system

Abstract

The system according to this embodiment aims to expedite negotiations between ambulances and hospitals regarding patient acceptance. [Solution] The system according to this embodiment comprises a medical examination unit, a decision-making unit, and a negotiation unit. The medical examination unit examines the patient's condition. The decision-making unit determines whether the hospital can accept the patient based on the information obtained by the medical examination unit. The negotiation unit has multiple agents simultaneously negotiate based on the decision obtained by the decision-making unit.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the acceptance negotiation between the ambulance and the hospital is not carried out quickly, and there is a risk that the treatment of the patient will be delayed.

[0005] The system according to the embodiment aims to quickly conduct the acceptance negotiation between the ambulance and the hospital.

Means for Solving the Problems

[0006] The system according to the embodiment includes an examination unit, a determination unit, and a negotiation unit. The examination unit examines the state of the patient. The determination unit determines whether the hospital can accept based on the information examined by the examination unit. The negotiation unit conducts negotiations simultaneously by a plurality of agents based on the determination result obtained by the determination unit.

Effects of the Invention

[0007] The system according to this embodiment can facilitate rapid negotiations between ambulances and hospitals regarding patient acceptance. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The multi-agent system according to an embodiment of the present invention is a system for expediting the determination of emergency transport destinations. This system deploys AI agents in ambulances and hospitals, and multiple agents cooperate to efficiently determine the transport destination. The AI ​​agent installed in the ambulance examines the patient's condition based on surrounding audio and video, automatically measures and analyzes vital signs, automatically determines the severity of the condition, and estimates the necessary medical resources. Next, it identifies nearby hospitals that can handle the patient based on GPS information and publicly available information about hospitals. The hospital side is equipped with an AI to assist in acceptance decisions, which manages the allocation of resources such as doctors and surgical instruments, the status of medical staff, the status of bed occupancy, and the operational status of medical equipment. This allows for real-time understanding of the acceptance status of each hospital and makes a decision on whether or not they can accept the patient. By having the AI ​​agent in the ambulance and the AI ​​agents in multiple hospitals cooperate and negotiate simultaneously, the optimal transport destination can be determined quickly. This system eliminates the need to negotiate one by one by phone as in the past, and can significantly reduce the time to make a decision. Furthermore, because it is managed in a distributed manner, the system load is reduced, and by designing it to simply return Yes / No to whether acceptance is possible, unnecessary disclosure of hospital information is avoided, thus respecting privacy. For example, an AI agent installed in an ambulance can automatically measure and analyze a patient's vital signs. For example, it can measure vital signs such as heart rate, blood pressure, and body temperature in real time, accurately understanding the patient's condition. The AI ​​agent can also automatically determine the severity of the patient's condition. For example, it can evaluate the type and urgency of symptoms and quickly determine the severity. In addition, the AI ​​agent can estimate the necessary medical resources. For example, it can estimate the number of necessary medical devices, medicines, and medical staff, and quickly secure appropriate medical resources. This speeds up the decision of emergency transport destinations and allows for the rapid commencement of patient treatment. Thus, the multi-agent system can speed up the decision of emergency transport destinations and allow for the rapid commencement of patient treatment.

[0029] The multi-agent system according to this embodiment comprises a diagnostic unit, a decision-making unit, and a negotiation unit. The diagnostic unit examines the patient's condition. The diagnostic unit can examine the patient's condition based on, for example, ambient sounds and images. The diagnostic unit can automatically measure and analyze the patient's vital signs. The diagnostic unit can accurately grasp the patient's condition by, for example, measuring vital signs such as heart rate, blood pressure, and body temperature in real time. The diagnostic unit can automatically determine the severity of the patient's condition. The diagnostic unit can quickly determine the severity by, for example, evaluating the type and urgency of symptoms. The diagnostic unit can estimate the necessary medical resources. The diagnostic unit can quickly secure appropriate medical resources by, for example, estimating the number of necessary medical devices, pharmaceuticals, and medical staff. The decision-making unit determines whether the hospital can accept the patient based on the information examined by the diagnostic unit. The decision-making unit can determine whether the hospital can accept the patient based on the information examined by the diagnostic unit. The decision unit can determine whether acceptance is possible by considering, for example, the hospital's resource status, the allocation of medical staff, the bed occupancy status, and the operational status of medical equipment. The decision unit can quickly determine whether acceptance is possible by, for example, grasping the hospital's resource status in real time. The negotiation unit has multiple agents negotiate simultaneously based on the decision results obtained by the decision unit. The negotiation unit can negotiate simultaneously with agents from multiple hospitals based on the decision results obtained by the decision unit. The negotiation unit can quickly determine the optimal destination by, for example, negotiating simultaneously with agents from multiple hospitals. The negotiation unit can significantly reduce the time to determine the destination by, for example, negotiating simultaneously with agents from multiple hospitals. As a result, the multi-agent system according to this embodiment can quickly determine the optimal destination by examining the patient's condition, determining whether hospitals can accept the patient, and having multiple agents negotiate simultaneously.

[0030] The medical examination unit examines the patient's condition. For example, the unit can assess the patient's condition based on surrounding audio and video. Specifically, it collects the patient's respiratory and heart sounds using a high-sensitivity microphone and monitors the patient's facial expressions and movements in real time using a video camera. This allows for a visual and auditory assessment of the patient's respiratory distress and pain levels. Furthermore, the medical examination unit can automatically measure and analyze the patient's vital signs. For example, it uses wearable devices to measure vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation in real time and transmits this data to a cloud-based database. Based on this data, the medical examination unit can accurately understand the patient's condition and immediately issue an alert if an abnormality is detected. The medical examination unit can automatically determine the severity of the patient's condition. For example, it utilizes AI-based diagnostic algorithms to evaluate collected vital signs, symptom types, and urgency, and quickly determine the severity. The AI ​​refers to past diagnostic data and medical literature, comparing with similar cases to make a more accurate severity determination. The medical examination unit can estimate the necessary medical resources. For example, it estimates the number of medical devices, medications, and medical staff needed based on the patient's symptoms and severity, and quickly secures appropriate medical resources. This allows the clinic to comprehensively examine the patient's condition and efficiently manage the necessary medical resources.

[0031] The decision-making unit determines whether a hospital can accept a patient based on information gathered by the examination unit. Specifically, it analyzes the patient's vital signs and symptom data collected by the examination unit, and considers the hospital's resource status, medical staff allocation, bed occupancy, and medical equipment operation status to determine acceptance. The decision-making unit can grasp the hospital's resource status in real time and make a quick decision on acceptance. For example, it can link with the hospital's electronic medical record system and medical equipment management system to obtain real-time information on bed availability and medical staff shifts. This allows the decision-making unit to comprehensively evaluate the patient's condition and the hospital's resource status and quickly determine the optimal receiving facility. Furthermore, the decision-making unit can also predict acceptance likelihood by utilizing past acceptance data and statistical information. For example, it can analyze bed utilization trends at specific times of day or on specific days of the week based on past acceptance history and predict acceptance likelihood. This allows the decision-making unit to improve the accuracy of acceptance decisions and support the rapid transport of patients.

[0032] The Negotiation Department conducts negotiations simultaneously with multiple agents based on the judgments obtained by the Decision Department. Specifically, it can negotiate simultaneously with agents from multiple hospitals based on the judgments obtained by the Decision Department. For example, by negotiating simultaneously with agents from multiple hospitals, the Negotiation Department can quickly determine the optimal destination for patient transfer. The Negotiation Department utilizes an AI-powered negotiation algorithm to evaluate the resource status and acceptance capacity of each hospital and propose the optimal destination. The AI ​​analyzes the information provided by agents from each hospital, significantly reducing the time to determine the destination. For example, it analyzes in real time the availability of hospital beds, the allocation of medical staff, and the operational status of medical equipment provided by agents from each hospital to quickly determine the optimal destination. Furthermore, by negotiating simultaneously with agents from multiple hospitals, the Negotiation Department can significantly reduce the time to determine the destination. In this way, the Negotiation Department plays a crucial role in supporting the rapid transfer of patients and providing optimal medical care. In addition, the Negotiation Department can improve the efficiency of negotiations by utilizing past negotiation history and statistical information. For example, based on past negotiation data, it can analyze the acceptance trends and success rates of specific hospitals and optimize negotiation strategies. This allows the negotiation department to always conduct optimal negotiations and ensure the rapid transport of patients.

