative medicine) In the first embodiment, the life-saving system 1 that optimizes the organ donation process has the function of utilizing preserved organs or cells as foundational resources for regenerative medicine, and the function of integrating these technologies. The purpose of this alternative example is that preserved organs and cells are being utilized in regenerative medicine as "transplant resources" and "seeds of regeneration," and in particular, when combined with iPS cell and stem cell preservation technology, it has the potential to directly lead to resolving the shortage of organ transplants and realizing personalized medicine.

[0324] This alternative approach is applicable to the replacement and complementation of organ transplantation. While organ transplantation suffers from a severe shortage of donors, reliance on transplantation can be reduced by repairing and regenerating organs or their constituent cells stored in the organ preservation unit 80 of the life-saving system 1 using regenerative medicine technology. Examples include cardiomyocyte sheet transplantation after myocardial infarction and hepatocyte transplantation for liver disease.

[0325] This alternative method is applicable to the preservation and reuse of iPS cells and stem cells. iPS cells are pluripotent cells and, if preserved, can differentiate into necessary organs and tissues in the future. Creating them from the patient's own cells reduces rejection and enables personalized treatment. By linking with potential donor and recipient information managed by the organ donation management device 600, it becomes possible to centrally manage them as a cell resource that can be supplied stably over the long term through preservation technologies (such as vitrification and other preservation techniques).

[0326] This alternative is applicable to tissue engineering and 3D bioprinting. Specifically, it allows for the integration of a life-saving system with the ability to artificially print organs and tissues using cells from preserved organs as bio-ink (3D bioprinting technology). In addition to cartilage and skin regeneration, complex organs such as the heart and kidneys could be targeted in the future.

[0327] This alternative approach can also be applied to disease modeling and drug discovery research. Specifically, disease models can be created from cells of preserved organs and used to elucidate disease mechanisms and develop new drugs.

[0328] This alternative method can also be applied to improving beauty and quality of life. Specifically, it can be used for tooth regeneration, hair follicle regeneration, and skin regeneration using cells from preserved organs. This has the potential to expand its application beyond life-saving medicine to areas aimed at improving quality of life.

[0329] Preserved organs are crucial as foundational resources for regenerative medicine, and combining them with iPS cell and stem cell preservation technologies will directly contribute to resolving organ transplant shortages, personalized medicine, and drug discovery research. The integration of organ preservation technology, regenerative medicine, AI, and 3D printing will greatly contribute to the advancement of life-saving medical technology.

[0330] (Functionality of the AI ​​agent in the Emergency Response Device 300) In the third embodiment, the emergency responder agent 301 may be an AI agent that operates in each emergency responder device 500 and emergency responder device 300. With this configuration, it becomes possible to respond quickly to emergencies not only with the cooperation of registered skilled users of automobiles 200A, smartphones 200B, monitored terminals, smartwatches 200C, and smart glasses 200D, but also with the cooperation of registered skilled users riding in ambulances 300A, fire trucks 300B, patrol cars 300C, etc.

[0331] (Monitoring and coordination of multiple agents by an independent meta-AI) In the fourth embodiment, the operator agent 101 functions as a meta-AI, but the life-saving system 1 may have a separate meta-AI that monitors, coordinates, manages, optimizes, etc., the operator agent 101, the caller agent 201, and the emergency responder agent 301. In this case, the operator agent 101 also works under the command of the meta-AI together with the emergency caller agent 201 and the emergency responder agent 301.

[0332] (Advanced suitability assessment through ultra-early precision medical collaboration) The transplant suitability detection unit evaluates compatibility by considering recipient information, potential donor information, and real-time homeostatic indicators based on a comprehensive understanding of inter-organ networks for early disease prediction and prevention. This enables ultra-precise scoring that predicts the long-term success rate of transplantation by integrating and analyzing dynamic biological data such as the recipient's current immune response state and the micro-inflammation level of the donor organ, as well as static information such as blood type and HLA type.

[0333] (Maximizing information gathering through high-speed AI agent communication protocols) The operator agent 101 of the emergency call center device and the caller agent 201 of the emergency call device 200 communicate using ultra-fast conversation modes such as giverlink mode and mathematical data sharing mode, which are designed to reduce conversation time during emergencies. As a result, from the moment of the call, the AI ​​instantly structures and analyzes the potential donor's biometric data (heart rate, blood pressure, brain waves, etc.), and transmits highly efficient information that detects signs of serious health conditions to the organ donation management device in real time, faster than human intervention, enabling rapid donor selection and preparation of the medical team.

[0334] (Organ resuscitation using molecular nanotechnology (NMT)) The Organ Preservation Device 700 preserves organs through vitrification, a method suitable for long-term storage, and then revives (restores) them to a transplantable state by applying molecular nanotechnology (MNT), an autonomous robotic technology at the molecular level. During this revitalization process, MNTs explore the body's vascular network and cells, detecting and repairing not only ischemic damage, CPA (cryoprotective agent) toxicity, and physical damage caused by ice crystals and cracks, but also signs of chronic inflammation (senoinflammation), a common underlying factor in aging and major diseases, at the molecular level. This maximizes the function and health of the organ immediately before transplantation.

[0335] (A donor selection process that integrates ethical considerations) The donor selection unit 70 acts as a trigger to initiate a rapid organ donation process, but requires confirmation of the donor's wishes regarding organ donation. If a critical health condition is detected in a potential donor whose wishes are unknown, the system automatically initiates a process of contacting the family and a process of confirming pre-death wishes based on the EHR, prioritizing this over donor selection. This ensures both highly efficient rapid life-saving medical care and compliance with ethical and legal constraints.

[0336] (Application to the development of immunosuppressants) Large-scale data collection and structuring: The Recipient Information Management Department 10 and the Potential Donor Information Management Department 40 continuously collect and update detailed medical information (HLA type, blood type, medical history, EHR data, etc.) based on individual consent. This structured, large-scale real-world data can serve as a valuable data base for identifying factors that cause rejection.

[0337] Predicting Rejection Risk: The transplant compatibility detection agent 51 continuously assesses compatibility (rejection risk) among numerous recipient-potential donors based on this detailed data (such as HLA type). The AI ​​prediction results can be used as training data to identify new biomarkers for rejection and genes involved in rejection.

[0338] Foundation for in silico trials: By integrating with predictive models of potential recipients (e.g., cohort conversion + multistate survival analysis), the efficacy and long-term side effects of immunosuppressants under development can be predicted and evaluated in advance of clinical trials, based on simulations (in silico trials), to see how they will affect specific patient groups.

