system

Abstract

The system according to this embodiment aims to improve the efficiency of medical care by optimally allocating resources based on the patient's symptoms and medical history. [Solution] The system according to the embodiment comprises an evaluation unit, an allocation unit, a prediction unit, and a navigation unit. The evaluation unit evaluates the patient's symptoms and medical history. The allocation unit allocates resources in real time based on the information evaluated by the evaluation unit. The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. The navigation unit performs navigation based on the information predicted by the prediction unit.

Description

Technical Field

[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 character of the chatbot, 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 optimal allocation of resources based on the symptoms and medical history of patients has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to optimally allocate resources based on the symptoms and medical history of patients and improve the efficiency of medical treatment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an evaluation unit, an allocation unit, a prediction unit, and a navigation unit. The evaluation unit evaluates the patient's symptoms and medical history. The allocation unit allocates resources in real time based on the information evaluated by the evaluation unit. The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. The navigation unit performs navigation based on the information predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize resource allocation based on the patient's symptoms and medical history, thereby improving the efficiency of medical care. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI-driven medical triage system according to an embodiment of the present invention is an advanced platform designed to revolutionize patient care in the Japanese healthcare system. This system leverages state-of-the-art artificial intelligence and machine learning algorithms to efficiently prioritize and route medical cases based on severity, available resources, and optimal treatment pathways. The platform aims to significantly reduce waiting times, improve patient outcomes, and optimize the allocation of medical resources across hospitals and clinics throughout Japan. For example, the AI-driven medical triage system includes an assessment unit for rapidly evaluating a patient's symptoms and medical history. The assessment unit uses AI to analyze the patient's symptoms and medical history and assess severity. Next, the allocation unit optimally allocates medical staff and facility resources in real time based on the information assessed by the assessment unit. For example, the allocation unit uses AI to analyze medical staff schedules and facility utilization to optimize resource allocation. Furthermore, the prediction unit predicts patient influx and resource needs based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze historical data and external factors to predict future patient influx and resource needs. Finally, the navigation unit optimizes the patient's route based on the information predicted by the prediction unit. For example, the navigation unit uses AI to analyze the patient's location and the congestion status of medical facilities to provide the optimal route. As a result, the AI-driven medical triage system can efficiently deliver medical care by evaluating the patient's symptoms and medical history, allocating resources in real time, performing predictive analysis, and providing navigation.

[0029] The AI-driven medical triage system according to this embodiment comprises an evaluation unit, an allocation unit, a prediction unit, and a navigation unit. The evaluation unit evaluates the patient's symptoms and medical history. For example, the evaluation unit uses AI to analyze the patient's symptoms and medical history and evaluate the severity. For example, the evaluation unit receives the patient's symptoms as input, the AI ​​analyzes the symptoms, and evaluates the severity. The evaluation unit can also receive the patient's medical history as input, the AI ​​analyzes the history, and evaluate the severity. Furthermore, the evaluation unit can analyze the patient's symptoms and medical history in combination to evaluate the severity. The allocation unit allocates resources in real time based on the information evaluated by the evaluation unit. For example, the allocation unit uses AI to analyze the schedules of medical staff and the utilization status of facilities and makes the optimal resource allocation. For example, the allocation unit receives the schedules of medical staff as input, the AI ​​analyzes the schedules, and makes the optimal resource allocation. Furthermore, the allocation unit can also receive the utilization status of facilities as input, the AI ​​analyzes the utilization status, and makes the optimal resource allocation. Furthermore, the allocation unit can analyze a combination of medical staff schedules and facility utilization to determine the optimal resource allocation. The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze past data and external factors to predict future patient influx and resource needs. The prediction unit can, for example, receive past data as input, and the AI ​​analyzes that data to predict future patient influx and resource needs. The prediction unit can also receive external factors as input, and the AI ​​analyzes those factors to predict future patient influx and resource needs. Furthermore, the prediction unit can combine past data and external factors to predict future patient influx and resource needs. The navigation unit provides navigation based on the information predicted by the prediction unit. For example, the navigation unit uses AI to analyze patient location information and the congestion status of medical facilities to provide the optimal route. The navigation unit can, for example, receive patient location information as input, and the AI ​​analyzes that information to provide the optimal route.Furthermore, the navigation unit can receive the congestion status of medical facilities as input, and the AI ​​can analyze that status and provide the optimal route. In addition, the navigation unit can combine and analyze the patient's location information and the congestion status of medical facilities to provide the optimal route. As a result, the AI-driven medical triage system according to this embodiment can provide efficient medical care by evaluating the patient's symptoms and medical history, allocating resources in real time, performing predictive analysis, and providing navigation.

[0030] The evaluation department assesses the patient's symptoms and medical history. For example, it uses AI to analyze the patient's symptoms and medical history and assess the severity. Specifically, when a patient arrives at the hospital, they are first provided with an interface to input their symptoms in detail. This interface is accessible through devices such as tablets and smartphones, and can be used by the patient themselves or medical staff to input the information. The entered symptom data is sent to the AI ​​system and analyzed using natural language processing technology. The AI ​​compares this data with a database of past cases to identify symptom patterns and assess the severity. For example, if symptoms such as chest pain and shortness of breath are entered, the AI ​​assesses the likelihood that these symptoms are related to serious illnesses such as myocardial infarction or pneumonia. Similarly, the patient's medical history is also entered, and the AI ​​analyzes this history. The medical history includes past diagnoses, prescribed medications, and surgical history, and this information is automatically retrieved from the electronic medical record system. Based on this historical data, the AI ​​evaluates the relevance to the current symptoms and further refines the assessment of severity. For example, if a patient who has been diagnosed with heart disease in the past complains of chest pain again, the AI ​​will rate the risk higher. Furthermore, the evaluation unit analyzes the patient's symptoms and medical history in combination to comprehensively assess the severity of their condition. This allows the evaluation unit to quickly and accurately understand the patient's condition and perform appropriate triage.

[0031] The allocation unit distributes resources in real time based on information evaluated by the evaluation unit. For example, the allocation unit uses AI to analyze the schedules of medical staff and the utilization of facilities to make optimal resource allocations. Specifically, it works in conjunction with the medical staff schedule management system to make optimal placements considering each staff member's work status and area of ​​expertise. For example, if a critically ill patient arrives, the AI ​​will quickly assign the most suitable specialist to that patient. Facility utilization is also monitored in real time, and the availability of hospital beds and the usage of examination rooms are constantly updated. Based on this information, the AI ​​guides patients to the appropriate examination rooms or hospital beds. Furthermore, the allocation unit can also combine and analyze the schedules of medical staff and facility utilization to make optimal resource allocations. For example, if there is a large number of emergency cases, the AI ​​will quickly reallocate resources and assign additional medical staff as needed. It will also adjust the use schedule of examination rooms according to facility utilization to ensure efficient medical care. In this way, the allocation unit can optimally allocate medical resources, minimize patient waiting times, and improve the efficiency of medical care delivery.

[0032] The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze historical data and external factors to predict future patient influx and resource needs. Specifically, it analyzes seasonal fluctuations in patient numbers and trends in the occurrence of specific diseases based on historical patient data. For example, since the number of patients tends to increase during influenza outbreaks, the AI ​​takes this into account when predicting resource allocation. External factors such as weather data and local event information are also considered. For example, if a large-scale event is held, the risk of emergency cases increases, so the AI ​​predicts this and secures the necessary resources in advance. Furthermore, the prediction unit can analyze historical data and external factors in combination to predict future patient influx and resource needs with greater accuracy. For example, if historical data shows a tendency for the number of patients to increase on specific days of the week or time slots, the AI ​​adjusts resource allocation based on this. This allows the prediction unit to accurately predict future resource needs and ensure thorough preparation for healthcare delivery.

[0033] The navigation unit provides navigation based on information predicted by the prediction unit. For example, the navigation unit uses AI to analyze the patient's location and the congestion status of medical facilities to provide the optimal route. Specifically, when a patient is heading to a hospital, it uses the location information from their smartphone to guide them along the best route. The AI ​​analyzes traffic information and road congestion in real time to calculate the route that will get them there in the shortest time. It also monitors the congestion status within medical facilities in real time, and when a patient arrives, it guides them to the least crowded examination room or waiting room. Furthermore, the navigation unit can also analyze the patient's location information and the congestion status of medical facilities in combination to provide the optimal route. For example, if there are multiple medical facilities, the AI ​​compares the congestion status of each facility and guides the patient to the facility where they can be seen most quickly. It also guides the patient along the shortest route when they move around within the hospital, minimizing waiting times. In this way, the navigation unit can support patients in receiving medical services quickly and efficiently, improving the efficiency of medical delivery.

[0034] The multilingual support unit performs multilingual support. The multilingual support unit can, for example, use AI to support patients who speak different languages. The multilingual support unit can, for example, use translation software to automatically translate the patient's language. The multilingual support unit can also recognize the patient's language using speech recognition technology and translate it into the appropriate language. Furthermore, the multilingual support unit can analyze the patient's language using text analysis technology and translate it into the appropriate language. This enables the multilingual support unit to support patients who speak different languages. Some or all of the above-described processes in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the patient's language data into a generating AI and have the generating AI perform language translation.

[0035] The telemedicine department integrates telemedicine. For example, the telemedicine department uses AI to enable patients to receive medical services even from remote locations. For example, the telemedicine department connects patients and medical staff using video call technology. The telemedicine department can also transmit patient health data to medical staff in real time using telemedicine technology. Furthermore, the telemedicine department can monitor the patient's health status using remote monitoring technology and notify medical staff as needed. This enables the telemedicine department to enable patients to receive medical services even from remote locations. Some or all of the above processes in the telemedicine department may be performed using AI, for example, or not using AI. For example, the telemedicine department can input patient health data into a generating AI and have the generating AI perform data analysis.