[0033] The measurement unit can automatically measure and analyze vital signs. For example, the measurement unit can measure vital signs such as heart rate, blood pressure, and body temperature in real time, accurately understanding the patient's condition. For example, the measurement unit can automatically measure heart rate and issue an alert if an abnormality is detected. For example, the measurement unit can automatically measure blood pressure and notify medical staff if an abnormality is detected. For example, the measurement unit can automatically measure body temperature and notify medical staff if an abnormality is detected. As a result, the measurement unit can accurately understand the patient's condition by automatically measuring and analyzing vital signs. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generating AI, or without a generating AI. For example, the measurement unit can input vital sign data such as heart rate, blood pressure, and body temperature into a generating AI, which can detect abnormalities in vital signs and issue an alert.

[0034] The assessment unit can automatically determine the severity of a condition. For example, the assessment unit can evaluate the type and urgency of symptoms and quickly determine the severity. For example, the assessment unit can automatically evaluate the patient's symptoms and determine the severity. For example, the assessment unit can quickly determine the severity based on the patient's symptoms. For example, the assessment unit can use an algorithm to evaluate the patient's symptoms and determine the severity. This allows the assessment unit to quickly determine the severity of a patient by automatically determining the severity. Some or all of the above-described processes in the assessment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the assessment unit can input patient symptom data into a generating AI, and the generating AI can determine the severity.

[0035] The estimation unit can estimate the necessary medical resources. For example, the estimation unit can estimate the number of necessary medical devices, pharmaceuticals, and medical staff, and quickly secure appropriate medical resources. For example, the estimation unit can estimate the necessary medical resources based on the patient's symptoms and severity. For example, the estimation unit can evaluate the patient's symptoms and severity and quickly estimate the necessary medical resources. For example, the estimation unit can use an algorithm for estimating medical resources. This allows the estimation unit to quickly secure appropriate medical resources by estimating the necessary medical resources. Some or all of the above-described processes in the estimation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the estimation unit can input data on the patient's symptoms and severity into a generative AI, which can then estimate the necessary medical resources.

[0036] The management department can manage the allocation of medical staff. For example, the management department can manage medical staff shifts and ensure appropriate allocation. For example, the management department can monitor the allocation of medical staff in real time and adjust allocation as needed. For example, the management department can use algorithms to optimize the allocation of medical staff. For example, the management department can build a system for managing the allocation of medical staff. This allows the management department to quickly allocate the appropriate medical staff by managing the allocation of medical staff. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input medical staff shift data into a generative AI, which can then propose the optimal allocation.

[0037] The management department can manage the status of hospital bed utilization. For example, the management department can grasp the availability of hospital beds in real time and quickly secure appropriate beds. For example, the management department can calculate the utilization rate of hospital beds and aim for efficient use of hospital beds. For example, the management department can use algorithms to manage the status of hospital bed utilization. For example, the management department can build a system to manage the status of hospital bed utilization. This allows the management department to quickly secure appropriate beds by managing the status of hospital bed utilization. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input hospital bed availability data into a generative AI, and the generative AI can propose the optimal arrangement of hospital beds.

[0038] The management department can manage the operating status of medical devices. For example, the management department can grasp the operating time of medical devices in real time and quickly secure the appropriate medical devices. For example, the management department can manage the maintenance status of medical devices and perform maintenance as needed. For example, the management department can use algorithms to manage the operating status of medical devices. For example, the management department can build a system to manage the operating status of medical devices. This allows the management department to quickly secure the appropriate medical devices by managing their operating status. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input medical device operating status data into a generative AI, which can then propose the optimal placement of medical devices.

[0039] The examination unit can optimize the examination content by referring to the patient's past medical history during the examination. For example, the examination unit can refer to the patient's past medical history and prioritize displaying information related to the current symptoms. For example, the examination unit can propose the optimal examination method based on the patient's past treatment history. For example, the examination unit can refer to the patient's past allergy information and adjust the examination content. In this way, the examination content can be optimized by referring to the patient's past medical history. Some or all of the above processing in the examination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the examination unit can input the patient's past medical history data into a generating AI, and the generating AI can propose the optimal examination method.

[0040] The examination unit can adjust the examination results during the examination by considering the patient's current living situation and environmental information. For example, the examination unit can adjust the examination results by considering the patient's current living environment (e.g., living situation and work environment). For example, the examination unit can adjust the examination results by considering the patient's current lifestyle habits (e.g., eating and exercise habits). For example, the examination unit can adjust the examination results by considering the patient's current stress level. In this way, the examination results can be adjusted by considering the patient's current living situation and environmental information. Some or all of the above processing in the examination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the examination unit can input patient living situation and environmental information data into a generating AI, and the generating AI can adjust the examination results.

[0041] The examination department can adjust the content of the examination by taking into account the patient's geographical location information. For example, the examination department can adjust the content of the examination by taking into account the medical resources in the area where the patient lives. For example, the examination department can adjust the content of the examination by taking into account the environment (e.g., climate and pollution) in the area where the patient lives. For example, the examination department can adjust the content of the examination by taking into account the infectious disease epidemic situation in the area where the patient lives. In this way, the content of the examination can be adjusted by taking into account the patient's geographical location information. Some or all of the above processing in the examination department may be performed using, for example, a generative AI, or without using a generative AI. For example, the examination department can input the patient's geographical location information data into a generative AI, and the generative AI can adjust the content of the examination.

[0042] The medical department can analyze a patient's social media activity during a consultation and obtain relevant medical information. For example, the medical department can extract health-related information from a patient's social media posts and use it to aid in the consultation. For example, the medical department can analyze a patient's social media friendships and consider their impact on health. For example, the medical department can analyze a patient's social media activity patterns and obtain information about their lifestyle. In this way, relevant medical information can be obtained by analyzing a patient's social media activity. Some or all of the above processing in the medical department may be performed using, for example, generative AI, or without generative AI. For example, the medical department can input a patient's social media data into a generative AI, which can then obtain relevant medical information.

[0043] The judgment unit can adjust the level of detail in its judgment based on the severity of the patient's symptoms. For example, if the patient's symptoms are severe, the judgment unit can provide a detailed judgment result. For example, if the patient's symptoms are mild, the judgment unit can provide a concise judgment result. For example, if the patient's symptoms are moderate, the judgment unit can provide a judgment result with an appropriate level of detail. In this way, by adjusting the level of detail in the judgment based on the severity of the patient's symptoms, an appropriate judgment result can be provided. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the judgment unit can input patient symptom data into a generating AI, and the generating AI can adjust the level of detail in its judgment based on the severity of the symptoms.

[0044] The decision unit can apply different decision algorithms depending on the patient's medical history when making a decision. For example, if the patient has a history of heart disease, the decision unit can apply an algorithm specialized for heart disease. For example, if the patient has a history of diabetes, the decision unit can apply an algorithm specialized for diabetes. For example, if the patient has a history of allergies, the decision unit can apply an algorithm specialized for allergies. By applying different decision algorithms depending on the patient's medical history, the decision unit can provide an appropriate decision result. Some or all of the above processing in the decision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the decision unit can input patient medical history data into a generative AI, and the generative AI can apply a decision algorithm appropriate to the medical history.

[0045] The decision-making unit can determine the priority of decisions based on the patient's submission timing. For example, if the patient is in an emergency, the decision-making unit can set a higher priority. For example, if the patient is in a routine consultation, the decision-making unit can set the priority of decisions as usual. For example, if the patient is in a regular check-up, the decision-making unit can set a lower priority. This allows decisions to be made at the appropriate time by determining the priority of decisions based on the patient's submission timing. Some or all of the above processing in the decision-making unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the decision-making unit can input patient submission timing data into a generating AI, and the generating AI can determine the priority of decisions based on the submission timing.

[0046] The decision-making unit can adjust the order of decisions based on the patient's relevance during the decision-making process. For example, the decision-making unit can prioritize decisions if the patient's symptoms are severe. For example, the decision-making unit can postpone decisions if the patient's symptoms are mild. For example, the decision-making unit can set the order of decisions appropriately if the patient's symptoms are moderate. This allows decisions to be made in an appropriate order by adjusting the order of decisions based on the patient's relevance. Some or all of the above-described processes in the decision-making unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the decision-making unit can input patient relevance data into a generating AI, which can then adjust the order of decisions based on the relevance.

[0047] The negotiation department can optimize negotiation content by referring to the hospital's past admission history during negotiations. For example, the negotiation department can refer to the hospital's past admission history and prioritize the selection of hospitals that can accept patients. For example, the negotiation department can propose the optimal negotiation method based on the hospital's past admission history. For example, the negotiation department can analyze the hospital's past admission history and quickly identify hospitals that can accept patients. This allows for the optimization of negotiation content by referring to the hospital's past admission history. Some or all of the above processes in the negotiation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiation department can input the hospital's past admission history data into a generative AI, which can then propose the optimal negotiation method.