[0339] (Privacy protection compliance assessment using zero-knowledge proof) The transplant suitability detection unit 50 may be configured to determine suitability without disclosing specific personal information or raw data of the recipient and potential donor to each other, using zero-knowledge proof (ZKP) technology. Specifically, the suitability detection agent 51 receives only cryptographic proof (Proof) generated from attribute information such as the potential donor's HLA type and blood type, and verifies the validity of the proof, thereby confirming only the fact that the donor "suits the transplant conditions" without decrypting the actual medical data. This makes it possible to achieve both a high level of privacy protection and rapid suitability detection.

[0340] (Autonomous decision mode using edge AI) The caller agent 201 of the emergency call device 200 may have an autonomous determination mode that functions when the communication network 400 is shut down due to a large-scale disaster or the like. In this autonomous determination mode, the caller agent 201 uses a lightweight inference model (edge ​​AI) that is completed within the device to autonomously detect the transition of a potential donor to a critical condition from the acquired biometric data. The detected critical condition and the time of detection are logged and temporarily stored in an immutable area (secure element) within the device, and are preferentially transmitted to the emergency call center device 100 when communication is restored. This prevents the loss of donor occurrence information during periods when the communication infrastructure is down, and enhances the robustness of the life-saving system.

[0341] (Application of Dynamic NFTs and Non-Transferable Tokens) In the sixth embodiment, the organ NFT generated by the identifier generation unit 82 may be implemented as a dynamic NFT (dNFT) in which metadata is automatically updated according to changes in the organ's state, such as temperature and elapsed time, obtained from an external oracle via a smart contract. Furthermore, it is desirable that this organ NFT be designed as a SoulBound Token (SBT) that cannot be transferred between wallets. This makes it possible to reflect changes in the organ's state in the digital identifier in real time and to prevent the resale or transfer of organs, which would be ethically unacceptable, at the system architecture level.

[0342] (Safety device using a physical shutoff mechanism) The output path of the donor selection signal from the donor selection unit 70 may be equipped with a physical blocking mechanism (hardware kill switch) that can only be deactivated by a human operator or authorized medical professional through physical operation (such as operating a hardware switch with a physical key or biometric authentication). With this configuration, even if an AI agent such as the donor selection agent 71 malfunctions or is hacked and makes an incorrect donor selection decision, as long as the physical blocking mechanism is deactivated, the final actions on the real world, such as dispatching an organ retrieval team or issuing medical intervention orders, will be prevented. This ensures the ultimate safety of a system that involves human lives at the hardware level.

[0343] The process described in the above embodiment can be realized by executing a pre-prepared program on a computer. This program is, for example, stored on a computer-readable storage medium and executed by being read from the storage medium. Alternatively, this program may be provided in the form of a non-volatile (non-transient) storage medium such as flash memory, or it may be provided via a network such as the Internet.

[0344] 9. Summary This specification discloses at least the following configuration: (1) A life-saving system that provides organs from a donor to a recipient, The recipient information management unit manages the recipient information, The Potential Donor Information Management Department manages information on potential donors, A transplant condition suitability detection unit detects a potential donor who is suitable for the transplant conditions of each recipient, based on the recipient information and the potential donor information. A health status monitoring unit that monitors the health status of each potential donor, A life-saving system comprising a donor selection unit that selects a potential donor who meets the transplant conditions and is in a predetermined state of health as the donor. (1) Action and effect By automating a series of processes—continuously monitoring the health status of potential donors who have completed suitability assessments and selecting a donor the moment they reach a predetermined critical condition—the time until organ donation can be initiated can be dramatically reduced compared to the traditional process involving human judgment. This will make life-saving medical treatment through organ transplantation more efficient.

[0345] (2) A life-saving system that provides organs from a donor to a recipient, A potential recipient prediction unit that predicts potential recipients, A potential recipient information management unit manages information on the aforementioned potential recipients, The Potential Donor Information Management Department manages information on potential donors, A transplant condition suitability detection unit detects a potential donor that is suitable for the transplant conditions of each potential recipient, based on the information of the potential recipient and the information of the potential donor. A health status monitoring unit that monitors the health status of each potential donor, A life-saving system comprising a donor selection unit that selects a potential donor who meets the transplant conditions and is in a predetermined state of health as the donor. (2) Action and effect By predicting potential recipients in advance and continuously evaluating and detecting compatibility with potential donors based on that information, it becomes possible to develop an optimal matching strategy that takes into account future organ demand (patients who are likely to require transplants). Even if there are currently no suitable recipients, it becomes possible to transition to an "on-demand donation process" that preserves organs long-term for future patients, maximizing the opportunity for organ donation. These features enable efficient organ allocation that considers not only current but also future demand, and dramatically shorten the time to donation by selecting a donor the moment a patient reaches a critical condition, thereby increasing the efficiency of life-saving medical care through organ transplantation.

[0346] (3) The life-saving system according to (1), wherein the recipient information management unit and the potential donor information management unit are configured as management units that use a single integrated database, and the transplant condition match detection unit accesses the single integrated database to detect potential donors that match the transplant conditions.

[0347] (3) Action and effect By managing recipient and potential donor information in a single, integrated database, and having the transplant suitability detection unit access it, data duplication and inconsistencies are prevented compared to using separate databases for two different management units (recipient information management unit and potential donor information management unit). This simplifies the system and improves operational efficiency through centralized information management. Furthermore, the transplant suitability detection unit can obtain the necessary information (recipient information, potential donor information) simply by accessing a single database, thus accelerating the suitability detection process. By improving the operational efficiency of the system and accelerating the suitability detection process, the effect of (1), "increased efficiency in life-saving medical care through organ transplantation," is enhanced from a data management perspective.

[0348] (4) The life-saving system according to claim 1, wherein the potential donor information management unit periodically updates information through data linkage with a personal AI agent via an emergency call device associated with the potential donor.

[0349] (4) Actions and effects By regularly updating information through data integration with a personal AI agent, the following effects can be obtained. Because potential donor information is kept up-to-date in near real-time via emergency call devices, based on individual consent, the obsolescence of donor information is suppressed. Because the transplant suitability detection unit 50 and the health status monitoring unit 60 can always perform processing based on the latest and most accurate information, the accuracy of the suitability assessment with recipients is improved, and the reliability of donor selection is enhanced. By improving the freshness and accuracy of information, the effect of (1), "increased efficiency in life-saving medical care through organ transplantation," is strengthened from the perspective of data accuracy.

[0350] (5) The life-saving system according to (1), wherein the health status monitoring unit evaluates whether the potential donor is in the predetermined health state based on biometric data (heart rate, respiratory irregularities, blood pressure, etc.) provided from an emergency call device associated with the potential donor.