[0036] The evaluation unit can analyze a patient's past treatment outcomes to improve the accuracy of the evaluation. For example, the evaluation unit can assess the relevance of a patient's past treatment outcomes to their current symptoms. For example, the evaluation unit can receive a patient's treatment history as input, and the AI ​​analyzes that history and assesses its relevance to the current symptoms. The evaluation unit can also evaluate the effectiveness of a specific treatment method based on past treatment outcomes. For example, the evaluation unit can analyze the success rate of a specific treatment method and evaluate its effectiveness. Furthermore, the evaluation unit can analyze a patient's past treatment outcomes and propose the optimal treatment method. For example, the evaluation unit can propose the optimal treatment method for the current symptoms based on past treatment outcomes. This improves the accuracy of the evaluation by analyzing past treatment outcomes. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input patient treatment history data into a generating AI and have the generating AI perform the data analysis.

[0037] The evaluation unit can incorporate patients' lifestyle data and reflect it in the evaluation. For example, the evaluation unit can reflect patients' dietary and exercise habits in the evaluation. For example, the evaluation unit can receive patients' dietary data as input, have the AI ​​analyze that data, and reflect it in the evaluation. The evaluation unit can also receive patients' exercise habit data as input, have the AI ​​analyze that data, and reflect it in the evaluation. Furthermore, the evaluation unit can incorporate patients' sleep patterns into the evaluation. For example, the evaluation unit can receive patients' sleep data as input, have the AI ​​analyze that data, and reflect it in the evaluation. The evaluation unit can also reflect patients' stress levels in the evaluation. For example, the evaluation unit can receive patients' stress data as input, have the AI ​​analyze that data, and reflect it in the evaluation. This allows for a more comprehensive evaluation by incorporating lifestyle data. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input patients' lifestyle data into a generating AI and have the generating AI perform the data analysis.

[0038] The evaluation unit can take the patient's geographical location into consideration and reflect region-specific health risks in its evaluation. For example, the evaluation unit can reflect the infectious disease risk in the area where the patient lives in its evaluation. For example, the evaluation unit can receive the patient's geographical location as input, have the AI ​​analyze that information, and reflect region-specific health risks in its evaluation. The evaluation unit can also incorporate environmental factors in the area where the patient lives into its evaluation. For example, the evaluation unit can receive the patient's geographical location as input, have the AI ​​analyze that information, and reflect environmental factors in its evaluation. Furthermore, the evaluation unit can also reflect the utilization of medical resources in the area where the patient lives in its evaluation. For example, the evaluation unit can receive the patient's geographical location as input, have the AI ​​analyze that information, and reflect the utilization of medical resources in its evaluation. In this way, by considering geographical location information, region-specific health risks can be reflected in the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the patient's geographical location into a generating AI and have the generating AI perform the data analysis.

[0039] The evaluation unit can analyze patients' social media activity and incorporate health-related information into the evaluation. For example, the evaluation unit can extract health-related information from patients' social media posts. For example, the evaluation unit can receive patients' social media activity as input, have an AI analyze that activity, and extract health-related information. The evaluation unit can also assess stress levels from patients' social media activity. For example, the evaluation unit can receive patients' social media activity as input, have an AI analyze that activity, and assess stress levels. Furthermore, the evaluation unit can reflect lifestyle habits in the evaluation from patients' social media activity. For example, the evaluation unit can receive patients' social media activity as input, have an AI analyze that activity, and reflect lifestyle habits in the evaluation. This allows health-related information to be incorporated into the evaluation by analyzing social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input patients' social media data into a generating AI and have the generating AI perform the data analysis.

[0040] The allocation unit can analyze past resource usage data to improve the efficiency of allocation. For example, the allocation unit can propose an optimal resource allocation based on past resource usage data. For example, the allocation unit can receive past resource usage data as input, have an AI analyze the data, and propose an optimal resource allocation. The allocation unit can also reduce wasteful resource allocations from past resource usage data. For example, the allocation unit can receive past resource usage data as input, have an AI analyze the data, and reduce wasteful resource allocations. Furthermore, the allocation unit can analyze past resource usage data to perform efficient resource allocation. For example, the allocation unit can receive past resource usage data as input, have an AI analyze the data, and perform efficient resource allocation. This improves the efficiency of allocation by analyzing past resource usage data. Some or all of the above processes in the allocation unit may be performed using an AI, or not. For example, the allocation unit can input past resource usage data into a generating AI and have the generating AI perform the data analysis.

[0041] The allocation unit can make optimal resource allocations by considering the skill sets of medical staff. For example, the allocation unit can allocate resources based on the medical staff's area of ​​expertise. For example, the allocation unit can receive the medical staff's skill sets as input, have the AI ​​analyze those skill sets, and make optimal resource allocations. The allocation unit can also allocate resources considering the medical staff's years of experience. For example, the allocation unit can receive the medical staff's years of experience as input, have the AI ​​analyze those years of experience, and make optimal resource allocations. Furthermore, the allocation unit can evaluate the medical staff's skill sets and make optimal resource allocations. For example, the allocation unit can receive the medical staff's skill sets as input, have the AI ​​evaluate those skill sets, and make optimal resource allocations. This makes optimal resource allocation possible by considering the medical staff's skill sets. Some or all of the above processes in the allocation unit may be performed using AI, or not using AI. For example, the allocation unit can input the medical staff's skill set data into a generating AI and have the generating AI perform the data analysis.

[0042] The allocation unit can perform optimal resource allocation by considering the geographical location information of medical facilities. For example, the allocation unit can propose an optimal resource allocation based on the geographical location information of medical facilities. For example, the allocation unit can receive geographical location information of medical facilities as input, have an AI analyze that information, and propose an optimal resource allocation. The allocation unit can also reduce resource waste based on the geographical location information of medical facilities. For example, the allocation unit can receive geographical location information of medical facilities as input, have an AI analyze that information, and reduce resource waste. Furthermore, the allocation unit can perform efficient resource allocation by considering the geographical location information of medical facilities. For example, the allocation unit can receive geographical location information of medical facilities as input, have an AI analyze that information, and perform efficient resource allocation. This makes optimal resource allocation possible by considering geographical location information. Some or all of the above processing in the allocation unit may be performed using an AI, or not using an AI. For example, the allocation unit can input geographical location information of medical facilities into a generating AI and have the generating AI perform data analysis.

[0043] The allocation unit can monitor the work status of medical staff in real time and dynamically adjust the allocation. For example, the allocation unit can monitor the work status of medical staff in real time and make the optimal resource allocation. For example, the allocation unit can receive the work status of medical staff as input, have an AI analyze the situation, and make the optimal resource allocation. The allocation unit can also reduce wasteful resource allocation based on the work status of medical staff. For example, the allocation unit can receive the work status of medical staff as input, have an AI analyze the situation, and reduce wasteful resource allocation. Furthermore, the allocation unit can make efficient resource allocation by taking the work status of medical staff into consideration. For example, the allocation unit can receive the work status of medical staff as input, have an AI analyze the situation, and make efficient resource allocation. This enables dynamic resource allocation by monitoring the work status in real time. Some or all of the above processing in the allocation unit may be performed using an AI, or not. For example, the allocation unit can input medical staff work status data into a generating AI and have the generating AI perform the data analysis.

[0044] The prediction unit can analyze past patient data and optimize the prediction model. For example, the prediction unit can propose an optimal prediction model based on past patient data. For example, the prediction unit can receive past patient data as input, have the AI ​​analyze that data, and propose an optimal prediction model. The prediction unit can also improve the accuracy of the prediction model from past patient data. For example, the prediction unit can receive past patient data as input, have the AI ​​analyze that data, and improve the accuracy of the prediction model. Furthermore, the prediction unit can analyze past patient data and construct an efficient prediction model. For example, the prediction unit can receive past patient data as input, have the AI ​​analyze that data, and construct an efficient prediction model. This improves the accuracy of the prediction model by analyzing past patient data. Some or all of the above processes in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input past patient data into a generating AI and have the generating AI perform the data analysis.

[0045] The prediction unit can take in seasonal and weather data and reflect it in its predictions. For example, the prediction unit can optimize its prediction model by considering seasonal variations. For example, the prediction unit can receive seasonal data as input, have the AI ​​analyze that data, and optimize its prediction model. The prediction unit can also improve the accuracy of its prediction model by considering weather variations. For example, the prediction unit can receive weather data as input, have the AI ​​analyze that data, and improve the accuracy of its prediction model. Furthermore, the prediction unit can analyze seasonal and weather data to build an efficient prediction model. For example, the prediction unit can receive seasonal and weather data as input, have the AI ​​analyze that data, and build an efficient prediction model. This improves the accuracy of predictions by incorporating seasonal and weather data. Some or all of the above processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input seasonal and weather data into a generating AI and have the generating AI perform the data analysis.

[0046] The prediction unit can improve the accuracy of its predictions by considering local health data. For example, the prediction unit can propose an optimal prediction model based on local health data. For example, the prediction unit can receive local health data as input, have an AI analyze the data, and propose an optimal prediction model. The prediction unit can also improve the accuracy of its prediction model from local health data. For example, the prediction unit can receive local health data as input, have an AI analyze the data, and improve the accuracy of its prediction model. Furthermore, the prediction unit can analyze local health data and construct an efficient prediction model. For example, the prediction unit can receive local health data as input, have an AI analyze the data, and construct an efficient prediction model. This improves the accuracy of predictions by considering local health data. Some or all of the above processes in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input local health data into a generating AI and have the generating AI perform the data analysis.