[0048] The negotiating department can adjust the negotiation results during negotiations by taking into account the hospital's current resource situation. For example, the negotiating department can adjust the negotiation results by taking into account the hospital's current medical staffing situation. For example, the negotiating department can adjust the negotiation results by taking into account the hospital's current bed occupancy situation. For example, the negotiating department can adjust the negotiation results by taking into account the hospital's current medical equipment operating status. In this way, the negotiation results can be adjusted by taking into account the hospital's current resource situation. Some or all of the above processing in the negotiating department may be performed using, for example, a generating AI, or without using a generating AI. For example, the negotiating department can input hospital resource situation data into a generating AI, and the generating AI can adjust the negotiation results based on the resource situation.

[0049] The negotiating department can adjust the negotiation content during negotiations by taking into account the geographical location information of the hospitals. For example, the negotiating department can prioritize selecting the nearest hospital based on the geographical location information of the hospitals. For example, the negotiating department can select the most suitable hospital based on traffic conditions, taking into account the geographical location information of the hospitals. For example, the negotiating department can select a hospital that minimizes the patient's travel time based on the geographical location information of the hospitals. In this way, the negotiation content can be adjusted by taking into account the geographical location information of the hospitals. Some or all of the above processing in the negotiating department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating department can input the geographical location information data of the hospitals into a generative AI, and the generative AI can adjust the negotiation content based on the geographical location information.

[0050] The negotiating department can analyze the hospital's social media activity during negotiations and obtain relevant negotiation information. For example, the negotiating department can extract acceptable information from the hospital's social media posts and use it to aid in negotiations. For example, the negotiating department can analyze the hospital's reputation on social media and select a reliable hospital. For example, the negotiating department can analyze the hospital's social media activity patterns and quickly identify acceptable hospitals. This allows the negotiating department to obtain relevant negotiation information by analyzing the hospital's social media activity. Some or all of the above processes in the negotiating department may be performed using, for example, generative AI, or not using generative AI. For example, the negotiating department can input the hospital's social media data into a generative AI, which can then obtain relevant negotiation information.

[0051] The measurement unit can optimize the measurement content by referring to the patient's past vital sign history during measurement. For example, the measurement unit can optimize the current measurement content by referring to the patient's past vital sign history. For example, the measurement unit can detect abnormal values ​​early based on the patient's past vital sign history. For example, the measurement unit can analyze the patient's past vital sign history and propose the optimal measurement method. This allows the measurement content to be optimized by referring to the patient's past vital sign history. Some or all of the above processing in the measurement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the measurement unit can input the patient's past vital sign history data into a generating AI, and the generating AI can propose the optimal measurement method.

[0052] The measurement unit can adjust the measurement content while taking into account the patient's geographical location information. For example, the measurement unit can adjust the measurement content while taking into account the medical resources in the area where the patient lives. For example, the measurement unit can adjust the measurement content while taking into account the environment (e.g., climate and pollution) in the area where the patient lives. For example, the measurement unit can adjust the measurement content while taking into account the infectious disease epidemic situation in the area where the patient lives. In this way, the measurement content can be adjusted by taking into account the patient's geographical location information. Some or all of the above processing in the measurement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the measurement unit can input the patient's geographical location information data into a generating AI, and the generating AI can adjust the measurement content based on the geographical location information.

[0053] The judgment unit can adjust the level of detail in its judgment based on the severity of the patient's symptoms. For example, if the patient's symptoms are severe, the judgment unit can provide a detailed judgment result. For example, if the patient's symptoms are mild, the judgment unit can provide a concise judgment result. For example, if the patient's symptoms are moderate, the judgment unit can provide a judgment result with an appropriate level of detail. In this way, by adjusting the level of detail in the judgment based on the severity of the patient's symptoms, an appropriate judgment result can be provided. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the judgment unit can input patient symptom data into a generating AI, and the generating AI can adjust the level of detail in the judgment based on the severity of the symptoms.

[0054] The judgment unit can determine the priority of the judgment based on the patient's submission timing. For example, if the patient is in an emergency, the judgment unit can set a higher priority for the judgment. For example, if the patient is in a routine consultation, the judgment unit can set the priority of the judgment as usual. For example, if the patient is in a regular check-up, the judgment unit can set a lower priority for the judgment. This allows the judgment to be made at an appropriate time by determining the priority of the judgment based on the patient's submission timing. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the judgment unit can input patient submission timing data into a generating AI, and the generating AI can determine the priority of the judgment based on the submission timing.

[0055] The estimation unit can optimize the estimation content by referring to the patient's past medical resource utilization history during estimation. For example, the estimation unit can optimize the current estimation content by referring to the patient's past medical resource utilization history. For example, the estimation unit can estimate the optimal medical resource based on the patient's past medical resource utilization history. For example, the estimation unit can analyze the patient's past medical resource utilization history and propose the optimal estimation method. This allows the estimation content to be optimized by referring to the patient's past medical resource utilization history. Some or all of the above processing in the estimation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the estimation unit can input the patient's past medical resource utilization history data into a generating AI, and the generating AI can propose the optimal estimation method.

[0056] The estimation unit can adjust the estimation content by considering the patient's geographical location information during estimation. For example, the estimation unit can adjust the estimation content by considering the medical resources in the area where the patient lives. For example, the estimation unit can adjust the estimation content by considering the environment (e.g., climate and pollution) in the area where the patient lives. For example, the estimation unit can adjust the estimation content by considering the infectious disease epidemic situation in the area where the patient lives. In this way, the estimation content can be adjusted by considering the patient's geographical location information. Some or all of the above processing in the estimation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the estimation unit can input the patient's geographical location information data into a generating AI, and the generating AI can adjust the estimation content based on the geographical location information.

[0057] The management department can optimize management practices by referring to the hospital's past resource management history during management. For example, the management department can optimize current management practices by referring to the hospital's past resource management history. For example, the management department can propose the optimal resource management method based on the hospital's past resource management history. For example, the management department can analyze the hospital's past resource management history and derive the optimal management method. This allows for the optimization of management practices by referring to the hospital's past resource management history. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department can input the hospital's past resource management history data into a generative AI, which can then propose the optimal management method.

[0058] The management department can adjust management procedures by considering the geographical location information of hospitals during management. For example, the management department can prioritize selecting the nearest hospital based on the geographical location information of hospitals. For example, the management department can select the most suitable hospital based on traffic conditions, taking into account the geographical location information of hospitals. For example, the management department can select a hospital that minimizes patient travel time based on the geographical location information of hospitals. In this way, management procedures can be adjusted by considering the geographical location information of hospitals. Some or all of the above-described processes in the management department may be performed using, for example, a generating AI, or without using a generating AI. For example, the management department can input hospital geographical location data into a generating AI, and the generating AI can adjust management procedures based on the geographical location information.

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

[0060] The consultation department can optimize the consultation by referring to the patient's past medical history. For example, it can refer to the patient's past medical history and prioritize displaying information relevant to the current symptoms. Based on the patient's past treatment history, it can suggest the most appropriate consultation method. It can also refer to the patient's past allergy information and adjust the consultation accordingly. In this way, the consultation can be optimized by referring to the patient's past medical history.

[0061] The medical department can adjust the examination results by considering the patient's current living situation and environmental information. For example, the examination results can be adjusted by considering the patient's current living environment (housing situation and work environment). The examination results can be adjusted by considering the patient's current lifestyle habits (eating and exercise habits). The examination results can be adjusted by considering the patient's current stress level. In this way, the examination results can be adjusted by considering the patient's current living situation and environmental information.

[0062] The medical department can adjust the content of the examination considering the patient's geographical location. For example, the content of the examination can be adjusted considering the medical resources in the area where the patient lives. The content of the examination can be adjusted considering the environment (climate and pollution) in the area where the patient lives. The content of the examination can be adjusted considering the infectious disease epidemic situation in the area where the patient lives. In this way, the content of the examination can be adjusted by considering the patient's geographical location.

[0063] The clinical department can analyze patients' social media activity and obtain relevant clinical information. For example, they can extract health-related information from patients' social media posts and use it to aid in consultations. They can analyze patients' social media friendships and consider their impact on health. They can analyze patients' social media activity patterns and obtain information about their lifestyle. In short, by analyzing patients' social media activity, relevant clinical information can be obtained.

[0064] The judgment unit can adjust the level of detail in its judgment based on the severity of the patient's symptoms. For example, if the patient's symptoms are severe, it can provide a detailed judgment result. If the patient's symptoms are mild, it can provide a concise judgment result. If the patient's symptoms are moderate, it can provide a judgment result with an appropriate level of detail. In this way, by adjusting the level of detail in the judgment based on the severity of the patient's symptoms, it is possible to provide an appropriate judgment result.