[0351] (5) Actions and effects By having monitoring and evaluation functions based on objective biological data, the following effects can be obtained. Because the health status of potential donors can be evaluated based on objective and near real-time information such as biometric data provided by emergency call devices, the reliability of the selection decision made by the donor selection unit (70) is increased. Because serious health conditions can be detected early and accurately, it becomes possible to decisively accelerate donor selection and the subsequent initiation of the organ donation process. Accurate and rapid assessment of health status contributes to speeding up the success rate of organ transplants by promptly providing recipients with suitable organs in a transplantable condition, thereby improving the feasibility of "highly efficient life-saving medical care through organ transplantation."

[0352] (6) The life-saving system according to claim 1, further comprising an emergency call center device and an emergency call device associated with the potential donor, wherein the emergency call center device has an automatic conversation unit that communicates with the emergency call device in high-speed conversation mode, the high-speed conversation mode being a mode in which a natural language voice call is completed in a shorter time than usual.

[0353] (6) Actions and Effects The following benefits can be obtained by having a call function with a high-speed conversation mode. By using the high-speed conversation mode, emergency calls can be received in a short time, and emergency call information can be quickly notified to designated emergency response devices. Even when automated notifications increase during large-scale disasters, the high-speed processing provided by the highly capable automated conversation unit helps to suppress the decrease in response speed to emergency calls compared to conventional systems. Because the time required for information gathering and transmission during emergencies is shortened, the time from donor selection to the start of the organ donation process is also shortened, and the effect of (1), "increased efficiency of life-saving medical care through organ transplantation," is strengthened from a communication perspective.

[0354] (7) The life-saving system according to (2), wherein the potential recipient information management unit and the potential donor information management unit are configured as management units that use a single integrated database, and the transplant condition match detection unit accesses the single integrated database to detect potential donors that match the transplant conditions.

[0355] (7) Actions and Effects By managing potential recipient information and potential donor information in a single integrated database, and having the transplant suitability detection unit access it, the following effects can be obtained. By allowing centralized management of two different types of information (demand forecasts and supply sources), data duplication and inconsistencies are prevented, simplifying and streamlining information management. Because the transplant suitability detection unit can quickly acquire all the necessary data (potential recipients and potential donors) from a single location, the suitability assessment (predictive matching) process, which takes future demand into account, becomes more efficient and faster. By improving the efficiency of data management and detection, the effect of (2), "increased efficiency in life-saving medical care through organ transplantation," is enhanced from the perspective of data linkage and processing.

[0356] (8) The life-saving system according to (2), wherein the potential recipient prediction unit predicts the potential recipient by combining one or more of a hazard model, a Markov model, and personalized machine learning.

[0357] (8) Actions and effects The potential recipient prediction unit predicts potential recipients not through simple statistical processing, but by combining one or more established advanced statistical and computational methods such as hazard models, Markov models, and personalized machine learning, resulting in the following effects: By utilizing multiple advanced predictive models (hazard models, Markov models, personalized machine learning, etc.), it is possible to predict with greater accuracy the likelihood that a group of individuals not currently on the waiting list will be added to the waiting list within a certain period in the future. Improved predictive accuracy will be useful for long-term clinical resource allocation for organs, hospital-level capacity planning, and designing early intervention strategies. Because future demand (potential recipients) can be grasped more accurately, it becomes possible to plan optimal matching and organ preservation strategies accordingly, and the effect of (2), "increased efficiency of life-saving medical care through organ transplantation," is strengthened from the perspective of demand forecasting.

[0358] (9) The life-saving system according to (2), further comprising an organ preservation device for preserving organs extracted from the donor in a manner that allows them to be restored to a transplantable state. The organ preservation device preserves the organs by, for example, freezing, supercooling, or vitrification.

[0359] (9) Actions and Effects By employing organ preservation devices and advanced preservation methods, the following effects can be achieved. Organs removed from a donor can be preserved in a way that allows them to be restored to a state suitable for transplantation. This allows organs to be preserved without waste, especially when a suitable recipient is unavailable or the ideal recipient is a potential recipient (future patient), and can be stored until a predicted recipient is placed on a waiting list, maximizing the opportunity for organ donation. Sub-zero freezing and supercooling prevent physical damage to cells and tissues by ice crystals, while vitrification is suitable for long-term storage and suppresses organ damage caused by ice crystals and cracks. This contributes to maintaining the quality and preserving the function of extracted organs. The strategic option of preserving organs to meet predicted future demand (potential recipients) enhances the effect of (2), "increased efficiency in life-saving medical care through organ transplantation," from a time-axis perspective.

[0360] (10) The life-saving system according to claim 2, wherein the information managed by the potential recipient information management unit includes hypothetical information based on the predictions of the potential recipient prediction unit.

[0361] (10) Actions and Effects The following effects can be obtained by managing and utilizing hypothetical information (information that estimates future possibilities). By managing and utilizing not only current, reliable information but also predictive information (hypothetical information) about future patients, it becomes possible to anticipate organ demand and develop long-term strategies for organ allocation and preservation (on-demand provision process). This allows for decisions to preserve and conserve organs for future suitable patients (potential recipients) who are not yet on the waiting list, ensuring that organ resources are utilized to their fullest potential without missing opportunities for donation. By enabling efficient matching strategies that look to the future, the effect of (2), "increased efficiency in life-saving medical care through organ transplantation," is strengthened in terms of both the time axis and strategic use.

[0362] (11) The life-saving system described in (8) is capable of using a hazard model to estimate the probability of transitions between multiple states in a patient population, from a healthy state to chronic disease, then to severe illness, and finally to registration on the transplant waiting list.

[0363] (11) Actions and Effects By having the ability to estimate the transition probabilities of multiple states using a hazard model, the following effects can be obtained. Hazard models that capture the time axis of disease progression in detail make it possible to estimate the cumulative number of patients who will be registered on the transplant waiting list over a certain period in the future, based on how they progress through each stage of the disease. This improves the accuracy of potential recipient predictions, enabling more reliable forecasts of future organ demand. Highly accurate predictions form the basis for optimal matching strategies, such as long-term clinical resource allocation and organ preservation (on-demand donation process) that take future demand into account. Therefore, the effect of (2), "increased efficiency in life-saving medical care through organ transplantation," is further enhanced from the perspective of the quality of the predictive model.

[0364] (12) The life-saving system according to (8), wherein the potential recipient prediction unit can estimate individuals or groups of individuals (future eligible recipient pool) who will be eligible for organ transplantation in the future by using a Markov chain that reflects the suitability criteria and transplant age for each organ.