[0047] The prediction unit can enhance its prediction model by referencing external health-related databases. For example, the prediction unit can propose an optimal prediction model based on an external health-related database. For example, the prediction unit can receive an external health-related database as input, have an AI analyze the data, and propose an optimal prediction model. The prediction unit can also improve the accuracy of its prediction model from external health-related databases. For example, the prediction unit can receive an external health-related database as input, have an AI analyze the data, and improve the accuracy of its prediction model. Furthermore, the prediction unit can analyze external health-related databases and build an efficient prediction model. For example, the prediction unit can receive an external health-related database as input, have an AI analyze the data, and build an efficient prediction model. This improves the accuracy of the prediction model by referencing external health-related databases. Some or all of the above processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input an external health-related database into a generating AI and have the generating AI perform the data analysis.

[0048] The navigation unit can analyze the patient's past travel history and propose the optimal route. For example, the navigation unit proposes the optimal route based on the patient's past travel history. For example, the navigation unit receives the patient's past travel history as input, the AI ​​analyzes the data, and proposes the optimal route. The navigation unit can also propose a route that avoids congestion based on the patient's past travel history. For example, the navigation unit receives the patient's past travel history as input, the AI ​​analyzes the data, and proposes a route that avoids congestion. Furthermore, the navigation unit can analyze the patient's past travel history and propose the most efficient route. For example, the navigation unit receives the patient's past travel history as input, the AI ​​analyzes the data, and proposes the most efficient route. In this way, the optimal route is proposed by analyzing past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the patient's past travel history data into a generating AI and have the generating AI perform the data analysis.

[0049] The navigation unit can monitor the congestion status of medical facilities in real time and dynamically adjust the route. For example, the navigation unit can monitor the congestion status of medical facilities in real time and propose the optimal route. For example, the navigation unit can receive the congestion status of medical facilities as input, the AI ​​analyzes the data, and propose the optimal route. The navigation unit can also reduce unnecessary routes based on the congestion status of medical facilities. For example, the navigation unit can receive the congestion status of medical facilities as input, the AI ​​analyzes the data, and reduce unnecessary routes. Furthermore, the navigation unit can propose an efficient route considering the congestion status of medical facilities. For example, the navigation unit can receive the congestion status of medical facilities as input, the AI ​​analyzes the data, and propose an efficient route. This enables dynamic route adjustment by monitoring congestion status in real time. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the congestion status data of medical facilities into a generating AI and have the generating AI perform the data analysis.

[0050] The navigation unit can propose the optimal route by considering geographical obstacles and traffic conditions. For example, the navigation unit can propose a route that avoids geographical obstacles. For example, the navigation unit can receive information on geographical obstacles as input, the AI ​​analyzes that information, and propose the optimal route. The navigation unit can also propose a route that avoids traffic congestion. For example, the navigation unit can receive information on traffic congestion as input, the AI ​​analyzes that information, and propose the optimal route. Furthermore, the navigation unit can propose a route that avoids road construction. For example, the navigation unit can receive information on road construction as input, the AI ​​analyzes that information, and propose the optimal route. In this way, the optimal route is proposed by considering geographical obstacles and traffic conditions. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input data on geographical obstacles and traffic conditions into a generating AI and have the generating AI perform the data analysis.

[0051] The navigation unit can provide an optimal navigation method by taking into account the patient's device information. For example, if the patient is using a smartphone, the navigation unit can provide a navigation method adapted to the screen size. For example, the navigation unit can receive the patient's device information as input, the AI ​​analyzes that information, and provide an optimal navigation method. The navigation unit can also provide a navigation method optimized for a larger screen if the patient is using a tablet. For example, the navigation unit can receive the patient's device information as input, the AI ​​analyzes that information, and provide an optimal navigation method. Furthermore, if the patient is using a smartwatch, the navigation unit can provide a concise and highly visible navigation method. For example, the navigation unit can receive the patient's device information as input, the AI ​​analyzes that information, and provide an optimal navigation method. In this way, an optimal navigation method is provided by taking device information into consideration. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the patient's device information into a generating AI and have the generating AI perform data analysis.

[0052] The multilingual support unit can analyze past multilingual support data to improve the accuracy of its responses. For example, the multilingual support unit can propose the optimal response method based on past multilingual support data. For example, the multilingual support unit can receive past multilingual support data as input, have an AI analyze that data, and propose the optimal response method. Furthermore, the multilingual support unit can improve the accuracy of its responses from past multilingual support data. For example, the multilingual support unit can receive past multilingual support data as input, have an AI analyze that data, and improve the accuracy of its responses. In addition, the multilingual support unit can analyze past multilingual support data and construct an efficient response method. For example, the multilingual support unit can receive past multilingual support data as input, have an AI analyze that data, and construct an efficient response method. As a result, the accuracy of the responses is improved by analyzing past multilingual support data. Some or all of the above processing in the multilingual support unit may be performed using an AI, or not. For example, the multilingual support unit can input past multilingual support data into a generating AI and have the generating AI perform the data analysis.

[0053] The multilingual support unit can provide optimal multilingual support based on the patient's language settings. For example, the multilingual support unit can provide multilingual support based on the language settings of the patient's device. For example, the multilingual support unit can receive the patient's device language settings as input, analyze the information with AI, and provide optimal multilingual support. The multilingual support unit can also provide a language switching function if the patient uses multiple languages. For example, the multilingual support unit can receive the patient's language settings as input, analyze the information with AI, and provide a language switching function. Furthermore, if the patient selects a specific language, the multilingual support unit can respond in that language. For example, the multilingual support unit can receive the patient's language settings as input, analyze the information with AI, and respond in that specific language. This enables optimal multilingual support based on language settings. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the patient's language setting data into a generating AI and have the generating AI perform the data analysis.

[0054] The multilingual support unit can perform optimal multilingual support by considering the regional language distribution. For example, the multilingual support unit proposes the optimal multilingual support based on the regional language distribution. For example, the multilingual support unit receives the regional language distribution as input, the AI ​​analyzes the data, and proposes the optimal multilingual support. Furthermore, the multilingual support unit can also improve the accuracy of its support based on the regional language distribution. For example, the multilingual support unit receives the regional language distribution as input, the AI ​​analyzes the data, and improves the accuracy of its support. In addition, the multilingual support unit can analyze the regional language distribution and construct an efficient multilingual support. For example, the multilingual support unit receives the regional language distribution as input, the AI ​​analyzes the data, and constructs an efficient multilingual support. This makes optimal multilingual support possible by considering the regional language distribution. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input regional language distribution data into a generating AI and have the generating AI perform the data analysis.

[0055] The multilingual support unit can evaluate the patient's language skills and provide optimal multilingual support. For example, the multilingual support unit can evaluate the patient's language skills and propose the optimal support method. For example, the multilingual support unit can receive the patient's language skills as input, have an AI analyze the data, and propose the optimal support method. The multilingual support unit can also improve the accuracy of its support based on the patient's language skills. For example, the multilingual support unit can receive the patient's language skills as input, have an AI analyze the data, and improve the accuracy of its support. Furthermore, the multilingual support unit can analyze the patient's language skills and construct efficient support methods. For example, the multilingual support unit can receive the patient's language skills as input, have an AI analyze the data, and construct efficient support methods. This enables optimal multilingual support by evaluating language skills. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the patient's language skills data into a generating AI and have the generating AI perform the data analysis.

[0056] The telemedicine department can analyze past telemedicine data to improve the accuracy of its responses. For example, the telemedicine department can propose the optimal response method based on past telemedicine data. For example, the telemedicine department can receive past telemedicine data as input, have an AI analyze that data, and propose the optimal response method. Furthermore, the telemedicine department can improve the accuracy of its responses from past telemedicine data. For example, the telemedicine department can receive past telemedicine data as input, have an AI analyze that data, and improve the accuracy of its responses. In addition, the telemedicine department can analyze past telemedicine data and construct efficient response methods. For example, the telemedicine department can receive past telemedicine data as input, have an AI analyze that data, and construct efficient response methods. As a result, the accuracy of responses is improved by analyzing past telemedicine data. Some or all of the above processes in the telemedicine department may be performed using AI, or not using AI. For example, the telemedicine department can input past telemedicine data into a generating AI and have the generating AI perform the data analysis.

[0057] The telemedicine department can monitor patients' health status in real time and dynamically adjust telemedicine responses. For example, the telemedicine department can monitor patients' health status in real time and propose the optimal response. For example, the telemedicine department can receive patient health status as input, have AI analyze the data, and propose the optimal response. The telemedicine department can also improve the accuracy of its responses based on the patient's health status. For example, the telemedicine department can receive patient health status as input, have AI analyze the data, and improve the accuracy of its responses. Furthermore, the telemedicine department can analyze patient health status and build efficient response methods. For example, the telemedicine department can receive patient health status as input, have AI analyze the data, and build efficient response methods. This enables dynamic responses by monitoring health status in real time. Some or all of the above processes in the telemedicine department may be performed using AI, or not. For example, the telemedicine department can input patient health status data into a generating AI and have the generating AI perform the data analysis.

[0058] The telemedicine department can provide optimal telemedicine support while considering geographical constraints. For example, the telemedicine department can propose the optimal response method while considering geographical constraints. For example, the telemedicine department can receive geographical constraint information as input, have an AI analyze that information, and propose the optimal response method. Furthermore, the telemedicine department can improve the accuracy of its response based on geographical constraints. For example, the telemedicine department can receive geographical constraint information as input, have an AI analyze that information, and improve the accuracy of its response. In addition, the telemedicine department can analyze geographical constraints and construct efficient response methods. For example, the telemedicine department can receive geographical constraint information as input, have an AI analyze that information, and construct efficient response methods. This makes it possible to provide optimal telemedicine support by considering geographical constraints. Some or all of the above processes in the telemedicine department may be performed using AI, or not. For example, the telemedicine department can input geographical constraint data into a generating AI and have the generating AI perform the data analysis.