[0065] The decision-making unit can apply different decision algorithms depending on the patient's medical history. For example, if the patient has a history of heart disease, a heart disease-specific algorithm can be applied. If the patient has a history of diabetes, a diabetes-specific algorithm can be applied. If the patient has a history of allergies, an allergy-specific algorithm can be applied. By applying different decision algorithms according to the patient's history, the system can provide appropriate decision results.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The examination unit examines the patient's condition. The examination unit can assess the patient's condition based on, for example, surrounding audio and video. It can also measure and analyze the patient's vital signs (heart rate, blood pressure, body temperature, etc.) in real time. Furthermore, the examination unit can automatically determine the severity of the patient's condition and estimate the necessary medical resources (number of medical devices, medications, and medical staff). Step 2: The decision-making unit determines whether the hospital can accept the patient based on the information gathered by the examination unit. The decision-making unit can determine whether acceptance is possible by considering, for example, the hospital's resource status, the allocation of medical staff, the bed occupancy status, and the operational status of medical equipment. The decision-making unit can grasp the hospital's resource status in real time and quickly determine whether acceptance is possible. Step 3: The Negotiation Department conducts negotiations with multiple agents simultaneously based on the decision-making results obtained by the Decision-Making Department. By negotiating with agents from multiple hospitals simultaneously, for example, the Negotiation Department can quickly determine the optimal destination for transport. This significantly reduces the time required to determine the destination for transport.

[0068] (Example of form 2) The multi-agent system according to an embodiment of the present invention is a system for expediting the determination of emergency transport destinations. This system deploys AI agents in ambulances and hospitals, and multiple agents cooperate to efficiently determine the transport destination. The AI ​​agent installed in the ambulance examines the patient's condition based on surrounding audio and video, automatically measures and analyzes vital signs, automatically determines the severity of the condition, and estimates the necessary medical resources. Next, it identifies nearby hospitals that can handle the patient based on GPS information and publicly available information about hospitals. The hospital side is equipped with an AI to assist in acceptance decisions, which manages the allocation of resources such as doctors and surgical instruments, the status of medical staff, the status of bed occupancy, and the operational status of medical equipment. This allows for real-time understanding of the acceptance status of each hospital and makes a decision on whether or not they can accept the patient. By having the AI ​​agent in the ambulance and the AI ​​agents in multiple hospitals cooperate and negotiate simultaneously, the optimal transport destination can be determined quickly. This system eliminates the need to negotiate one by one by phone as in the past, and can significantly reduce the time to make a decision. Furthermore, because it is managed in a distributed manner, the system load is reduced, and by designing it to simply return Yes / No to whether acceptance is possible, unnecessary disclosure of hospital information is avoided, thus respecting privacy. For example, an AI agent installed in an ambulance can automatically measure and analyze a patient's vital signs. For example, it can measure vital signs such as heart rate, blood pressure, and body temperature in real time, accurately understanding the patient's condition. The AI ​​agent can also automatically determine the severity of the patient's condition. For example, it can evaluate the type and urgency of symptoms and quickly determine the severity. In addition, the AI ​​agent can estimate the necessary medical resources. For example, it can estimate the number of necessary medical devices, medicines, and medical staff, and quickly secure appropriate medical resources. This speeds up the decision of emergency transport destinations and allows for the rapid commencement of patient treatment. Thus, the multi-agent system can speed up the decision of emergency transport destinations and allow for the rapid commencement of patient treatment.

[0069] The multi-agent system according to this embodiment comprises a diagnostic unit, a decision-making unit, and a negotiation unit. The diagnostic unit examines the patient's condition. The diagnostic unit can examine the patient's condition based on, for example, ambient sounds and images. The diagnostic unit can automatically measure and analyze the patient's vital signs. The diagnostic unit can accurately grasp the patient's condition by, for example, measuring vital signs such as heart rate, blood pressure, and body temperature in real time. The diagnostic unit can automatically determine the severity of the patient's condition. The diagnostic unit can quickly determine the severity by, for example, evaluating the type and urgency of symptoms. The diagnostic unit can estimate the necessary medical resources. The diagnostic unit can quickly secure appropriate medical resources by, for example, estimating the number of necessary medical devices, pharmaceuticals, and medical staff. The decision-making unit determines whether the hospital can accept the patient based on the information examined by the diagnostic unit. The decision-making unit can determine whether the hospital can accept the patient based on the information examined by the diagnostic unit. The decision unit can determine whether acceptance is possible by considering, for example, the hospital's resource status, the allocation of medical staff, the bed occupancy status, and the operational status of medical equipment. The decision unit can quickly determine whether acceptance is possible by, for example, grasping the hospital's resource status in real time. The negotiation unit has multiple agents negotiate simultaneously based on the decision results obtained by the decision unit. The negotiation unit can negotiate simultaneously with agents from multiple hospitals based on the decision results obtained by the decision unit. The negotiation unit can quickly determine the optimal destination by, for example, negotiating simultaneously with agents from multiple hospitals. The negotiation unit can significantly reduce the time to determine the destination by, for example, negotiating simultaneously with agents from multiple hospitals. As a result, the multi-agent system according to this embodiment can quickly determine the optimal destination by examining the patient's condition, determining whether hospitals can accept the patient, and having multiple agents negotiate simultaneously.

[0070] The medical examination unit examines the patient's condition. For example, the unit can assess the patient's condition based on surrounding audio and video. Specifically, it collects the patient's respiratory and heart sounds using a high-sensitivity microphone and monitors the patient's facial expressions and movements in real time using a video camera. This allows for a visual and auditory assessment of the patient's respiratory distress and pain levels. Furthermore, the medical examination unit can automatically measure and analyze the patient's vital signs. For example, it uses wearable devices to measure vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation in real time and transmits this data to a cloud-based database. Based on this data, the medical examination unit can accurately understand the patient's condition and immediately issue an alert if an abnormality is detected. The medical examination unit can automatically determine the severity of the patient's condition. For example, it utilizes AI-based diagnostic algorithms to evaluate collected vital signs, symptom types, and urgency, and quickly determine the severity. The AI ​​refers to past diagnostic data and medical literature, comparing with similar cases to make a more accurate severity determination. The medical examination unit can estimate the necessary medical resources. For example, it estimates the number of medical devices, medications, and medical staff needed based on the patient's symptoms and severity, and quickly secures appropriate medical resources. This allows the clinic to comprehensively examine the patient's condition and efficiently manage the necessary medical resources.

[0071] The decision-making unit determines whether a hospital can accept a patient based on information gathered by the examination unit. Specifically, it analyzes the patient's vital signs and symptom data collected by the examination unit, and considers the hospital's resource status, medical staff allocation, bed occupancy, and medical equipment operation status to determine acceptance. The decision-making unit can grasp the hospital's resource status in real time and make a quick decision on acceptance. For example, it can link with the hospital's electronic medical record system and medical equipment management system to obtain real-time information on bed availability and medical staff shifts. This allows the decision-making unit to comprehensively evaluate the patient's condition and the hospital's resource status and quickly determine the optimal receiving facility. Furthermore, the decision-making unit can also predict acceptance likelihood by utilizing past acceptance data and statistical information. For example, it can analyze bed utilization trends at specific times of day or on specific days of the week based on past acceptance history and predict acceptance likelihood. This allows the decision-making unit to improve the accuracy of acceptance decisions and support the rapid transport of patients.

[0072] The Negotiation Department conducts negotiations simultaneously with multiple agents based on the judgments obtained by the Decision Department. Specifically, it can negotiate simultaneously with agents from multiple hospitals based on the judgments obtained by the Decision Department. For example, by negotiating simultaneously with agents from multiple hospitals, the Negotiation Department can quickly determine the optimal destination for patient transfer. The Negotiation Department utilizes an AI-powered negotiation algorithm to evaluate the resource status and acceptance capacity of each hospital and propose the optimal destination. The AI ​​analyzes the information provided by agents from each hospital, significantly reducing the time to determine the destination. For example, it analyzes in real time the availability of hospital beds, the allocation of medical staff, and the operational status of medical equipment provided by agents from each hospital to quickly determine the optimal destination. Furthermore, by negotiating simultaneously with agents from multiple hospitals, the Negotiation Department can significantly reduce the time to determine the destination. In this way, the Negotiation Department plays a crucial role in supporting the rapid transfer of patients and providing optimal medical care. In addition, the Negotiation Department can improve the efficiency of negotiations by utilizing past negotiation history and statistical information. For example, based on past negotiation data, it can analyze the acceptance trends and success rates of specific hospitals and optimize negotiation strategies. This allows the negotiation department to always conduct optimal negotiations and ensure the rapid transport of patients.