[0365] (12) Actions and Effects By incorporating organ-specific prediction capabilities using Markov chains, the following effects can be obtained. Because transplant suitability varies significantly from organ to organ, and these differences can be addressed by modeling them separately, it becomes possible to estimate future eligible donor pools more accurately, reflecting the specialized and individual circumstances of each organ. Based on these highly accurate organ-specific predictions, optimal resource allocation and preservation strategies can be formulated for each organ (e.g., long-term preservation for kidneys, emergency response for hearts, etc.). Improving the quality of organ-specific demand forecasts will reduce the mismatch between organ supply and demand, and as a result, the effect of (2), "increased efficiency in life-saving medical care through organ transplantation," will be further strengthened from the perspective of organ-specific forecasting.

[0366] (13) The life-saving system according to claim 8, wherein the potential recipient prediction unit can predict the rapid progression of the disease and suitability for transplantation in the short to medium term of the potential recipient by personalized machine learning using time-series features of electronic health records.

[0367] (13) Actions and Effects By incorporating a prediction function using personalized machine learning based on the time-series features of EHRs, the following effects can be obtained. By using not only historical population data but also specific time-series data from individual patients' EHRs, it becomes possible to detect signals of rapid disease progression in the short to medium term, improving the accuracy of disease progression risk prediction at the individual level. Because it can predict the rapid progression of the disease in the short term at an early stage, it can be integrated with an operational flow that automatically detects patients who have exceeded the risk threshold and encourages early referral, thereby optimizing the allocation of clinical resources and early intervention strategies. By improving prediction accuracy and promoting early intervention, it becomes possible to more accurately determine the timing of transplantation for potential patients, further strengthening the effect of (2), "increased efficiency in life-saving medical care through organ transplantation," from the perspective of individual prediction.

[0368] (14) A life-saving method that involves providing organs from a donor to a recipient. A life-saving method characterized by performing the following steps on a cloud system that communicates with multiple terminal devices, including an emergency call device and an emergency responder device, via a communication network. A recipient information management step that manages information about recipients and / or potential recipients. Potential donor information management step: managing information about potential donors. A match detection step that continuously detects potential donors who meet the transplantation criteria based on the recipient information and / or potential recipient information and the potential donor information. A health monitoring step that continuously monitors the health status of the potential donor. A donor selection step in which a potential donor who meets the transplantation conditions and is in a predetermined state of health is selected as the donor.

[0369] (14) Actions and Effects By executing each of the above steps as a series of actions on a coordinating cloud system, the following effects can be achieved. By continuously detecting suitable candidates for transplantation and monitoring their health status, and selecting a donor the moment they meet the required health criteria, the time from the opportunity for organ donation to the start of the process can be significantly reduced. This rapid process makes life-saving medical treatment through organ transplantation more efficient. Because it runs on a cloud system that connects with emergency call devices and emergency responder devices via a communication network, it is less susceptible to geographical constraints and can provide stable life-saving services over a wide area. By managing and detecting compatibility based on information about both current and potential recipients, it becomes possible to develop an optimal matching strategy that considers not only current but also future organ demand, contributing to the maximum utilization of organ resources.

[0370] (15) The life-saving method according to (14), further comprising, after the donor selection step, an organ preservation step of preserving the organs removed from the donor in a transplantable state until a suitable recipient becomes available.

[0371] (15) Actions and Effects Adding the organ preservation step provides the following benefits: Even if a suitable recipient is unavailable at the time of donor selection, or if the most suitable recipient is a "potential recipient" (a future patient), it will still be possible to preserve the organ in the long term. This will maximize organ donation opportunities without missing any, enable an optimal matching strategy that considers not only current but also future organ demand, and contribute to the maximum utilization of organ resources. The addition of this process of preserving organs in a transplantable state (on-demand donation process) enhances the effect of (14), "increased efficiency in life-saving medical care through organ transplantation," from the perspective of effective utilization of resources beyond the constraints of the time axis.

[0372] (16) A life-saving method using a multi-AI agent system for providing donor organs to recipients, A life-saving method characterized in that an agent included in a multi-AI agent system performs the following steps. A potential recipient prediction step in which a potential recipient prediction agent predicts a potential recipient. A potential recipient information management step in which a potential recipient information management agent manages information about the potential recipient. Potential donor information management step: A potential donor information management agent manages information about potential donors. A match detection step in which a match detection agent detects a potential donor who meets the transplantation conditions for each potential recipient, based on the information of the potential recipient and the information of the potential donor. A health status monitoring step in which a health status monitoring agent monitors the health status of each potential donor. A donor selection step in which a donor selection agent selects a potential donor who meets the transplantation conditions and has achieved a predetermined health condition as a donor, according to the health monitoring agent.

[0373] (16) Actions and Effects Each of the above steps is automatically and seamlessly executed by a multi-AI agent system, thereby increasing the efficiency of life-saving medical care through organ transplantation. More specifically, AI agents automatically and continuously perform compatibility assessments and health monitoring, selecting donors without waiting for human judgment. Compared to conventional processes, this dramatically speeds up the entire organ donation process and can improve the success rate of organ transplants. By predicting potential recipients and assessing suitability for future patients even when no recipients are currently available, organ donation opportunities can be maximized.

[0374] (17) The life-saving method according to claim 16, wherein the health status monitoring step is a step of monitoring the health status of each potential donor based on an automated notification from a caller agent working on a device capable of acquiring information (personal information, medical information) of the potential donor.

[0375] (17) Actions and Effects By monitoring the health status of each potential donor based on automated reports from whistleblower agents, the following benefits can be obtained: Because the device detects abnormalities in the potential donor's health and automatically notifies the whistleblower agent without human intervention, signs of a transition to a serious health condition can be detected very early and quickly, initiating a monitoring process. By starting the monitoring step earlier, the determination of whether the required health condition (donor selection criteria) has been reached is also expedited, and as a result, the effect of (16), "dramatically expediting the entire organ donation process and making life-saving medical care through organ transplantation more efficient," is strengthened.

[0376] (18) The life-saving method according to (17), further comprising an emergency response request information transmission step in which an operator agent transmits emergency response request information, including information on selected donors and suitable recipients and / or potential recipients, to an emergency response agent working on a device associated with the emergency responder, based on a call with the caller agent.

[0377] (18) Actions and Effects Having an emergency response request information transmission step provides the following benefits: After donor selection, the operator agent, without the need for human operators, immediately transmits emergency response request information to the emergency response agent based on information obtained through high-speed AI-to-AI communication with the caller agent. This allows preparations for organ retrieval, preservation, and transport to begin quickly and efficiently, simultaneously with donor selection. Because emergency response request information includes information on selected donors and compatible recipients, emergency responders can already identify the most suitable transplant recipients before heading to the scene, improving the accuracy and efficiency of the response. The collaboration and high-speed communication between AI agents dramatically shortens the time from the discovery of a donor to the initial response of the specialized team, thereby further enhancing the effect of (16), which is to "dramatically expedite the entire organ donation process and make life-saving medical care through organ transplantation more efficient."