[0059] The telemedicine department can provide optimal telemedicine support by considering the patient's internet connection status. For example, the telemedicine department can evaluate the patient's internet connection status and propose the optimal response method. For example, the telemedicine department can receive the patient's internet connection status as input, have an AI analyze that information, and propose the optimal response method. Furthermore, the telemedicine department can improve the accuracy of its responses based on the patient's internet connection status. For example, the telemedicine department can receive the patient's internet connection status as input, have an AI analyze that information, and improve the accuracy of its responses. In addition, the telemedicine department can analyze the patient's internet connection status and construct efficient response methods. For example, the telemedicine department can receive the patient's internet connection status as input, have an AI analyze that information, and construct efficient response methods. This makes it possible to provide optimal telemedicine support by considering the internet connection status. Some or all of the above processes in the telemedicine department may be performed using AI, or not. For example, the telemedicine department can input data on the patient's internet connection status into a generating AI and have the generating AI perform the data analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The evaluation unit can analyze a patient's past treatment outcomes to improve the accuracy of its evaluations. For example, it can evaluate the relevance of a patient's past treatment results to their current symptoms. The evaluation unit receives the patient's treatment history as input, and the AI ​​analyzes this history to evaluate its relevance to the current symptoms. The evaluation unit can also evaluate the effectiveness of specific treatments based on past treatment results. For example, it can analyze the success rate of a specific treatment and evaluate its effectiveness. Furthermore, the evaluation unit can analyze a patient's past treatment results and propose the optimal treatment. For example, it can propose the most suitable treatment for the current symptoms based on past treatment results. This improves the accuracy of evaluations by analyzing past treatment results.

[0062] The evaluation unit can incorporate patients' lifestyle data and reflect it in the evaluation. For example, it can reflect patients' dietary and exercise habits in the evaluation. The evaluation unit receives patients' dietary data as input, the AI ​​analyzes that data, and reflects it in the evaluation. The evaluation unit can also receive patients' exercise habit data as input, the AI ​​analyzes that data, and reflects it in the evaluation. Furthermore, the evaluation unit can incorporate patients' sleep patterns into the evaluation. For example, the evaluation unit receives patients' sleep data as input, the AI ​​analyzes that data, and reflects it in the evaluation. The evaluation unit can also reflect patients' stress levels in the evaluation. For example, the evaluation unit receives patients' stress data as input, the AI ​​analyzes that data, and reflects it in the evaluation. By incorporating lifestyle data, a more comprehensive evaluation becomes possible.

[0063] The evaluation unit can take into account the patient's geographical location and incorporate region-specific health risks into the evaluation. For example, it can incorporate the infectious disease risk in the patient's area of ​​residence into the evaluation. The evaluation unit receives the patient's geographical location as input, the AI ​​analyzes that information, and incorporates region-specific health risks into the evaluation. The evaluation unit can also incorporate environmental factors in the patient's area of ​​residence into the evaluation. For example, the evaluation unit receives the patient's geographical location as input, the AI ​​analyzes that information, and incorporates environmental factors into the evaluation. Furthermore, the evaluation unit can also incorporate the utilization of medical resources in the patient's area into the evaluation. For example, the evaluation unit receives the patient's geographical location as input, the AI ​​analyzes that information, and incorporates the utilization of medical resources into the evaluation. In this way, by considering geographical location, region-specific health risks can be incorporated into the evaluation.

[0064] The evaluation unit can analyze patients' social media activity and incorporate health-related information into the evaluation. For example, it can extract health-related information from patients' social media posts. The evaluation unit receives patients' social media activity as input, an AI analyzes that activity, and extracts health-related information. The evaluation unit can also assess stress levels from patients' social media activity. For example, the evaluation unit receives patients' social media activity as input, an AI analyzes that activity, and assesses stress levels. Furthermore, the evaluation unit can reflect lifestyle habits in the evaluation from patients' social media activity. For example, the evaluation unit receives patients' social media activity as input, an AI analyzes that activity, and reflects lifestyle habits in the evaluation. In this way, by analyzing social media activity, health-related information can be incorporated into the evaluation.

[0065] The allocation unit can analyze past resource usage data to improve the efficiency of resource allocation. For example, it can propose the optimal resource allocation based on past resource usage data. The allocation unit receives past resource usage data as input, the AI ​​analyzes that data, and proposes the optimal resource allocation. The allocation unit can also reduce wasteful resource allocation based on past resource usage data. For example, the allocation unit receives past resource usage data as input, the AI ​​analyzes that data, and reduces wasteful resource allocation. Furthermore, the allocation unit can analyze past resource usage data to perform efficient resource allocation. For example, the allocation unit receives past resource usage data as input, the AI ​​analyzes that data, and performs efficient resource allocation. In this way, the efficiency of resource allocation is improved by analyzing past resource usage data.

[0066] The allocation unit can optimize resource allocation by considering the skill sets of medical staff. For example, it can allocate resources based on the medical staff's area of ​​expertise. The allocation unit receives the medical staff's skill sets as input, the AI ​​analyzes those skill sets, and then optimizes resource allocation. The allocation unit can also allocate resources considering the medical staff's years of experience. For example, the allocation unit receives the medical staff's years of experience as input, the AI ​​analyzes those years of experience, and then optimizes resource allocation. Furthermore, the allocation unit can evaluate the medical staff's skill sets and then optimize resource allocation. For example, the allocation unit receives the medical staff's skill sets as input, the AI ​​evaluates those skill sets, and then optimizes resource allocation. This allows for optimal resource allocation by considering the medical staff's skill sets.

[0067] The prediction unit can analyze past patient data and optimize the prediction model. For example, it can propose an optimal prediction model based on past patient data. The prediction unit receives past patient data as input, the AI ​​analyzes that data, and proposes an optimal prediction model. The prediction unit can also improve the accuracy of the prediction model from past patient data. For example, the prediction unit receives past patient data as input, the AI ​​analyzes that data, and improves the accuracy of the prediction model. Furthermore, the prediction unit can analyze past patient data and build an efficient prediction model. For example, the prediction unit receives past patient data as input, the AI ​​analyzes that data, and builds an efficient prediction model. As a result, the accuracy of the prediction model is improved by analyzing past patient data.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The evaluation unit assesses the patient's symptoms and medical history. The evaluation unit, for example, uses AI to analyze the patient's symptoms and medical history and assess the severity. The evaluation unit receives the patient's symptoms as input, the AI ​​analyzes those symptoms, and assesses the severity. It can also receive the patient's medical history as input, the AI ​​analyzes that history, and assesses the severity. Furthermore, it can combine the patient's symptoms and medical history in its analysis to assess the severity. Step 2: The allocation unit allocates resources in real time based on the information evaluated by the evaluation unit. For example, the allocation unit uses AI to analyze the schedules of medical staff and the utilization status of facilities to make the optimal resource allocation. The allocation unit receives the schedules of medical staff as input, the AI ​​analyzes those schedules, and makes the optimal resource allocation. It can also receive the utilization status of facilities as input, the AI ​​analyzes that utilization status, and makes the optimal resource allocation. Furthermore, it can analyze the medical staff schedules and facility utilization status in combination to make the optimal resource allocation. Step 3: The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze historical data and external factors to predict future patient influx and resource needs. The prediction unit receives historical data as input, and the AI ​​analyzes that data to predict future patient influx and resource needs. It can also receive external factors as input, and the AI ​​analyzes those factors to predict future patient influx and resource needs. Furthermore, it can combine historical data and external factors in its analysis to predict future patient influx and resource needs. Step 4: The navigation unit performs navigation based on the information predicted by the prediction unit. For example, the navigation unit uses AI to analyze the patient's location information and the congestion status of medical facilities to provide the optimal route. The navigation unit receives the patient's location information as input, the AI ​​analyzes that information, and provides the optimal route. It can also receive the congestion status of medical facilities as input, the AI ​​analyzes that status, and provides the optimal route. Furthermore, it can combine and analyze the patient's location information and the congestion status of medical facilities to provide the optimal route.

[0070] (Example of form 2) An AI-driven medical triage system according to an embodiment of the present invention is an advanced platform designed to revolutionize patient care in the Japanese healthcare system. This system leverages state-of-the-art artificial intelligence and machine learning algorithms to efficiently prioritize and route medical cases based on severity, available resources, and optimal treatment pathways. The platform aims to significantly reduce waiting times, improve patient outcomes, and optimize the allocation of medical resources across hospitals and clinics throughout Japan. For example, the AI-driven medical triage system includes an assessment unit for rapidly evaluating a patient's symptoms and medical history. The assessment unit uses AI to analyze the patient's symptoms and medical history and assess severity. Next, the allocation unit optimally allocates medical staff and facility resources in real time based on the information assessed by the assessment unit. For example, the allocation unit uses AI to analyze medical staff schedules and facility utilization to optimize resource allocation. Furthermore, the prediction unit predicts patient influx and resource needs based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze historical data and external factors to predict future patient influx and resource needs. Finally, the navigation unit optimizes the patient's route based on the information predicted by the prediction unit. For example, the navigation unit uses AI to analyze the patient's location and the congestion status of medical facilities to provide the optimal route. As a result, the AI-driven medical triage system can efficiently deliver medical care by evaluating the patient's symptoms and medical history, allocating resources in real time, performing predictive analysis, and providing navigation.