[0073] The measurement unit can automatically measure and analyze vital signs. For example, the measurement unit can measure vital signs such as heart rate, blood pressure, and body temperature in real time, accurately understanding the patient's condition. For example, the measurement unit can automatically measure heart rate and issue an alert if an abnormality is detected. For example, the measurement unit can automatically measure blood pressure and notify medical staff if an abnormality is detected. For example, the measurement unit can automatically measure body temperature and notify medical staff if an abnormality is detected. As a result, the measurement unit can accurately understand the patient's condition by automatically measuring and analyzing vital signs. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generating AI, or without a generating AI. For example, the measurement unit can input vital sign data such as heart rate, blood pressure, and body temperature into a generating AI, which can detect abnormalities in vital signs and issue an alert.

[0074] The assessment unit can automatically determine the severity of a condition. For example, the assessment unit can evaluate the type and urgency of symptoms and quickly determine the severity. For example, the assessment unit can automatically evaluate the patient's symptoms and determine the severity. For example, the assessment unit can quickly determine the severity based on the patient's symptoms. For example, the assessment unit can use an algorithm to evaluate the patient's symptoms and determine the severity. This allows the assessment unit to quickly determine the severity of a patient by automatically determining the severity. Some or all of the above-described processes in the assessment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the assessment unit can input patient symptom data into a generating AI, and the generating AI can determine the severity.

[0075] The estimation unit can estimate the necessary medical resources. For example, the estimation unit can estimate the number of necessary medical devices, pharmaceuticals, and medical staff, and quickly secure appropriate medical resources. For example, the estimation unit can estimate the necessary medical resources based on the patient's symptoms and severity. For example, the estimation unit can evaluate the patient's symptoms and severity and quickly estimate the necessary medical resources. For example, the estimation unit can use an algorithm for estimating medical resources. This allows the estimation unit to quickly secure appropriate medical resources by estimating the necessary medical resources. Some or all of the above-described processes in the estimation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the estimation unit can input data on the patient's symptoms and severity into a generative AI, which can then estimate the necessary medical resources.

[0076] The management department can manage the allocation of medical staff. For example, the management department can manage medical staff shifts and ensure appropriate allocation. For example, the management department can monitor the allocation of medical staff in real time and adjust allocation as needed. For example, the management department can use algorithms to optimize the allocation of medical staff. For example, the management department can build a system for managing the allocation of medical staff. This allows the management department to quickly allocate the appropriate medical staff by managing the allocation of medical staff. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input medical staff shift data into a generative AI, which can then propose the optimal allocation.

[0077] The management department can manage the status of hospital bed utilization. For example, the management department can grasp the availability of hospital beds in real time and quickly secure appropriate beds. For example, the management department can calculate the utilization rate of hospital beds and aim for efficient use of hospital beds. For example, the management department can use algorithms to manage the status of hospital bed utilization. For example, the management department can build a system to manage the status of hospital bed utilization. This allows the management department to quickly secure appropriate beds by managing the status of hospital bed utilization. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input hospital bed availability data into a generative AI, and the generative AI can propose the optimal arrangement of hospital beds.

[0078] The management department can manage the operating status of medical devices. For example, the management department can grasp the operating time of medical devices in real time and quickly secure the appropriate medical devices. For example, the management department can manage the maintenance status of medical devices and perform maintenance as needed. For example, the management department can use algorithms to manage the operating status of medical devices. For example, the management department can build a system to manage the operating status of medical devices. This allows the management department to quickly secure the appropriate medical devices by managing their operating status. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input medical device operating status data into a generative AI, which can then propose the optimal placement of medical devices.

[0079] The examination unit can estimate the patient's emotions and adjust the pace of the examination based on the estimated emotions. For example, if the patient is feeling anxious, the examination unit can slow down the pace of the examination to make the patient feel at ease. For example, if the patient is relaxed, the examination unit can maintain a normal pace of the examination. For example, if the patient is in a hurry, the examination unit can speed up the pace of the examination to conduct the examination quickly. In this way, by adjusting the pace of the examination according to the patient's emotions, the patient can feel at ease. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the examination unit may be performed using AI, for example, or not using AI. For example, the examination unit can input the patient's facial expression data into a generative AI, which can estimate the patient's emotions and adjust the pace of the examination.

[0080] The examination unit can optimize the examination content by referring to the patient's past medical history during the examination. For example, the examination unit can refer to the patient's past medical history and prioritize displaying information related to the current symptoms. For example, the examination unit can propose the optimal examination method based on the patient's past treatment history. For example, the examination unit can refer to the patient's past allergy information and adjust the examination content. In this way, the examination content can be optimized by referring to the patient's past medical history. Some or all of the above processing in the examination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the examination unit can input the patient's past medical history data into a generating AI, and the generating AI can propose the optimal examination method.

[0081] The examination unit can adjust the examination results during the examination by considering the patient's current living situation and environmental information. For example, the examination unit can adjust the examination results by considering the patient's current living environment (e.g., living situation and work environment). For example, the examination unit can adjust the examination results by considering the patient's current lifestyle habits (e.g., eating and exercise habits). For example, the examination unit can adjust the examination results by considering the patient's current stress level. In this way, the examination results can be adjusted by considering the patient's current living situation and environmental information. Some or all of the above processing in the examination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the examination unit can input patient living situation and environmental information data into a generating AI, and the generating AI can adjust the examination results.

[0082] The examination unit can estimate the patient's emotions and determine the priority of the examination based on the estimated emotions. For example, if the patient is experiencing severe pain, the examination unit can set a higher priority. For example, if the patient is feeling anxious, the examination unit can set a higher priority. For example, if the patient is relaxed, the examination unit can set the priority to normal. This allows for quick examinations by determining the priority of the examination according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the examination unit may be performed using AI, for example, or without AI. For example, the examination unit can input patient facial expression data into a generative AI, which can estimate the patient's emotions and determine the priority of the examination.

[0083] The examination department can adjust the content of the examination by taking into account the patient's geographical location information. For example, the examination department can adjust the content of the examination by taking into account the medical resources in the area where the patient lives. For example, the examination department can adjust the content of the examination by taking into account the environment (e.g., climate and pollution) in the area where the patient lives. For example, the examination department can adjust the content of the examination by taking into account the infectious disease epidemic situation in the area where the patient lives. In this way, the content of the examination can be adjusted by taking into account the patient's geographical location information. Some or all of the above processing in the examination department may be performed using, for example, a generative AI, or without using a generative AI. For example, the examination department can input the patient's geographical location information data into a generative AI, and the generative AI can adjust the content of the examination.

[0084] The medical department can analyze a patient's social media activity during a consultation and obtain relevant medical information. For example, the medical department can extract health-related information from a patient's social media posts and use it to aid in the consultation. For example, the medical department can analyze a patient's social media friendships and consider their impact on health. For example, the medical department can analyze a patient's social media activity patterns and obtain information about their lifestyle. In this way, relevant medical information can be obtained by analyzing a patient's social media activity. Some or all of the above processing in the medical department may be performed using, for example, generative AI, or without generative AI. For example, the medical department can input a patient's social media data into a generative AI, which can then obtain relevant medical information.

[0085] The decision-making unit can estimate the patient's emotions and adjust the way the decision is expressed based on the estimated emotions. For example, if the patient is feeling anxious, the decision-making unit can convey the decision in gentle language. For example, if the patient is relaxed, the decision-making unit can convey the decision in normal language. For example, if the patient is in a hurry, the decision-making unit can convey the decision concisely. In this way, by adjusting the way the decision is expressed according to the patient's emotions, a sense of security can be given to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and adjust the way the decision is expressed.

[0086] The judgment unit can adjust the level of detail in its judgment based on the severity of the patient's symptoms. For example, if the patient's symptoms are severe, the judgment unit can provide a detailed judgment result. For example, if the patient's symptoms are mild, the judgment unit can provide a concise judgment result. For example, if the patient's symptoms are moderate, the judgment unit can provide a judgment result with an appropriate level of detail. In this way, by adjusting the level of detail in the judgment based on the severity of the patient's symptoms, an appropriate judgment result can be provided. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the judgment unit can input patient symptom data into a generating AI, and the generating AI can adjust the level of detail in its judgment based on the severity of the symptoms.

[0087] The decision unit can apply different decision algorithms depending on the patient's medical history when making a decision. For example, if the patient has a history of heart disease, the decision unit can apply an algorithm specialized for heart disease. For example, if the patient has a history of diabetes, the decision unit can apply an algorithm specialized for diabetes. For example, if the patient has a history of allergies, the decision unit can apply an algorithm specialized for allergies. By applying different decision algorithms depending on the patient's medical history, the decision unit can provide an appropriate decision result. Some or all of the above processing in the decision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the decision unit can input patient medical history data into a generative AI, and the generative AI can apply a decision algorithm appropriate to the medical history.