[0378] (19) A computer program for implementing one or more computers the life-saving system described in any of (1) to (13).

[0379] (19) Actions and Effects By running this computer program on one or more computers, any of the life-saving systems described in (1) through (13) can be realized. More specifically, each of the functional units described in (1) to (13), such as the recipient information management unit, the potential donor information management unit, the transplant suitability detection unit, the health status monitoring unit, and the donor selection unit, can be implemented and realized as software running on one or more computers (hardware). By running this program, a series of automated and continuous management and selection steps in the organ donation process (such as information management, matching, health monitoring, and donor selection) can be performed by a computer. This makes it possible to realize on a computer all the effects achieved by the life-saving systems (1) through (13), namely, "high efficiency of life-saving medical care through organ transplantation," "minimization of ischemic time and improvement of survival rates," and "maximum utilization of organ resources and response to future demand," which are advanced life-saving processes. This program (software) serves as the foundation for each AI agent function (operator agent, caller agent, etc.) of the life-saving system as a multi-AI agent system, contributing to the realization of a highly efficient life-saving medical process through AI collaboration.

[0380] (50) A life-saving system that provides organs from a donor to a recipient, The recipient information management unit manages the recipient information, The Potential Donor Information Management Department manages information on potential donors, A transplant suitability detection unit detects suitability based on the recipient information and the potential donor information, A health status monitoring unit that monitors the health status of the potential donor, A donor selection unit that selects the potential donor as the donor based on the aforementioned suitability and health status, A life-saving system characterized by having a blockchain recording unit for recording, in an immutable format, the fact of donor selection by the donor selection unit and the information that forms the basis for the objective judgment.

[0381] (50) Effects By recording the facts and criteria for donor selection (suitability, serious health conditions) on an immutable blockchain, it is possible to contribute to ensuring ethical objectivity and improving social trust in the rapid AI-driven donor selection process.

[0382] (51) The Potential Donor Information Management Department manages information on potential donors, An identifier generation unit generates a unique digital identifier, including a non-fungible token (NFT), for each type of organ provided by the aforementioned potential donor, and registers it on the blockchain. A history recording unit records the results of the compatibility detection, the results of the donor selection, and the entire physical and medical history from the extraction of the organ to its provision to the recipient, linked to the digital identifier and recorded on the blockchain. A life-saving system characterized by having the following features.

[0383] (51) Effects By generating a unique digital identifier, including NFTs, for each organ type and linking and recording the entire physical and medical history from extraction to donation, ultimate traceability in the organ donation process can be achieved, eliminating the risk of data tampering and dramatically improving organ quality control and transparency.

[0384] (52) An organ preservation unit that preserves organs extracted from donors in a way that allows them to be restored to a transplantable state, A preservation treatment data recording unit records preservation treatment data, including the history of temperature, pressure, or chemical solution application during long-term preservation treatment (e.g., vitrification) of organs by the organ preservation unit, in the blockchain, linked to a digital identifier assigned to the organ. A life-saving system characterized by having a system that allows a third party to verify the quality maintenance status of the organs based on the recorded data. (52) Effects By recording and verifying detailed data such as temperature and chemical solutions used in long-term organ preservation procedures on a blockchain, it becomes possible to objectively prove the quality assurance of long-term preserved organs. This will increase the reliability of transplant decisions for future recipients and allow for the management of the risks of long-term organ preservation in the ongoing donation process.

[0385] (53) A computer program for implementing one or more computers the life-saving system described in any of (50) to (52).

[0386] (53) Effects By having one or more computers run this computer program, any of the life-saving systems described in (50) to (52) can be realized. More specifically, each of the functional units described in (50) to (52), such as the recipient information management unit, the potential donor information management unit, the transplant suitability detection unit, the health status monitoring unit, and the donor selection unit, can be implemented and realized as software running on one or more computers (hardware). By running this program, a series of automated and continuous management and selection steps in the organ donation process (such as information management, matching, health monitoring, and donor selection) can be performed by a computer. This makes it possible to realize on a computer a life-saving process with advanced functions, such as the effects achieved by any of the life-saving systems described in (50) to (52), namely "high efficiency of life-saving medical care through organ transplantation," "minimization of ischemic time and improvement of survival rates," and "maximum utilization of organ resources and response to future demand." In particular, the application of blockchain technology ensures transparency, objectivity, and tamper-proof traceability of data throughout the entire organ donation process. This program (software) serves as the foundation for each AI agent function (operator agent, caller agent, etc.) of the life-saving system as a multi-AI agent system, contributing to the realization of a highly efficient life-saving medical process through AI collaboration.

[0387] (103) A multi-agent system having at least one first agent (operator agent) and multiple second agents (informant agents), The first agent said, Call processing for communicating with the second agent in high-speed conversation mode or normal speed conversation mode, Emergency call reception processing that receives emergency calls from the aforementioned second agent, The AI ​​agent performs predetermined processing, including, if the emergency call is an automated emergency call, high-speed conversation processing, which involves communicating with the second agent that made the automated emergency call in high-speed conversation mode, The second agent, The automatic emergency call process that makes the aforementioned automatic emergency call, A life-saving system comprising an AI agent that performs predetermined processing, including high-speed conversation processing, which involves communicating with the first agent in the high-speed conversation mode.

[0388] According to the life-saving system in (103), if the emergency call from the second agent is an automated emergency call, the first agent can communicate with the second agent who made the automated emergency call in high-speed conversation mode. By communicating in high-speed conversation mode, it is possible to receive emergency calls in a shorter time compared to communicating in normal mode.

[0389] Therefore, the life-saving system of (103) can suppress the decrease in the response speed to emergency calls when the number of automatic emergency calls increases.

[0390] (104) A multi-agent system having a meta-agent (operator agent), The aforementioned meta-agent is Behavior monitoring process that monitors the behavior of other agents, A life-saving system of (103), which is an AI agent that performs a predetermined process including an intervention process that intervenes in the other agent that has detected a predetermined behavior and suppresses said predetermined behavior.

[0391] According to the life-saving system in (104), the safety of the life-saving system, which is a multi-agent system, is improved.