[0071] The AI-driven medical triage system according to this embodiment comprises an evaluation unit, an allocation unit, a prediction unit, and a navigation unit. The evaluation unit evaluates the patient's symptoms and medical history. For example, the evaluation unit uses AI to analyze the patient's symptoms and medical history and evaluate the severity. For example, the evaluation unit receives the patient's symptoms as input, the AI ​​analyzes the symptoms, and evaluates the severity. The evaluation unit can also receive the patient's medical history as input, the AI ​​analyzes the history, and evaluate the severity. Furthermore, the evaluation unit can analyze the patient's symptoms and medical history in combination to evaluate the severity. The allocation unit allocates resources in real time based on the information evaluated by the evaluation unit. For example, the allocation unit uses AI to analyze the schedules of medical staff and the utilization status of facilities and makes the optimal resource allocation. For example, the allocation unit receives the schedules of medical staff as input, the AI ​​analyzes the schedules, and makes the optimal resource allocation. Furthermore, the allocation unit can also receive the utilization status of facilities as input, the AI ​​analyzes the utilization status, and makes the optimal resource allocation. Furthermore, the allocation unit can analyze a combination of medical staff schedules and facility utilization to determine the optimal resource allocation. The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze past data and external factors to predict future patient influx and resource needs. The prediction unit can, for example, receive past data as input, and the AI ​​analyzes that data to predict future patient influx and resource needs. The prediction unit can also receive external factors as input, and the AI ​​analyzes those factors to predict future patient influx and resource needs. Furthermore, the prediction unit can combine past data and external factors to predict future patient influx and resource needs. The navigation unit provides navigation based on the information predicted by the prediction unit. For example, the navigation unit uses AI to analyze patient location information and the congestion status of medical facilities to provide the optimal route. The navigation unit can, for example, receive patient location information as input, and the AI ​​analyzes that information to provide the optimal route.Furthermore, the navigation unit can receive the congestion status of medical facilities as input, and the AI ​​can analyze that status and provide the optimal route. In addition, the navigation unit can combine and analyze the patient's location information and the congestion status of medical facilities to provide the optimal route. As a result, the AI-driven medical triage system according to this embodiment can provide efficient medical care by evaluating the patient's symptoms and medical history, allocating resources in real time, performing predictive analysis, and providing navigation.

[0072] The evaluation department assesses the patient's symptoms and medical history. For example, it uses AI to analyze the patient's symptoms and medical history and assess the severity. Specifically, when a patient arrives at the hospital, they are first provided with an interface to input their symptoms in detail. This interface is accessible through devices such as tablets and smartphones, and can be used by the patient themselves or medical staff to input the information. The entered symptom data is sent to the AI ​​system and analyzed using natural language processing technology. The AI ​​compares this data with a database of past cases to identify symptom patterns and assess the severity. For example, if symptoms such as chest pain and shortness of breath are entered, the AI ​​assesses the likelihood that these symptoms are related to serious illnesses such as myocardial infarction or pneumonia. Similarly, the patient's medical history is also entered, and the AI ​​analyzes this history. The medical history includes past diagnoses, prescribed medications, and surgical history, and this information is automatically retrieved from the electronic medical record system. Based on this historical data, the AI ​​evaluates the relevance to the current symptoms and further refines the assessment of severity. For example, if a patient who has been diagnosed with heart disease in the past complains of chest pain again, the AI ​​will rate the risk higher. Furthermore, the evaluation unit analyzes the patient's symptoms and medical history in combination to comprehensively assess the severity of their condition. This allows the evaluation unit to quickly and accurately understand the patient's condition and perform appropriate triage.

[0073] The allocation unit distributes resources in real time based on information evaluated by the evaluation unit. For example, the allocation unit uses AI to analyze the schedules of medical staff and the utilization of facilities to make optimal resource allocations. Specifically, it works in conjunction with the medical staff schedule management system to make optimal placements considering each staff member's work status and area of ​​expertise. For example, if a critically ill patient arrives, the AI ​​will quickly assign the most suitable specialist to that patient. Facility utilization is also monitored in real time, and the availability of hospital beds and the usage of examination rooms are constantly updated. Based on this information, the AI ​​guides patients to the appropriate examination rooms or hospital beds. Furthermore, the allocation unit can also combine and analyze the schedules of medical staff and facility utilization to make optimal resource allocations. For example, if there is a large number of emergency cases, the AI ​​will quickly reallocate resources and assign additional medical staff as needed. It will also adjust the use schedule of examination rooms according to facility utilization to ensure efficient medical care. In this way, the allocation unit can optimally allocate medical resources, minimize patient waiting times, and improve the efficiency of medical care delivery.

[0074] The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze historical data and external factors to predict future patient influx and resource needs. Specifically, it analyzes seasonal fluctuations in patient numbers and trends in the occurrence of specific diseases based on historical patient data. For example, since the number of patients tends to increase during influenza outbreaks, the AI ​​takes this into account when predicting resource allocation. External factors such as weather data and local event information are also considered. For example, if a large-scale event is held, the risk of emergency cases increases, so the AI ​​predicts this and secures the necessary resources in advance. Furthermore, the prediction unit can analyze historical data and external factors in combination to predict future patient influx and resource needs with greater accuracy. For example, if historical data shows a tendency for the number of patients to increase on specific days of the week or time slots, the AI ​​adjusts resource allocation based on this. This allows the prediction unit to accurately predict future resource needs and ensure thorough preparation for healthcare delivery.

[0075] The navigation unit provides navigation based on information predicted by the prediction unit. For example, the navigation unit uses AI to analyze the patient's location and the congestion status of medical facilities to provide the optimal route. Specifically, when a patient is heading to a hospital, it uses the location information from their smartphone to guide them along the best route. The AI ​​analyzes traffic information and road congestion in real time to calculate the route that will get them there in the shortest time. It also monitors the congestion status within medical facilities in real time, and when a patient arrives, it guides them to the least crowded examination room or waiting room. Furthermore, the navigation unit can also analyze the patient's location information and the congestion status of medical facilities in combination to provide the optimal route. For example, if there are multiple medical facilities, the AI ​​compares the congestion status of each facility and guides the patient to the facility where they can be seen most quickly. It also guides the patient along the shortest route when they move around within the hospital, minimizing waiting times. In this way, the navigation unit can support patients in receiving medical services quickly and efficiently, improving the efficiency of medical delivery.

[0076] The multilingual support unit performs multilingual support. The multilingual support unit can, for example, use AI to support patients who speak different languages. The multilingual support unit can, for example, use translation software to automatically translate the patient's language. The multilingual support unit can also recognize the patient's language using speech recognition technology and translate it into the appropriate language. Furthermore, the multilingual support unit can analyze the patient's language using text analysis technology and translate it into the appropriate language. This enables the multilingual support unit to support patients who speak different languages. Some or all of the above-described processes in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the patient's language data into a generating AI and have the generating AI perform language translation.

[0077] The telemedicine department integrates telemedicine. For example, the telemedicine department uses AI to enable patients to receive medical services even from remote locations. For example, the telemedicine department connects patients and medical staff using video call technology. The telemedicine department can also transmit patient health data to medical staff in real time using telemedicine technology. Furthermore, the telemedicine department can monitor the patient's health status using remote monitoring technology and notify medical staff as needed. This enables the telemedicine department to enable patients to receive medical services even from remote locations. Some or all of the above processes in the telemedicine department may be performed using AI, for example, or not using AI. For example, the telemedicine department can input patient health data into a generating AI and have the generating AI perform data analysis.

[0078] The evaluation unit can estimate the patient's emotions and adjust the evaluation priority based on the estimated emotions. For example, if the patient is feeling anxious, the evaluation unit will prioritize evaluating symptoms of high urgency. The evaluation unit can, for example, capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The evaluation unit can also record the patient's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the evaluation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of more appropriate medical care by adjusting the evaluation priority based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0079] The evaluation unit can analyze a patient's past treatment outcomes to improve the accuracy of the evaluation. For example, the evaluation unit can assess the relevance of a patient's past treatment outcomes to their current symptoms. For example, the evaluation unit can receive a patient's treatment history as input, and the AI ​​analyzes that history and assesses its relevance to the current symptoms. The evaluation unit can also evaluate the effectiveness of a specific treatment method based on past treatment outcomes. For example, the evaluation unit can analyze the success rate of a specific treatment method and evaluate its effectiveness. Furthermore, the evaluation unit can analyze a patient's past treatment outcomes and propose the optimal treatment method. For example, the evaluation unit can propose the optimal treatment method for the current symptoms based on past treatment outcomes. This improves the accuracy of the evaluation by analyzing past treatment outcomes. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input patient treatment history data into a generating AI and have the generating AI perform the data analysis.

[0080] The evaluation unit can incorporate patients' lifestyle data and reflect it in the evaluation. For example, the evaluation unit can reflect patients' dietary and exercise habits in the evaluation. For example, the evaluation unit can receive patients' dietary data as input, have the AI ​​analyze that data, and reflect it in the evaluation. The evaluation unit can also receive patients' exercise habit data as input, have the AI ​​analyze that data, and reflect it in the evaluation. Furthermore, the evaluation unit can incorporate patients' sleep patterns into the evaluation. For example, the evaluation unit can receive patients' sleep data as input, have the AI ​​analyze that data, and reflect it in the evaluation. The evaluation unit can also reflect patients' stress levels in the evaluation. For example, the evaluation unit can receive patients' stress data as input, have the AI ​​analyze that data, and reflect it in the evaluation. This allows for a more comprehensive evaluation by incorporating lifestyle data. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input patients' lifestyle data into a generating AI and have the generating AI perform the data analysis.