[0088] The decision-making unit can estimate the patient's emotions and determine the priority of decisions based on the estimated emotions. For example, if the patient is experiencing severe pain, the decision-making unit can set a higher priority for decisions. For example, if the patient is feeling anxious, the decision-making unit can set a higher priority for decisions. For example, if the patient is relaxed, the decision-making unit can set the priority of decisions to normal. This allows for quick decision-making by determining the priority of decisions according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input patient facial expression data into a generative AI, which can estimate the patient's emotions and determine the priority of decisions.

[0089] The decision-making unit can determine the priority of decisions based on the patient's submission timing. For example, if the patient is in an emergency, the decision-making unit can set a higher priority. For example, if the patient is in a routine consultation, the decision-making unit can set the priority of decisions as usual. For example, if the patient is in a regular check-up, the decision-making unit can set a lower priority. This allows decisions to be made at the appropriate time by determining the priority of decisions based on the patient's submission timing. Some or all of the above processing in the decision-making unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the decision-making unit can input patient submission timing data into a generating AI, and the generating AI can determine the priority of decisions based on the submission timing.

[0090] The decision-making unit can adjust the order of decisions based on the patient's relevance during the decision-making process. For example, the decision-making unit can prioritize decisions if the patient's symptoms are severe. For example, the decision-making unit can postpone decisions if the patient's symptoms are mild. For example, the decision-making unit can set the order of decisions appropriately if the patient's symptoms are moderate. This allows decisions to be made in an appropriate order by adjusting the order of decisions based on the patient's relevance. Some or all of the above-described processes in the decision-making unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the decision-making unit can input patient relevance data into a generating AI, which can then adjust the order of decisions based on the relevance.

[0091] The negotiation unit can estimate the patient's emotions and adjust the pace of negotiation based on the estimated emotions. For example, if the patient is feeling anxious, the negotiation unit can slow down the pace of negotiation to make the patient feel at ease. For example, if the patient is relaxed, the negotiation unit can maintain a normal pace of negotiation. For example, if the patient is in a hurry, the negotiation unit can speed up the pace of negotiation to conduct the negotiation quickly. In this way, by adjusting the pace of negotiation according to the patient's emotions, the patient can feel at ease. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not using AI. For example, the negotiation unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and adjust the pace of negotiation.

[0092] The negotiation department can optimize negotiation content by referring to the hospital's past admission history during negotiations. For example, the negotiation department can refer to the hospital's past admission history and prioritize the selection of hospitals that can accept patients. For example, the negotiation department can propose the optimal negotiation method based on the hospital's past admission history. For example, the negotiation department can analyze the hospital's past admission history and quickly identify hospitals that can accept patients. This allows for the optimization of negotiation content by referring to the hospital's past admission history. Some or all of the above processes in the negotiation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiation department can input the hospital's past admission history data into a generative AI, which can then propose the optimal negotiation method.

[0093] The negotiating department can adjust the negotiation results during negotiations by taking into account the hospital's current resource situation. For example, the negotiating department can adjust the negotiation results by taking into account the hospital's current medical staffing situation. For example, the negotiating department can adjust the negotiation results by taking into account the hospital's current bed occupancy situation. For example, the negotiating department can adjust the negotiation results by taking into account the hospital's current medical equipment operating status. In this way, the negotiation results can be adjusted by taking into account the hospital's current resource situation. Some or all of the above processing in the negotiating department may be performed using, for example, a generating AI, or without using a generating AI. For example, the negotiating department can input hospital resource situation data into a generating AI, and the generating AI can adjust the negotiation results based on the resource situation.

[0094] The negotiating department can adjust the negotiation content during negotiations by taking into account the geographical location information of the hospitals. For example, the negotiating department can prioritize selecting the nearest hospital based on the geographical location information of the hospitals. For example, the negotiating department can select the most suitable hospital based on traffic conditions, taking into account the geographical location information of the hospitals. For example, the negotiating department can select a hospital that minimizes the patient's travel time based on the geographical location information of the hospitals. In this way, the negotiation content can be adjusted by taking into account the geographical location information of the hospitals. Some or all of the above processing in the negotiating department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiating department can input the geographical location information data of the hospitals into a generative AI, and the generative AI can adjust the negotiation content based on the geographical location information.

[0095] The negotiating department can analyze the hospital's social media activity during negotiations and obtain relevant negotiation information. For example, the negotiating department can extract acceptable information from the hospital's social media posts and use it to aid in negotiations. For example, the negotiating department can analyze the hospital's reputation on social media and select a reliable hospital. For example, the negotiating department can analyze the hospital's social media activity patterns and quickly identify acceptable hospitals. This allows the negotiating department to obtain relevant negotiation information by analyzing the hospital's social media activity. Some or all of the above processes in the negotiating department may be performed using, for example, generative AI, or not using generative AI. For example, the negotiating department can input the hospital's social media data into a generative AI, which can then obtain relevant negotiation information.

[0096] The measurement unit can estimate the patient's emotions and adjust the measurement speed based on the estimated emotions. For example, if the patient is feeling anxious, the measurement unit can slow down the measurement speed to give the patient a sense of security. For example, if the patient is relaxed, the measurement unit can maintain a normal measurement speed. For example, if the patient is in a hurry, the measurement unit can speed up the measurement speed to perform the measurement quickly. In this way, by adjusting the measurement speed according to the patient's emotions, a sense of security can be given to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and adjust the measurement speed.

[0097] The measurement unit can optimize the measurement content by referring to the patient's past vital sign history during measurement. For example, the measurement unit can optimize the current measurement content by referring to the patient's past vital sign history. For example, the measurement unit can detect abnormal values ​​early based on the patient's past vital sign history. For example, the measurement unit can analyze the patient's past vital sign history and propose the optimal measurement method. This allows the measurement content to be optimized by referring to the patient's past vital sign history. Some or all of the above processing in the measurement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the measurement unit can input the patient's past vital sign history data into a generating AI, and the generating AI can propose the optimal measurement method.

[0098] The measurement unit can estimate the patient's emotions and determine the measurement priority based on the estimated emotions. For example, if the patient is experiencing severe pain, the measurement unit can set a higher measurement priority. For example, if the patient is feeling anxious, the measurement unit can set a higher measurement priority. For example, if the patient is relaxed, the measurement unit can set the measurement priority to normal. This allows for rapid measurement by determining the measurement priority according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and determine the measurement priority.

[0099] The measurement unit can adjust the measurement content while taking into account the patient's geographical location information. For example, the measurement unit can adjust the measurement content while taking into account the medical resources in the area where the patient lives. For example, the measurement unit can adjust the measurement content while taking into account the environment (e.g., climate and pollution) in the area where the patient lives. For example, the measurement unit can adjust the measurement content while taking into account the infectious disease epidemic situation in the area where the patient lives. In this way, the measurement content can be adjusted by taking into account the patient's geographical location information. Some or all of the above processing in the measurement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the measurement unit can input the patient's geographical location information data into a generating AI, and the generating AI can adjust the measurement content based on the geographical location information.

[0100] The judgment unit can estimate the patient's emotions and adjust the way the judgment is expressed based on the estimated emotions. For example, if the patient is feeling anxious, the judgment unit can convey the judgment result in gentle language. For example, if the patient is relaxed, the judgment unit can convey the judgment result in normal language. For example, if the patient is in a hurry, the judgment unit can convey the judgment result concisely. In this way, by adjusting the way the judgment is expressed according to the patient's emotions, a sense of security can be given to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and adjust the way the judgment is expressed.

[0101] The judgment unit can adjust the level of detail in its judgment based on the severity of the patient's symptoms. For example, if the patient's symptoms are severe, the judgment unit can provide a detailed judgment result. For example, if the patient's symptoms are mild, the judgment unit can provide a concise judgment result. For example, if the patient's symptoms are moderate, the judgment unit can provide a judgment result with an appropriate level of detail. In this way, by adjusting the level of detail in the judgment based on the severity of the patient's symptoms, an appropriate judgment result can be provided. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the judgment unit can input patient symptom data into a generating AI, and the generating AI can adjust the level of detail in the judgment based on the severity of the symptoms.

[0102] The judgment unit can estimate the patient's emotions and determine the priority of judgments based on the estimated emotions. For example, if the patient is experiencing severe pain, the judgment unit can set a higher priority for judgment. For example, if the patient is feeling anxious, the judgment unit can set a higher priority for judgment. For example, if the patient is relaxed, the judgment unit can set the priority of judgment to normal. This allows for quick judgments by determining the priority of judgments according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and determine the priority of judgments.

[0103] The judgment unit can determine the priority of the judgment based on the patient's submission timing. For example, if the patient is in an emergency, the judgment unit can set a higher priority for the judgment. For example, if the patient is in a routine consultation, the judgment unit can set the priority of the judgment as usual. For example, if the patient is in a regular check-up, the judgment unit can set a lower priority for the judgment. This allows the judgment to be made at an appropriate time by determining the priority of the judgment based on the patient's submission timing. Some or all of the above processing in the judgment unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the judgment unit can input patient submission timing data into a generating AI, and the generating AI can determine the priority of the judgment based on the submission timing.