[0392] (105) A life-saving system comprising an emergency call center device, an emergency call device, and an emergency responder device, The aforementioned emergency call center device has an operator agent, The emergency call device has a caller agent, The aforementioned operator agent, Call processing that involves communicating with the aforementioned caller agent in either high-speed conversation mode or normal conversation mode, An emergency call reception process that receives an emergency call from the aforementioned emergency call device, An automatic emergency call determination process that determines whether the emergency call from the emergency call device is an automatic emergency call or a manual emergency call, If the emergency call is an automated emergency call, a high-speed conversation process is performed to communicate with the caller agent of the emergency call device that made the automated emergency call in high-speed conversation mode, An AI agent that performs a predetermined process, which includes an emergency response request information transmission process that transmits emergency response request information to a predetermined emergency responder device based on a call in high-speed conversation mode with the caller agent of the emergency call device, The aforementioned whistleblower agent, The automatic emergency call process that makes the aforementioned automatic emergency call, A life-saving system comprising an AI agent that performs predetermined processing, including high-speed conversation processing, which involves communicating with the operator agent in the high-speed conversation mode.

[0393] The life-saving system of (105) allows the operator agent of the emergency call center device and the caller agent of the emergency call device to communicate in high-speed conversation mode when the emergency call is an automatic emergency call. Based on the communication with the caller agent in high-speed conversation mode, the operator agent can transmit emergency response request information to a designated emergency responder device. By having the operator agent and the caller agent communicate in high-speed conversation mode, the emergency call can be received in a shorter time compared to when communicating in normal conversation mode, and thus emergency call information can be notified to the designated emergency responder device in a shorter time.

[0394] Therefore, the life-saving system of (105) can suppress the decrease in the response speed to emergency calls when the number of automatic emergency calls increases.

[0395] (106) The emergency call device has an emergency response agent, The aforementioned emergency response agent, An emergency response request information reception process that receives the aforementioned emergency response request information, A life-saving system of (105), which is an AI agent that performs a predetermined process including an emergency response request content presentation process that presents the content of the emergency response request information to the user of the emergency notification device.

[0396] According to (106), an emergency response agent can receive emergency response request information and present the contents of the emergency response request information to the user of the emergency call device. The user of the emergency call device can take emergency action based on the contents of the emergency response request information presented.

[0397] (107) The emergency call device is at least one device from among a car device, a mobile device, and a wearable device. The emergency responder device is at least one device from among an emergency command system device, an ambulance device, a fire engine device, and a police vehicle device, in the life-saving system of (101), (105), or (106).

[0398] According to (107), when an emergency call is an automated emergency call, the emergency call center device can communicate with at least one of the car device, mobile device, and wearable device that made the automated emergency call in high-speed conversation mode. Based on the conversation in high-speed conversation mode, the emergency call center device can notify at least one predetermined device from among the emergency command system device, ambulance device, fire engine device, and police vehicle device of the emergency call information. By communicating in high-speed conversation mode, emergency calls can be received in a shorter time compared to communicating in normal mode, making it possible to notify at least one predetermined device from among the ambulance device, fire engine device, and police vehicle device of the emergency call information in a shorter time.

[0399] Therefore, the life-saving system of (107) can suppress the decrease in the response speed to emergency calls when the number of automatic emergency calls increases.

[0400] (108) The emergency call device and the emergency responder device are at least one device selected from a car device, a mobile device, and a wearable device, in a life-saving system of (105) or (106).

[0401] According to (108), when an emergency call is an automated emergency call, the emergency call center device can communicate with at least one of the car devices, mobile devices, and wearable devices that made the automated emergency call in high-speed conversation mode. Based on the conversation in high-speed conversation mode, the emergency call center device can notify at least one of the car devices, mobile devices, and wearable devices of emergency response request information. By communicating in high-speed conversation mode, emergency calls can be received in a shorter time compared to communicating in normal mode, making it possible to notify at least one of the car devices, mobile devices, and wearable devices of emergency response request information in a shorter time.

[0402] Therefore, the life-saving system of (108) can suppress the decrease in the response speed to emergency calls when the number of automatic emergency calls increases.

[0403] (109) The life-saving system according to claim (105) or (106), wherein the predetermined emergency responder device is an emergency responder device of a user who is registered as having the skills necessary for emergency response.

[0404] According to (109), when an emergency call is made, emergency response request information can be sent to a designated emergency response device of a registered skilled worker user who is registered as having the skills required for emergency response.

[0405] Therefore, according to (109), it becomes possible to respond quickly to emergencies not only to those whose duties involve saving lives and rescue, such as firefighters and police officers, but also with the cooperation of a wide range of registered skilled users. As a result, the decrease in the speed of response to emergency calls when the number of automated emergency calls increases can be further suppressed.

[0406] (110) The aforementioned automatic emergency call determination process is: The life-saving system of (105), which includes a process for determining whether an emergency call from the emergency call device is an automatic emergency call or a manual emergency call based on the content of a call with the caller agent.

[0407] According to (110), it is possible to determine whether an emergency call from an emergency call device is an automatic or manual emergency call based on the content of the conversation with the caller agent, and to take appropriate action according to the determination result.

[0408] (111) The life-saving system of (101), (103), or (105), wherein the high-speed conversation mode is at least one of the following modes: rapid speech mode, abbreviation mode, and machine language mode.

[0409] According to (111), the life-saving system can communicate with the emergency call device that made the automatic emergency call in at least one of the following modes: rapid speech mode, abbreviation mode, and machine language mode, when the emergency call is an automatic emergency call.

[0410] (112) The aforementioned operator agent, The life-saving system of (105), which, when an emergency call from the emergency call device is a manual emergency call, performs a person-to-person conversation process to converse in normal conversation mode with the caller who made the manual emergency call using the emergency call device.

[0411] According to (112), if the emergency call is a manual emergency call, the operator agent can communicate with the caller who made the manual emergency call using the emergency call device via machine voice.

[0412] (113) The aforementioned operator agent, A life-saving system (112) that, based on the content of the conversation with the caller, executes a response instruction process to instruct the caller on how to deal with the emergency.

[0413] According to (113), the operator agent may, based on the content of the conversation with the caller, instruct the caller on how to deal with the emergency using an automated voice.

[0414] (114) The emergency call center device has a function that enables direct conversation between the caller and a human operator when the emergency call is a manual emergency call. The aforementioned emergency response device includes an emergency command system device for direct communication between the caller and the human operator. The aforementioned operator agent, The system performs a comprehension determination process to determine whether the informant understood the instructions. If it is determined that the caller does not understand the instructions, the life-saving system (113) executes a conversation method change process that enables direct conversation between the caller and the human operator.

[0415] According to (114), if the operator agent determines that the caller does not understand the machine-voiced instructions, it enables a direct conversation between the caller and the human operator. This allows a human operator at the emergency call center to provide instructions (explanations, persuasion) in human voice (the voice of the human operator) to callers (humans) who do not understand the machine-voiced instructions from the operator agent.

[0416] Therefore, according to (114), while suppressing the decrease in the response speed to emergency calls when automated calls increase, it is possible for the caller (person) and the human operator to communicate directly when the emergency call is a manual call.