[0081] The evaluation unit can estimate the patient's emotions and adjust the feedback method of the evaluation results based on the estimated emotions. For example, if the patient is feeling anxious, the evaluation unit can provide reassuring feedback. For example, the evaluation unit can capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The evaluation unit can also record the patient's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the evaluation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate feedback to be provided by adjusting the feedback method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0082] The evaluation unit can take the patient's geographical location into consideration and reflect region-specific health risks in its evaluation. For example, the evaluation unit can reflect the infectious disease risk in the area where the patient lives in its evaluation. For example, the evaluation unit can receive the patient's geographical location as input, have the AI ​​analyze that information, and reflect region-specific health risks in its evaluation. The evaluation unit can also incorporate environmental factors in the area where the patient lives into its evaluation. For example, the evaluation unit can receive the patient's geographical location as input, have the AI ​​analyze that information, and reflect environmental factors in its evaluation. Furthermore, the evaluation unit can also reflect the utilization of medical resources in the area where the patient lives in its evaluation. For example, the evaluation unit can receive the patient's geographical location as input, have the AI ​​analyze that information, and reflect the utilization of medical resources in its evaluation. In this way, by considering geographical location information, region-specific health risks can be reflected in the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the patient's geographical location into a generating AI and have the generating AI perform the data analysis.

[0083] The evaluation unit can analyze patients' social media activity and incorporate health-related information into the evaluation. For example, the evaluation unit can extract health-related information from patients' social media posts. For example, the evaluation unit can receive patients' social media activity as input, have an AI analyze that activity, and extract health-related information. The evaluation unit can also assess stress levels from patients' social media activity. For example, the evaluation unit can receive patients' social media activity as input, have an AI analyze that activity, and assess stress levels. Furthermore, the evaluation unit can reflect lifestyle habits in the evaluation from patients' social media activity. For example, the evaluation unit can receive patients' social media activity as input, have an AI analyze that activity, and reflect lifestyle habits in the evaluation. This allows health-related information to be incorporated into the evaluation by analyzing social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input patients' social media data into a generating AI and have the generating AI perform the data analysis.

[0084] The allocation unit can estimate a patient's emotions and adjust resource allocation priorities based on those priorities. For example, if a patient is feeling anxious, the allocation unit can quickly allocate medical staff. The allocation unit can, for example, capture a patient's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The allocation unit can also record a patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice and calculate an emotion score. Furthermore, the allocation unit can collect a patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate resource allocation by adjusting resource allocation priorities based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0085] The allocation unit can analyze past resource usage data to improve the efficiency of allocation. For example, the allocation unit can propose an optimal resource allocation based on past resource usage data. For example, the allocation unit can receive past resource usage data as input, have an AI analyze the data, and propose an optimal resource allocation. The allocation unit can also reduce wasteful resource allocations from past resource usage data. For example, the allocation unit can receive past resource usage data as input, have an AI analyze the data, and reduce wasteful resource allocations. Furthermore, the allocation unit can analyze past resource usage data to perform efficient resource allocation. For example, the allocation unit can receive past resource usage data as input, have an AI analyze the data, and perform efficient resource allocation. This improves the efficiency of allocation by analyzing past resource usage data. Some or all of the above processes in the allocation unit may be performed using an AI, or not. For example, the allocation unit can input past resource usage data into a generating AI and have the generating AI perform the data analysis.

[0086] The allocation unit can make optimal resource allocations by considering the skill sets of medical staff. For example, the allocation unit can allocate resources based on the medical staff's area of ​​expertise. For example, the allocation unit can receive the medical staff's skill sets as input, have the AI ​​analyze those skill sets, and make optimal resource allocations. The allocation unit can also allocate resources considering the medical staff's years of experience. For example, the allocation unit can receive the medical staff's years of experience as input, have the AI ​​analyze those years of experience, and make optimal resource allocations. Furthermore, the allocation unit can evaluate the medical staff's skill sets and make optimal resource allocations. For example, the allocation unit can receive the medical staff's skill sets as input, have the AI ​​evaluate those skill sets, and make optimal resource allocations. This makes optimal resource allocation possible by considering the medical staff's skill sets. Some or all of the above processes in the allocation unit may be performed using AI, or not using AI. For example, the allocation unit can input the medical staff's skill set data into a generating AI and have the generating AI perform the data analysis.

[0087] The allocation unit can estimate the patient's emotions and adjust the notification method for resource allocation based on the estimated emotions. For example, if the patient is feeling anxious, the allocation unit can provide a reassuring notification method. For example, the allocation unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The allocation unit can also record the patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the allocation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate resource allocation by adjusting the notification method based on 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-described processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0088] The allocation unit can perform optimal resource allocation by considering the geographical location information of medical facilities. For example, the allocation unit can propose an optimal resource allocation based on the geographical location information of medical facilities. For example, the allocation unit can receive geographical location information of medical facilities as input, have an AI analyze that information, and propose an optimal resource allocation. The allocation unit can also reduce resource waste based on the geographical location information of medical facilities. For example, the allocation unit can receive geographical location information of medical facilities as input, have an AI analyze that information, and reduce resource waste. Furthermore, the allocation unit can perform efficient resource allocation by considering the geographical location information of medical facilities. For example, the allocation unit can receive geographical location information of medical facilities as input, have an AI analyze that information, and perform efficient resource allocation. This makes optimal resource allocation possible by considering geographical location information. Some or all of the above processing in the allocation unit may be performed using an AI, or not using an AI. For example, the allocation unit can input geographical location information of medical facilities into a generating AI and have the generating AI perform data analysis.

[0089] The allocation unit can monitor the work status of medical staff in real time and dynamically adjust the allocation. For example, the allocation unit can monitor the work status of medical staff in real time and make the optimal resource allocation. For example, the allocation unit can receive the work status of medical staff as input, have an AI analyze the situation, and make the optimal resource allocation. The allocation unit can also reduce wasteful resource allocation based on the work status of medical staff. For example, the allocation unit can receive the work status of medical staff as input, have an AI analyze the situation, and reduce wasteful resource allocation. Furthermore, the allocation unit can make efficient resource allocation by taking the work status of medical staff into consideration. For example, the allocation unit can receive the work status of medical staff as input, have an AI analyze the situation, and make efficient resource allocation. This enables dynamic resource allocation by monitoring the work status in real time. Some or all of the above processing in the allocation unit may be performed using an AI, or not. For example, the allocation unit can input medical staff work status data into a generating AI and have the generating AI perform the data analysis.

[0090] The prediction unit can estimate the patient's emotions and improve the accuracy of predictions based on the estimated emotions. For example, if the patient is feeling anxious, the prediction unit will prioritize predicting symptoms of high urgency. The prediction unit can, for example, capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The prediction unit can also record the patient's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the prediction unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more accurate predictions by improving the accuracy of predictions based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0091] The prediction unit can analyze past patient data and optimize the prediction model. For example, the prediction unit can propose an optimal prediction model based on past patient data. For example, the prediction unit can receive past patient data as input, have the AI ​​analyze that data, and propose an optimal prediction model. The prediction unit can also improve the accuracy of the prediction model from past patient data. For example, the prediction unit can receive past patient data as input, have the AI ​​analyze that data, and improve the accuracy of the prediction model. Furthermore, the prediction unit can analyze past patient data and construct an efficient prediction model. For example, the prediction unit can receive past patient data as input, have the AI ​​analyze that data, and construct an efficient prediction model. This improves the accuracy of the prediction model by analyzing past patient data. Some or all of the above processes in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input past patient data into a generating AI and have the generating AI perform the data analysis.

[0092] The prediction unit can take in seasonal and weather data and reflect it in its predictions. For example, the prediction unit can optimize its prediction model by considering seasonal variations. For example, the prediction unit can receive seasonal data as input, have the AI ​​analyze that data, and optimize its prediction model. The prediction unit can also improve the accuracy of its prediction model by considering weather variations. For example, the prediction unit can receive weather data as input, have the AI ​​analyze that data, and improve the accuracy of its prediction model. Furthermore, the prediction unit can analyze seasonal and weather data to build an efficient prediction model. For example, the prediction unit can receive seasonal and weather data as input, have the AI ​​analyze that data, and build an efficient prediction model. This improves the accuracy of predictions by incorporating seasonal and weather data. Some or all of the above processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input seasonal and weather data into a generating AI and have the generating AI perform the data analysis.

[0093] The prediction unit can estimate the patient's emotions and adjust the notification method of the prediction result based on the estimated emotions. For example, if the patient is feeling anxious, the prediction unit can provide a reassuring notification method. The prediction unit can, for example, capture the patient's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The prediction unit can also record the patient's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the prediction unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate prediction results by adjusting the notification method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0094] The prediction unit can improve the accuracy of its predictions by considering local health data. For example, the prediction unit can propose an optimal prediction model based on local health data. For example, the prediction unit can receive local health data as input, have an AI analyze the data, and propose an optimal prediction model. The prediction unit can also improve the accuracy of its prediction model from local health data. For example, the prediction unit can receive local health data as input, have an AI analyze the data, and improve the accuracy of its prediction model. Furthermore, the prediction unit can analyze local health data and construct an efficient prediction model. For example, the prediction unit can receive local health data as input, have an AI analyze the data, and construct an efficient prediction model. This improves the accuracy of predictions by considering local health data. Some or all of the above processes in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input local health data into a generating AI and have the generating AI perform the data analysis.

[0095] The prediction unit can enhance its prediction model by referencing external health-related databases. For example, the prediction unit can propose an optimal prediction model based on an external health-related database. For example, the prediction unit can receive an external health-related database as input, have an AI analyze the data, and propose an optimal prediction model. The prediction unit can also improve the accuracy of its prediction model from external health-related databases. For example, the prediction unit can receive an external health-related database as input, have an AI analyze the data, and improve the accuracy of its prediction model. Furthermore, the prediction unit can analyze external health-related databases and build an efficient prediction model. For example, the prediction unit can receive an external health-related database as input, have an AI analyze the data, and build an efficient prediction model. This improves the accuracy of the prediction model by referencing external health-related databases. Some or all of the above processes in the prediction unit may be performed using AI, or not. For example, the prediction unit can input an external health-related database into a generating AI and have the generating AI perform the data analysis.