[0104] The estimation unit can estimate the patient's emotions and adjust the estimation speed based on the estimated emotions. For example, if the patient is feeling anxious, the estimation unit can slow down the estimation speed to give the patient a sense of security. For example, if the patient is relaxed, the estimation unit can maintain a normal estimation speed. For example, if the patient is in a hurry, the estimation unit can speed up the estimation speed to perform the estimation quickly. This allows the patient to feel secure by adjusting the estimation speed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the patient's facial expression data into the generative AI, which can estimate the patient's emotions and adjust the estimation speed.

[0105] The estimation unit can optimize the estimation content by referring to the patient's past medical resource utilization history during estimation. For example, the estimation unit can optimize the current estimation content by referring to the patient's past medical resource utilization history. For example, the estimation unit can estimate the optimal medical resource based on the patient's past medical resource utilization history. For example, the estimation unit can analyze the patient's past medical resource utilization history and propose the optimal estimation method. This allows the estimation content to be optimized by referring to the patient's past medical resource utilization history. Some or all of the above processing in the estimation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the estimation unit can input the patient's past medical resource utilization history data into a generating AI, and the generating AI can propose the optimal estimation method.

[0106] The estimation unit can estimate the patient's emotions and determine the estimation priority based on the estimated emotions. For example, if the patient is experiencing severe pain, the estimation unit can set a higher estimation priority. For example, if the patient is feeling anxious, the estimation unit can set a higher estimation priority. For example, if the patient is relaxed, the estimation unit can set the estimation priority as usual. This allows for rapid estimation by determining the estimation priority according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the patient's facial expression data into the generative AI, which can then estimate the patient's emotions and determine the estimation priority.

[0107] The estimation unit can adjust the estimation content by considering the patient's geographical location information during estimation. For example, the estimation unit can adjust the estimation content by considering the medical resources in the area where the patient lives. For example, the estimation unit can adjust the estimation content by considering the environment (e.g., climate and pollution) in the area where the patient lives. For example, the estimation unit can adjust the estimation content by considering the infectious disease epidemic situation in the area where the patient lives. In this way, the estimation content can be adjusted by considering the patient's geographical location information. Some or all of the above processing in the estimation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the estimation unit can input the patient's geographical location information data into a generating AI, and the generating AI can adjust the estimation content based on the geographical location information.

[0108] The management unit can estimate the patient's emotions and adjust the pace of management based on the estimated emotions. For example, if the patient is feeling anxious, the management unit can slow down the pace of management to make the patient feel at ease. For example, if the patient is relaxed, the management unit can maintain a normal pace of management. For example, if the patient is in a hurry, the management unit can speed up the pace of management to perform the management quickly. In this way, by adjusting the pace of management according to the patient's emotions, a sense of security can be provided to the patient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input patient facial expression data into a generative AI, which can estimate the patient's emotions and adjust the pace of management.

[0109] The management department can optimize management practices by referring to the hospital's past resource management history during management. For example, the management department can optimize current management practices by referring to the hospital's past resource management history. For example, the management department can propose the optimal resource management method based on the hospital's past resource management history. For example, the management department can analyze the hospital's past resource management history and derive the optimal management method. This allows for the optimization of management practices by referring to the hospital's past resource management history. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department can input the hospital's past resource management history data into a generative AI, which can then propose the optimal management method.

[0110] The management unit can estimate the patient's emotions and determine management priorities based on the estimated emotions. For example, if the patient is experiencing severe pain, the management unit can set a higher management priority. For example, if the patient is feeling anxious, the management unit can set a higher management priority. For example, if the patient is relaxed, the management unit can set the management priority to normal. This allows for rapid management by determining management priorities according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input patient facial expression data into a generative AI, which can estimate the patient's emotions and determine management priorities.

[0111] The management department can adjust management procedures by considering the geographical location information of hospitals during management. For example, the management department can prioritize selecting the nearest hospital based on the geographical location information of hospitals. For example, the management department can select the most suitable hospital based on traffic conditions, taking into account the geographical location information of hospitals. For example, the management department can select a hospital that minimizes patient travel time based on the geographical location information of hospitals. In this way, management procedures can be adjusted by considering the geographical location information of hospitals. Some or all of the above-described processes in the management department may be performed using, for example, a generating AI, or without using a generating AI. For example, the management department can input hospital geographical location data into a generating AI, and the generating AI can adjust management procedures based on the geographical location information.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The examination department can estimate the patient's emotions and adjust the pace of the examination based on those estimates. For example, if the patient is feeling anxious, the pace of the examination can be slowed down to help the patient feel at ease. If the patient is relaxed, the pace of the examination can be kept at a normal rate. If the patient is in a hurry, the pace of the examination can be sped up to conduct the examination quickly. In this way, by adjusting the pace of the examination according to the patient's emotions, a sense of security can be provided to the patient.

[0114] The consultation department can optimize the consultation by referring to the patient's past medical history. For example, it can refer to the patient's past medical history and prioritize displaying information relevant to the current symptoms. Based on the patient's past treatment history, it can suggest the most appropriate consultation method. It can also refer to the patient's past allergy information and adjust the consultation accordingly. In this way, the consultation can be optimized by referring to the patient's past medical history.

[0115] The medical department can adjust the examination results by considering the patient's current living situation and environmental information. For example, the examination results can be adjusted by considering the patient's current living environment (housing situation and work environment). The examination results can be adjusted by considering the patient's current lifestyle habits (eating and exercise habits). The examination results can be adjusted by considering the patient's current stress level. In this way, the examination results can be adjusted by considering the patient's current living situation and environmental information.

[0116] The medical department can estimate the patient's emotions and determine the priority of the examination based on those estimates. For example, if the patient is experiencing severe pain, the examination can be given a higher priority. If the patient is feeling anxious, the examination can be given a higher priority. If the patient is relaxed, the examination can be given a normal priority. This allows for faster examinations by determining the priority of the examination according to the patient's emotions.

[0117] The medical department can adjust the content of the examination considering the patient's geographical location. For example, the content of the examination can be adjusted considering the medical resources in the area where the patient lives. The content of the examination can be adjusted considering the environment (climate and pollution) in the area where the patient lives. The content of the examination can be adjusted considering the infectious disease epidemic situation in the area where the patient lives. In this way, the content of the examination can be adjusted by considering the patient's geographical location.

[0118] The clinical department can analyze patients' social media activity and obtain relevant clinical information. For example, they can extract health-related information from patients' social media posts and use it to aid in consultations. They can analyze patients' social media friendships and consider their impact on health. They can analyze patients' social media activity patterns and obtain information about their lifestyle. In short, by analyzing patients' social media activity, relevant clinical information can be obtained.

[0119] The decision-making unit can estimate the patient's emotions and adjust the way the decision is expressed based on those emotions. For example, if the patient is feeling anxious, the decision can be conveyed in gentle language. If the patient is relaxed, the decision can be conveyed in normal language. If the patient is in a hurry, the decision can be conveyed concisely. In this way, by adjusting the way the decision is expressed according to the patient's emotions, a sense of reassurance can be given to the patient.

[0120] The judgment unit can adjust the level of detail in its judgment based on the severity of the patient's symptoms. For example, if the patient's symptoms are severe, it can provide a detailed judgment result. If the patient's symptoms are mild, it can provide a concise judgment result. If the patient's symptoms are moderate, it can provide a judgment result with an appropriate level of detail. In this way, by adjusting the level of detail in the judgment based on the severity of the patient's symptoms, it is possible to provide an appropriate judgment result.

[0121] The decision-making unit can apply different decision algorithms depending on the patient's medical history. For example, if the patient has a history of heart disease, a heart disease-specific algorithm can be applied. If the patient has a history of diabetes, a diabetes-specific algorithm can be applied. If the patient has a history of allergies, an allergy-specific algorithm can be applied. By applying different decision algorithms according to the patient's history, the system can provide appropriate decision results.