[0417] (115) A multi-AI agent system comprising: an emergency call center device having a first agent; a plurality of emergency call devices having a second agent; a plurality of emergency responder devices having a third agent; and a meta-agent, The first agent said, Call processing for making a call with the aforementioned emergency call device in high-speed conversation mode or normal conversation mode, An emergency call reception process that receives an emergency call from the aforementioned emergency call device, An automatic emergency call determination process that determines whether the emergency call from the emergency call device is an automatic emergency call or a manual emergency call, If the emergency call is an automated emergency call, a high-speed conversation process is performed to communicate with the second agent of the emergency call device that made the automated emergency call in high-speed conversation mode, An AI agent that performs predetermined processing, including an emergency response request information transmission process that transmits emergency response request information to a predetermined emergency responder device based on a call in high-speed conversation mode with a second agent of the emergency call device, The second agent, The automatic emergency call process that makes the aforementioned automatic emergency call, An AI agent that performs predetermined processing including high-speed conversation processing that communicates with the first agent in the high-speed conversation mode, The aforementioned third agent, An emergency response request information reception process that receives the aforementioned emergency response request information, An AI agent that performs predetermined processing including an emergency response request presentation process that presents the contents of the emergency response request information to the user of the emergency notification device, The aforementioned meta-agent is A behavior monitoring process that monitors the behavior of at least one of the aforementioned AI agents, A life-saving system comprising an AI agent that performs a predetermined process, which includes an intervention process that intervenes in the AI ​​agent when a predetermined behavior is detected and suppresses said predetermined behavior.

[0418] The life-saving system of (115) allows a first agent of the emergency call center device and a second agent of the emergency call device to communicate in high-speed conversation mode when the emergency call is an automatic emergency call. Based on the communication with the second agent in high-speed conversation mode, the first agent can transmit emergency response request information to a designated emergency responder device. By having the first agent and the second agent communicate in high-speed conversation mode, the emergency call can be received in a shorter time compared to when communicating in normal conversation mode, and thus emergency call information can be notified to the designated emergency responder device in a shorter time.

[0419] Furthermore, the life-saving system of (115) has a meta-agent. The meta-agent monitors the behavior of the first agent, the second agent, and the third agent, and if it detects a predetermined behavior that could lead to a situation that reduces the safety of the life-saving system, it intervenes in that agent to suppress the predetermined behavior.

[0420] Therefore, the life-saving system of (115) can suppress the decrease in response speed to emergency calls when the number of automatic emergency calls increases. The life-saving system of (115) improves the safety of the life-saving system, which is a multi-agent system.

[0421] (1001) A multi-agent system having at least one first agent and a plurality of second agents, The first agent said, A call process for communicating with the aforementioned second agent, An AI agent that performs predetermined processing, including an emergency response request process that, based on a call with the second agent that made the automated emergency call, requests an emergency response from an agent other than the second agent that made the automated emergency call, The second agent, The automatic emergency call process that makes the aforementioned automatic emergency call, A life-saving system comprising an AI agent that performs predetermined processes, including a call process for communicating with the first agent.

[0422] According to the life-saving system of (1001), it is possible to receive an automated emergency call in a short time and request an emergency response through call processing between the first agent and the second agent. This can suppress the decrease in the speed of response to emergency calls when the number of automated emergency calls increases.

[0423] (1002) A multi-agent system having at least one first agent, a plurality of second agents, and a plurality of third agents, The first agent said, A call process for communicating with the aforementioned second agent, An AI agent that performs predetermined processes, including an emergency response request process that requests an emergency response from the third agent based on a call with the second agent that has made an automated emergency call, The second agent, The automatic emergency call process that makes the aforementioned automatic emergency call, An AI agent that performs predetermined processing including a call process that makes a call with the first agent, The aforementioned third agent, The emergency request receiving process for receiving the aforementioned emergency response request, A life-saving system, which is an AI agent that performs predetermined processes, including emergency response-related processing based on the aforementioned emergency response request.

[0424] According to the life-saving system of (1002), it is possible to receive an automated emergency call in a short time through communication processing between the first agent and the second agent, and for the third agent to perform emergency response-related processing. This can suppress the decrease in the speed of responding to emergency calls when the number of automated emergency calls increases.

[0425] (1003) A multi-agent system having a meta-agent, The aforementioned meta-agent is Behavior monitoring process that monitors the behavior of other agents, A life-saving system of (01) or (02), which is an AI agent that performs a predetermined process including an intervention process that intervenes in the other agent that has detected a predetermined behavior and suppresses said predetermined behavior.

[0426] According to the life-saving system of (1003), the safety of the life-saving system, which is a multi-agent system, is improved.

[0427] (116) A computer program for implementing the first agent in the life-saving system described in (103), (115), (1001), or (1002) using one or more computers. According to the computer program of (116), by executing it on one or more computers, the first agent in the life-saving system described in (103), (115), (1001), or (1002) can be realized by one or more computers.

[0428] (117) A computer program for implementing the second agent in the life-saving system described in (103), (115), (1001), or (1002) using one or more computers. According to the computer program of (117), by executing it on one or more computers, the second agent in the life-saving system described in (103), (115), (1001), or (1002) can be realized on one or more computers.

[0429] (118) A computer program for implementing the third agent in the life-saving system described in (115) or (1002) using one or more computers. According to the computer program of (118), by executing it on one or more computers, the third agent in the life-saving system described in (115) or (1002) can be realized on one or more computers.

[0430] (119) A computer program for implementing the meta-agent in the life-saving system described in (104), (115), or (1003) using one or more computers. According to the computer program of (119), by executing it on one or more computers, the meta-agent in the life-saving system described in (104), (115), or (1003) can be realized on one or more computers. [Explanation of symbols]

[0431] 1. Life-saving system 10. Recipient Information Management Department 11. Recipient Information Management Agent 20 Potential Recipient Prediction Unit 21 Potential Recipient Prediction Agent 30 Potential Recipient Information Management Department 31 Potential Recipient Information Management Agent 40 Potential Donor Information Management Department 41 Potential Donor Information Management Agent 50 Transplant Condition Compatibility Detection Unit 51 Competent Detection Agent 60 Health Status Monitoring Department 61 Health Monitoring Agents 70 Donor Selection Department 71 Donor Selection Agents 100 Emergency Call Center Device 101 Operator Agent (Meta-Agent) 200 Emergency Call Devices 201 Informant Agent 300 Emergency Response Devices 301 Emergency Response Agent 500 Emergency Response Devices 600 Organ Donation Management Device

Claims

1. A life-saving system that provides organs from a donor to a recipient, The recipient information management unit manages the recipient information, The Potential Donor Information Management Department manages information on potential donors, A transplant condition suitability detection unit detects a potential donor who is suitable for the transplant conditions of each recipient, based on the recipient information and the potential donor information. A health status monitoring unit that monitors the health status of each potential donor, A life-saving system comprising a donor selection unit that selects a potential donor who meets the transplant conditions and is in a predetermined state of health as the donor.