[0096] The navigation unit can estimate the patient's emotions and adjust the navigation route based on the estimated emotions. For example, if the patient is feeling anxious, the navigation unit will suggest the shortest route. The navigation unit can, for example, capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The navigation unit can also record the patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the navigation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of a more appropriate route by adjusting the navigation route based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input patient image data captured by a camera into a generating AI, which can then perform the estimation of the patient's emotions.

[0097] The navigation unit can analyze the patient's past travel history and propose the optimal route. For example, the navigation unit proposes the optimal route based on the patient's past travel history. For example, the navigation unit receives the patient's past travel history as input, the AI ​​analyzes the data, and proposes the optimal route. The navigation unit can also propose a route that avoids congestion based on the patient's past travel history. For example, the navigation unit receives the patient's past travel history as input, the AI ​​analyzes the data, and proposes a route that avoids congestion. Furthermore, the navigation unit can analyze the patient's past travel history and propose the most efficient route. For example, the navigation unit receives the patient's past travel history as input, the AI ​​analyzes the data, and proposes the most efficient route. In this way, the optimal route is proposed by analyzing past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the patient's past travel history data into a generating AI and have the generating AI perform the data analysis.

[0098] The navigation unit can monitor the congestion status of medical facilities in real time and dynamically adjust the route. For example, the navigation unit can monitor the congestion status of medical facilities in real time and propose the optimal route. For example, the navigation unit can receive the congestion status of medical facilities as input, the AI ​​analyzes the data, and propose the optimal route. The navigation unit can also reduce unnecessary routes based on the congestion status of medical facilities. For example, the navigation unit can receive the congestion status of medical facilities as input, the AI ​​analyzes the data, and reduce unnecessary routes. Furthermore, the navigation unit can propose an efficient route considering the congestion status of medical facilities. For example, the navigation unit can receive the congestion status of medical facilities as input, the AI ​​analyzes the data, and propose an efficient route. This enables dynamic route adjustment by monitoring congestion status in real time. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the congestion status data of medical facilities into a generating AI and have the generating AI perform the data analysis.

[0099] The navigation unit can estimate the patient's emotions and adjust the navigation guidance method based on the estimated emotions. For example, if the patient is feeling anxious, the navigation unit can provide guidance that provides reassurance. The navigation unit can, for example, capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The navigation unit can also record the patient's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the navigation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate guidance to be provided by adjusting the guidance method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0100] The navigation unit can propose the optimal route by considering geographical obstacles and traffic conditions. For example, the navigation unit can propose a route that avoids geographical obstacles. For example, the navigation unit can receive information on geographical obstacles as input, the AI ​​analyzes that information, and propose the optimal route. The navigation unit can also propose a route that avoids traffic congestion. For example, the navigation unit can receive information on traffic congestion as input, the AI ​​analyzes that information, and propose the optimal route. Furthermore, the navigation unit can propose a route that avoids road construction. For example, the navigation unit can receive information on road construction as input, the AI ​​analyzes that information, and propose the optimal route. In this way, the optimal route is proposed by considering geographical obstacles and traffic conditions. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input data on geographical obstacles and traffic conditions into a generating AI and have the generating AI perform the data analysis.

[0101] The navigation unit can provide an optimal navigation method by taking into account the patient's device information. For example, if the patient is using a smartphone, the navigation unit can provide a navigation method adapted to the screen size. For example, the navigation unit can receive the patient's device information as input, the AI ​​analyzes that information, and provide an optimal navigation method. The navigation unit can also provide a navigation method optimized for a larger screen if the patient is using a tablet. For example, the navigation unit can receive the patient's device information as input, the AI ​​analyzes that information, and provide an optimal navigation method. Furthermore, if the patient is using a smartwatch, the navigation unit can provide a concise and highly visible navigation method. For example, the navigation unit can receive the patient's device information as input, the AI ​​analyzes that information, and provide an optimal navigation method. In this way, an optimal navigation method is provided by taking device information into consideration. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the patient's device information into a generating AI and have the generating AI perform data analysis.

[0102] The multilingual support unit can estimate the patient's emotions and adjust the priority of multilingual support based on the estimated emotions. For example, if the patient is feeling anxious, the multilingual support unit will provide rapid multilingual support. For example, the multilingual support unit can capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The multilingual support unit can also record the patient's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the multilingual support unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate support by adjusting the priority of multilingual support based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0103] The multilingual support unit can analyze past multilingual support data to improve the accuracy of its responses. For example, the multilingual support unit can propose the optimal response method based on past multilingual support data. For example, the multilingual support unit can receive past multilingual support data as input, have an AI analyze that data, and propose the optimal response method. Furthermore, the multilingual support unit can improve the accuracy of its responses from past multilingual support data. For example, the multilingual support unit can receive past multilingual support data as input, have an AI analyze that data, and improve the accuracy of its responses. In addition, the multilingual support unit can analyze past multilingual support data and construct an efficient response method. For example, the multilingual support unit can receive past multilingual support data as input, have an AI analyze that data, and construct an efficient response method. As a result, the accuracy of the responses is improved by analyzing past multilingual support data. Some or all of the above processing in the multilingual support unit may be performed using an AI, or not. For example, the multilingual support unit can input past multilingual support data into a generating AI and have the generating AI perform the data analysis.

[0104] The multilingual support unit can provide optimal multilingual support based on the patient's language settings. For example, the multilingual support unit can provide multilingual support based on the language settings of the patient's device. For example, the multilingual support unit can receive the patient's device language settings as input, analyze the information with AI, and provide optimal multilingual support. The multilingual support unit can also provide a language switching function if the patient uses multiple languages. For example, the multilingual support unit can receive the patient's language settings as input, analyze the information with AI, and provide a language switching function. Furthermore, if the patient selects a specific language, the multilingual support unit can respond in that language. For example, the multilingual support unit can receive the patient's language settings as input, analyze the information with AI, and respond in that specific language. This enables optimal multilingual support based on language settings. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the patient's language setting data into a generating AI and have the generating AI perform the data analysis.

[0105] The multilingual support unit can estimate the patient's emotions and adjust the multilingual notification method based on the estimated emotions. For example, if the patient is feeling anxious, the multilingual support unit can provide a reassuring notification method. For example, the multilingual support unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The multilingual support unit can also record the patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the multilingual support unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for a more appropriate response by adjusting the notification method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0106] The multilingual support unit can perform optimal multilingual support by considering the regional language distribution. For example, the multilingual support unit proposes the optimal multilingual support based on the regional language distribution. For example, the multilingual support unit receives the regional language distribution as input, the AI ​​analyzes the data, and proposes the optimal multilingual support. Furthermore, the multilingual support unit can also improve the accuracy of its support based on the regional language distribution. For example, the multilingual support unit receives the regional language distribution as input, the AI ​​analyzes the data, and improves the accuracy of its support. In addition, the multilingual support unit can analyze the regional language distribution and construct an efficient multilingual support. For example, the multilingual support unit receives the regional language distribution as input, the AI ​​analyzes the data, and constructs an efficient multilingual support. This makes optimal multilingual support possible by considering the regional language distribution. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input regional language distribution data into a generating AI and have the generating AI perform the data analysis.

[0107] The multilingual support unit can evaluate the patient's language skills and provide optimal multilingual support. For example, the multilingual support unit can evaluate the patient's language skills and propose the optimal support method. For example, the multilingual support unit can receive the patient's language skills as input, have an AI analyze the data, and propose the optimal support method. The multilingual support unit can also improve the accuracy of its support based on the patient's language skills. For example, the multilingual support unit can receive the patient's language skills as input, have an AI analyze the data, and improve the accuracy of its support. Furthermore, the multilingual support unit can analyze the patient's language skills and construct efficient support methods. For example, the multilingual support unit can receive the patient's language skills as input, have an AI analyze the data, and construct efficient support methods. This enables optimal multilingual support by evaluating language skills. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the patient's language skills data into a generating AI and have the generating AI perform the data analysis.

[0108] The telemedicine department can estimate a patient's emotions and adjust the priority of telemedicine based on the estimated emotions. For example, if a patient is feeling anxious, the telemedicine department can provide prompt telemedicine. For example, the telemedicine department can capture a patient's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The telemedicine department can also record a patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice and calculate an emotion score. Furthermore, the telemedicine department can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate responses by adjusting the priority of telemedicine based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the telemedicine department may be performed using AI, for example, or without AI. For example, the telemedicine department can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0109] The telemedicine department can analyze past telemedicine data to improve the accuracy of its responses. For example, the telemedicine department can propose the optimal response method based on past telemedicine data. For example, the telemedicine department can receive past telemedicine data as input, have an AI analyze that data, and propose the optimal response method. Furthermore, the telemedicine department can improve the accuracy of its responses from past telemedicine data. For example, the telemedicine department can receive past telemedicine data as input, have an AI analyze that data, and improve the accuracy of its responses. In addition, the telemedicine department can analyze past telemedicine data and construct efficient response methods. For example, the telemedicine department can receive past telemedicine data as input, have an AI analyze that data, and construct efficient response methods. As a result, the accuracy of responses is improved by analyzing past telemedicine data. Some or all of the above processes in the telemedicine department may be performed using AI, or not using AI. For example, the telemedicine department can input past telemedicine data into a generating AI and have the generating AI perform the data analysis.

[0110] The telemedicine department can monitor patients' health status in real time and dynamically adjust telemedicine responses. For example, the telemedicine department can monitor patients' health status in real time and propose the optimal response. For example, the telemedicine department can receive patient health status as input, have AI analyze the data, and propose the optimal response. The telemedicine department can also improve the accuracy of its responses based on the patient's health status. For example, the telemedicine department can receive patient health status as input, have AI analyze the data, and improve the accuracy of its responses. Furthermore, the telemedicine department can analyze patient health status and build efficient response methods. For example, the telemedicine department can receive patient health status as input, have AI analyze the data, and build efficient response methods. This enables dynamic responses by monitoring health status in real time. Some or all of the above processes in the telemedicine department may be performed using AI, or not. For example, the telemedicine department can input patient health status data into a generating AI and have the generating AI perform the data analysis.