[0122] The decision-making unit can estimate the patient's emotions and determine the priority of decisions based on those emotions. For example, if the patient is experiencing severe pain, the decision priority can be set higher. If the patient is feeling anxious, the decision priority can also be set higher. If the patient is relaxed, the decision priority can be set normally. This allows for quick decision-making by determining the priority of decisions according to the patient's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The examination unit examines the patient's condition. The examination unit can assess the patient's condition based on, for example, surrounding audio and video. It can also measure and analyze the patient's vital signs (heart rate, blood pressure, body temperature, etc.) in real time. Furthermore, the examination unit can automatically determine the severity of the patient's condition and estimate the necessary medical resources (number of medical devices, medications, and medical staff). Step 2: The decision-making unit determines whether the hospital can accept the patient based on the information gathered by the examination unit. The decision-making unit can determine whether acceptance is possible by considering, for example, the hospital's resource status, the allocation of medical staff, the bed occupancy status, and the operational status of medical equipment. The decision-making unit can grasp the hospital's resource status in real time and quickly determine whether acceptance is possible. Step 3: The Negotiation Department conducts negotiations with multiple agents simultaneously based on the decision-making results obtained by the Decision-Making Department. By negotiating with agents from multiple hospitals simultaneously, for example, the Negotiation Department can quickly determine the optimal destination for transport. This significantly reduces the time required to determine the destination for transport.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the examination unit, judgment unit, negotiation unit, measurement unit, determination unit, estimation unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the examination unit examines the patient's condition using the camera 42 and microphone 38B of the smart device 14 and measures and analyzes vital signs using the control unit 46A. The judgment unit determines whether the hospital can accept the patient using the specific processing unit 290 of the data processing unit 12. The negotiation unit negotiates with agents from multiple hospitals simultaneously using the specific processing unit 290 of the data processing unit 12. The measurement unit automatically measures vital signs using the control unit 46A of the smart device 14 and issues an alert if there is an abnormality. The determination unit automatically determines the severity of the condition using the specific processing unit 290 of the data processing unit 12. The estimation unit estimates the necessary medical resources using the specific processing unit 290 of the data processing unit 12. The management unit manages the allocation of medical staff, the occupancy rate of hospital beds, and the operating status of medical equipment using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the examination unit, judgment unit, negotiation unit, measurement unit, determination unit, estimation unit, and management unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the examination unit examines the patient's condition using the camera 42 and microphone 238 of the smart glasses 214 and measures and analyzes vital signs using the control unit 46A. The judgment unit determines whether the hospital can accept the patient using the identification processing unit 290 of the data processing unit 12. The negotiation unit negotiates with agents from multiple hospitals simultaneously using the identification processing unit 290 of the data processing unit 12. The measurement unit automatically measures vital signs using the control unit 46A of the smart glasses 214 and issues an alert if there is an abnormality. The determination unit automatically determines the severity of the condition using the identification processing unit 290 of the data processing unit 12. The estimation unit estimates the necessary medical resources using the identification processing unit 290 of the data processing unit 12. The management unit manages the allocation of medical staff, the occupancy rate of hospital beds, and the operating status of medical equipment using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the examination unit, judgment unit, negotiation unit, measurement unit, determination unit, estimation unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the examination unit examines the patient's condition using the camera 42 and microphone 238 of the headset terminal 314 and measures and analyzes vital signs using the control unit 46A. The judgment unit determines whether the hospital can accept the patient using the identification processing unit 290 of the data processing unit 12. The negotiation unit negotiates with agents from multiple hospitals simultaneously using the identification processing unit 290 of the data processing unit 12. The measurement unit automatically measures vital signs using the control unit 46A of the headset terminal 314 and issues an alert if there is an abnormality. The determination unit automatically determines the severity of the condition using the identification processing unit 290 of the data processing unit 12. The estimation unit estimates the necessary medical resources using the identification processing unit 290 of the data processing unit 12. The management unit manages the allocation status of medical staff, the occupancy status of hospital beds, and the operating status of medical equipment using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the equipment and control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the examination unit, judgment unit, negotiation unit, measurement unit, determination unit, estimation unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the examination unit examines the patient's condition using the camera 42 and microphone 238 of the robot 414 and measures and analyzes vital signs using the control unit 46A. The judgment unit determines whether the hospital can accept the patient using the specific processing unit 290 of the data processing unit 12. The negotiation unit negotiates with agents from multiple hospitals simultaneously using the specific processing unit 290 of the data processing unit 12. The measurement unit automatically measures vital signs using the control unit 46A of the robot 414 and issues an alert if there is an abnormality. The determination unit automatically determines the severity of the condition using the specific processing unit 290 of the data processing unit 12. The estimation unit estimates the necessary medical resources using the specific processing unit 290 of the data processing unit 12. The management unit manages the deployment status of medical staff, the occupancy status of hospital beds, and the operating status of medical equipment using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The examination department examines the patient's condition, Based on the information obtained by the aforementioned examination unit, a determination unit determines whether or not the hospital can accept the patient, The system comprises a negotiation unit in which multiple agents negotiate simultaneously based on the judgment results obtained by the aforementioned determination unit. A system characterized by the following features. (Note 2) It is equipped with a measurement unit that performs automatic measurement and analysis of vital signs. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a determination unit that automatically determines the severity of the condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an estimation unit that estimates the necessary medical resources. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a management department that manages the allocation of medical staff. The system described in Appendix 1, characterized by the features described herein. (Note 6) It has a management department that manages the status of hospital bed occupancy. The system described in Appendix 1, characterized by the features described herein. (Note 7) It has a management department that manages the operating status of medical equipment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned examination department is, The system estimates the patient's emotions and adjusts the pace of the consultation based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned examination department is, During the consultation, we optimize the consultation content by referring to the patient's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned examination department is, During the examination, the results of the examination will be adjusted by taking into account the patient's current living situation and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned examination department is, The system estimates the patient's emotions and determines the priority of consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned examination department is, During the consultation, we adjust the consultation content taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned examination department is, During consultations, we analyze the patient's social media activity and obtain relevant medical information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, The system estimates the patient's emotions and adjusts the way decisions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When making a decision, adjust the level of detail based on the severity of the patient's symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, When making a decision, different decision algorithms are applied depending on the patient's medical history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The unit that makes the determination said, The system estimates the patient's emotions and determines the priority of decisions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The unit that makes the determination said, When making a decision, the priority of the decision is determined based on when the patient submitted the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The unit that makes the determination said, When making a decision, adjust the order of decisions based on the patient's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, The system estimates the patient's emotions and adjusts the pace of negotiations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, During negotiations, refer to the hospital's past admission history to optimize the negotiation strategy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, During negotiations, adjust the negotiation results to take into account the hospital's current resource situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, During negotiations, we adjust the negotiation terms while taking into account the hospital's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, During negotiations, we analyze the hospital's social media activity and obtain relevant negotiation information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned measuring unit is The system estimates the patient's emotions and adjusts the measurement speed based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned measuring unit is During measurement, the measurement content is optimized by referring to the patient's past vital sign history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned measuring unit is The system estimates the patient's emotions and determines the priority of measurements based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned measuring unit is During measurement, the measurement content is adjusted to take into account the patient's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The determination unit, The system estimates the patient's emotions and adjusts the way the judgment is expressed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The determination unit, During the assessment, the level of detail in the assessment is adjusted based on the severity of the patient's symptoms. The system described in Appendix 3, characterized by the features described herein. (Note 31) The determination unit, The system estimates the patient's emotions and determines the priority of decisions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The determination unit, When making a decision, the priority of the decision will be determined based on when the patient submitted their report. The system described in Appendix 3, characterized by the features described herein. (Note 33) The estimation unit, The system estimates the patient's emotions and adjusts the rate of estimation based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The estimation unit, During estimation, the patient's past medical resource usage history is referenced to optimize the estimation. The system described in Appendix 4, characterized by the features described herein. (Note 35) The estimation unit, The system estimates the patient's emotions and determines the priority of the estimations based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The estimation unit, During estimation, the estimation results are adjusted to take into account the patient's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned management department, The system estimates the patient's emotions and adjusts the pace of management based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned management department, During management, optimize management by referring to the hospital's past resource management history. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned management department, The system estimates the patient's emotions and determines management priorities based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned management department, During management, the management content is adjusted taking into account the hospital's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The examination department examines the patient's condition, Based on the information obtained by the aforementioned examination unit, a determination unit determines whether or not the hospital can accept the patient, The system comprises a negotiation unit in which multiple agents negotiate simultaneously based on the judgment results obtained by the aforementioned determination unit. A system characterized by the following features.

2. It is equipped with a measurement unit that automatically measures and analyzes vital signs. The system according to feature 1.

3. It is equipped with a determination unit that automatically determines the severity of the condition. The system according to feature 1.

4. It includes an estimation unit that estimates the necessary medical resources. The system according to feature 1.

5. It has a management department that manages the allocation of medical staff. The system according to feature 1.

6. It has a management department that manages the status of hospital bed occupancy. The system according to feature 1.

7. It has a management department that manages the operating status of medical equipment. The system according to feature 1.

8. The aforementioned examination department is, The system estimates the patient's emotions and adjusts the pace of the consultation based on those estimates. The system according to feature 1.

9. The aforementioned examination department is, During the consultation, we optimize the consultation content by referring to the patient's past medical history. The system according to feature 1.

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