2. A life-saving system that provides organs from a donor to a recipient, A potential recipient prediction unit that predicts potential recipients, A potential recipient information management unit manages information on the aforementioned potential recipients, The Potential Donor Information Management Department manages information on potential donors, A transplant condition suitability detection unit detects a potential donor that is suitable for the transplant conditions of each potential recipient, based on the information of the potential recipient and the information of the potential donor. A health status monitoring unit that monitors the health status of each potential donor, A life-saving system comprising a donor selection unit that selects a potential donor who meets the transplant conditions and is in a predetermined state of health as the donor.

3. The life-saving system according to claim 1, wherein the recipient information management unit and the potential donor information management unit are configured as management units that use a single integrated database, and the transplant condition match detection unit accesses the single integrated database to detect potential donors that match the transplant conditions.

4. The life-saving system according to claim 1, wherein the potential donor information management unit periodically updates information through data linkage with a personal AI agent via an emergency call device associated with the potential donor.

5. The life-saving system according to claim 1, wherein the health status monitoring unit evaluates whether the potential donor is in the predetermined health state based on biometric data provided from an emergency notification device associated with the potential donor.

6. The life-saving system according to claim 1, further comprising an emergency call center device and an emergency call device associated with the potential donor, wherein the emergency call center device has an automatic conversation unit that communicates with the emergency call device in high-speed conversation mode, the high-speed conversation mode being a mode in which a natural language voice call is completed in a shorter time than usual.

7. The life-saving system according to claim 2, wherein the potential recipient information management unit and the potential donor information management unit are configured as management units that use a single integrated database, and the transplant condition match detection unit accesses the single integrated database to detect potential donors that match the transplant conditions.

8. The life-saving system according to claim 2, wherein the potential recipient prediction unit predicts the potential recipient by combining one or more of a hazard model, a Markov model, and personalized machine learning.

9. The life-saving system according to claim 2, further comprising an organ preservation device for preserving organs extracted from the donor in a manner that allows them to be restored to a transplantable state.

10. The life-saving system according to claim 2, wherein the information managed by the potential recipient information management unit includes hypothetical information based on the predictions of the potential recipient prediction unit.

11. The life-saving system according to claim 8, wherein the potential recipient prediction unit can estimate the probability of transitions between multiple states in a patient population, from a healthy state to chronic disease, then to severe illness, and finally to registration on the transplant waiting list, using a hazard model.

12. The life-saving system according to claim 8, wherein the potential recipient prediction unit can estimate individuals or groups of individuals who will be eligible for organ transplantation in the future by using a Markov chain that reflects the suitability criteria and transplant age for each organ.

13. The life-saving system according to claim 8, wherein the potential recipient prediction unit can predict the rapid progression of the disease and suitability for transplantation in the short to medium term of the potential recipient by personalized machine learning using time-series features of electronic health records.

14. A life-saving method that involves providing organs from a donor to a recipient. A life-saving method characterized by performing the following steps on a cloud system that communicates with multiple terminal devices, including an emergency call device and an emergency responder device, via a communication network. A recipient information management step for managing recipient information and / or potential recipient information. Potential donor information management step: managing information about potential donors. A match detection step that continuously detects potential donors who meet the transplantation conditions based on the recipient information and / or potential recipient information and the potential donor information. A health monitoring step that continuously monitors the health status of the potential donor. A donor selection step in which a potential donor who meets the transplantation conditions and is in a predetermined state of health is selected as the donor.

15. The life-saving method according to claim 14, further comprising, after the donor selection step, an organ preservation step of preserving the organs removed from the donor in a transplantable state until a suitable recipient becomes available.

16. A life-saving method using a multi-AI agent system for providing organs from a donor to a recipient, A life-saving method characterized in that an agent included in a multi-AI agent system performs the following steps. A potential recipient prediction step in which a potential recipient prediction agent predicts a potential recipient. A potential recipient information management step in which a potential recipient information management agent manages information about the potential recipient. Potential donor information management step: A potential donor information management agent manages information about potential donors. A match detection step in which a match detection agent detects a potential donor who meets the transplantation conditions for each potential recipient, based on the information of the potential recipient and the information of the potential donor. A health status monitoring step in which a health status monitoring agent monitors the health status of each potential donor. A donor selection step in which a donor selection agent selects a potential donor who meets the transplantation conditions and has achieved a predetermined health condition as a donor, according to the health monitoring agent.

17. The life-saving method according to claim 16, wherein the health status monitoring step includes monitoring the health status of each potential donor based on an automated notification from a caller agent working with a device capable of acquiring information on the potential donor.

18. The life-saving method according to claim 17, further comprising an emergency response request information transmission step in which an operator agent transmits emergency response request information, including information on selected donors and suitable recipients and / or potential recipients, to an emergency response agent working on a device associated with the emergency responder, based on a call with the caller agent.

19. A computer program for implementing the life-saving system described in any one of claims 1 to 13 using one or more computers.

20. A life-saving system that provides organs from a donor to a recipient, The recipient information management unit manages the recipient information, The Potential Donor Information Management Department manages information on potential donors, A transplant suitability detection unit detects suitability based on the recipient information and the potential donor information, A health status monitoring unit that monitors the health status of the potential donor, A donor selection unit that selects the potential donor as the donor based on the aforementioned suitability and health status, An organ donation system characterized by having a blockchain recording unit for recording, in an immutable format, the fact of donor selection by the donor selection unit and the information that forms the basis for that objective judgment.

21. The Potential Donor Information Management Department manages information on potential donors, An identifier generation unit generates a unique digital identifier, including a non-fungible token, for each type of organ provided by the aforementioned potential donor, and registers it on the blockchain. A history recording unit records the results of the compatibility detection, the results of the donor selection, and the entire physical and medical history from the extraction of the organ to its provision to the recipient, linked to the digital identifier and recorded on the blockchain. A life-saving system characterized by having the following features.

22. An organ preservation unit that preserves organs extracted from donors in a way that allows them to be restored to a transplantable state, A preservation treatment data recording unit records preservation treatment data in the long-term preservation treatment of organs by the organ preservation unit on the blockchain, linked to a digital identifier assigned to the organ. A life-saving system characterized by having a system that allows a third party to verify the quality maintenance status of the organs based on the recorded data.

23. A computer program for implementing the life-saving system described in any one of claims 20 to 22 using one or more computers.

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