[0111] The telemedicine department can estimate a patient's emotions and adjust telemedicine notification methods based on those estimated emotions. For example, if a patient is feeling anxious, the telemedicine department can provide reassuring notification methods. For example, the telemedicine department can capture a patient's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The telemedicine department can also record a patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice to calculate an emotion score. Furthermore, the telemedicine department can collect a patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate responses by adjusting notification methods based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the telemedicine department may be performed using AI, for example, or without AI. For example, the telemedicine department can input patient image data captured by a camera into a generating AI and have the generating AI perform the estimation of the patient's emotions.

[0112] The telemedicine department can provide optimal telemedicine support while considering geographical constraints. For example, the telemedicine department can propose the optimal response method while considering geographical constraints. For example, the telemedicine department can receive geographical constraint information as input, have an AI analyze that information, and propose the optimal response method. Furthermore, the telemedicine department can improve the accuracy of its response based on geographical constraints. For example, the telemedicine department can receive geographical constraint information as input, have an AI analyze that information, and improve the accuracy of its response. In addition, the telemedicine department can analyze geographical constraints and construct efficient response methods. For example, the telemedicine department can receive geographical constraint information as input, have an AI analyze that information, and construct efficient response methods. This makes it possible to provide optimal telemedicine support by considering geographical constraints. Some or all of the above processes in the telemedicine department may be performed using AI, or not. For example, the telemedicine department can input geographical constraint data into a generating AI and have the generating AI perform the data analysis.

[0113] The telemedicine department can provide optimal telemedicine support by considering the patient's internet connection status. For example, the telemedicine department can evaluate the patient's internet connection status and propose the optimal response method. For example, the telemedicine department can receive the patient's internet connection status as input, have an AI analyze that information, and propose the optimal response method. Furthermore, the telemedicine department can improve the accuracy of its responses based on the patient's internet connection status. For example, the telemedicine department can receive the patient's internet connection status as input, have an AI analyze that information, and improve the accuracy of its responses. In addition, the telemedicine department can analyze the patient's internet connection status and construct efficient response methods. For example, the telemedicine department can receive the patient's internet connection status as input, have an AI analyze that information, and construct efficient response methods. This makes it possible to provide optimal telemedicine support by considering the internet connection status. Some or all of the above processes in the telemedicine department may be performed using AI, or not. For example, the telemedicine department can input data on the patient's internet connection status into a generating AI and have the generating AI perform the data analysis.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The evaluation unit can estimate the patient's emotions and adjust the evaluation priority based on the estimated emotions. For example, if the patient is feeling anxious, it will prioritize the evaluation of symptoms with a high degree of urgency. The evaluation unit can also capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The evaluation unit can also record the patient's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the evaluation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of more appropriate medical care by adjusting the evaluation priority based on the patient's emotions.

[0116] The evaluation unit can analyze a patient's past treatment outcomes to improve the accuracy of its evaluations. For example, it can evaluate the relevance of a patient's past treatment results to their current symptoms. The evaluation unit receives the patient's treatment history as input, and the AI ​​analyzes this history to evaluate its relevance to the current symptoms. The evaluation unit can also evaluate the effectiveness of specific treatments based on past treatment results. For example, it can analyze the success rate of a specific treatment and evaluate its effectiveness. Furthermore, the evaluation unit can analyze a patient's past treatment results and propose the optimal treatment. For example, it can propose the most suitable treatment for the current symptoms based on past treatment results. This improves the accuracy of evaluations by analyzing past treatment results.

[0117] The evaluation unit can incorporate patients' lifestyle data and reflect it in the evaluation. For example, it can reflect patients' dietary and exercise habits in the evaluation. The evaluation unit receives patients' dietary data as input, the AI ​​analyzes that data, and reflects it in the evaluation. The evaluation unit can also receive patients' exercise habit data as input, the AI ​​analyzes that data, and reflects it in the evaluation. Furthermore, the evaluation unit can incorporate patients' sleep patterns into the evaluation. For example, the evaluation unit receives patients' sleep data as input, the AI ​​analyzes that data, and reflects it in the evaluation. The evaluation unit can also reflect patients' stress levels in the evaluation. For example, the evaluation unit receives patients' stress data as input, the AI ​​analyzes that data, and reflects it in the evaluation. By incorporating lifestyle data, a more comprehensive evaluation becomes possible.

[0118] The evaluation unit can take into account the patient's geographical location and incorporate region-specific health risks into the evaluation. For example, it can incorporate the infectious disease risk in the patient's area of ​​residence into the evaluation. The evaluation unit receives the patient's geographical location as input, the AI ​​analyzes that information, and incorporates region-specific health risks into the evaluation. The evaluation unit can also incorporate environmental factors in the patient's area of ​​residence into the evaluation. For example, the evaluation unit receives the patient's geographical location as input, the AI ​​analyzes that information, and incorporates environmental factors into the evaluation. Furthermore, the evaluation unit can also incorporate the utilization of medical resources in the patient's area into the evaluation. For example, the evaluation unit receives the patient's geographical location as input, the AI ​​analyzes that information, and incorporates the utilization of medical resources into the evaluation. In this way, by considering geographical location, region-specific health risks can be incorporated into the evaluation.

[0119] The evaluation unit can analyze patients' social media activity and incorporate health-related information into the evaluation. For example, it can extract health-related information from patients' social media posts. The evaluation unit receives patients' social media activity as input, an AI analyzes that activity, and extracts health-related information. The evaluation unit can also assess stress levels from patients' social media activity. For example, the evaluation unit receives patients' social media activity as input, an AI analyzes that activity, and assesses stress levels. Furthermore, the evaluation unit can reflect lifestyle habits in the evaluation from patients' social media activity. For example, the evaluation unit receives patients' social media activity as input, an AI analyzes that activity, and reflects lifestyle habits in the evaluation. In this way, by analyzing social media activity, health-related information can be incorporated into the evaluation.

[0120] The allocation unit can estimate a patient's emotions and adjust resource allocation priorities based on those priorities. For example, if a patient is feeling anxious, medical staff can be quickly allocated. The allocation unit can capture the patient's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The allocation unit can also record the patient's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice to calculate an emotion score. Furthermore, the allocation unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate resource allocation by adjusting resource allocation priorities based on the patient's emotions.

[0121] The allocation unit can analyze past resource usage data to improve the efficiency of resource allocation. For example, it can propose the optimal resource allocation based on past resource usage data. The allocation unit receives past resource usage data as input, the AI ​​analyzes that data, and proposes the optimal resource allocation. The allocation unit can also reduce wasteful resource allocation based on past resource usage data. For example, the allocation unit receives past resource usage data as input, the AI ​​analyzes that data, and reduces wasteful resource allocation. Furthermore, the allocation unit can analyze past resource usage data to perform efficient resource allocation. For example, the allocation unit receives past resource usage data as input, the AI ​​analyzes that data, and performs efficient resource allocation. In this way, the efficiency of resource allocation is improved by analyzing past resource usage data.

[0122] The allocation unit can optimize resource allocation by considering the skill sets of medical staff. For example, it can allocate resources based on the medical staff's area of ​​expertise. The allocation unit receives the medical staff's skill sets as input, the AI ​​analyzes those skill sets, and then optimizes resource allocation. The allocation unit can also allocate resources considering the medical staff's years of experience. For example, the allocation unit receives the medical staff's years of experience as input, the AI ​​analyzes those years of experience, and then optimizes resource allocation. Furthermore, the allocation unit can evaluate the medical staff's skill sets and then optimize resource allocation. For example, the allocation unit receives the medical staff's skill sets as input, the AI ​​evaluates those skill sets, and then optimizes resource allocation. This allows for optimal resource allocation by considering the medical staff's skill sets.

[0123] The prediction unit can estimate the patient's emotions and improve the accuracy of predictions based on those emotions. For example, if the patient is feeling anxious, it will prioritize predicting symptoms of high urgency. The prediction unit captures the patient's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The prediction unit can also record the patient's voice and estimate their emotions using voice analysis technology. For example, it analyzes the tone and speed of their voice to calculate an emotion score. Furthermore, the prediction unit can collect the patient's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. By improving the accuracy of predictions based on the patient's emotions, more accurate predictions become possible.

[0124] The prediction unit can analyze past patient data and optimize the prediction model. For example, it can propose an optimal prediction model based on past patient data. The prediction unit receives past patient data as input, the AI ​​analyzes that data, and proposes an optimal prediction model. The prediction unit can also improve the accuracy of the prediction model from past patient data. For example, the prediction unit receives past patient data as input, the AI ​​analyzes that data, and improves the accuracy of the prediction model. Furthermore, the prediction unit can analyze past patient data and build an efficient prediction model. For example, the prediction unit receives past patient data as input, the AI ​​analyzes that data, and builds an efficient prediction model. As a result, the accuracy of the prediction model is improved by analyzing past patient data.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The evaluation unit assesses the patient's symptoms and medical history. The evaluation unit, for example, uses AI to analyze the patient's symptoms and medical history and assess the severity. The evaluation unit receives the patient's symptoms as input, the AI ​​analyzes those symptoms, and assesses the severity. It can also receive the patient's medical history as input, the AI ​​analyzes that history, and assesses the severity. Furthermore, it can combine the patient's symptoms and medical history in its analysis to assess the severity. Step 2: The allocation unit allocates resources in real time based on the information evaluated by the evaluation unit. For example, the allocation unit uses AI to analyze the schedules of medical staff and the utilization status of facilities to make the optimal resource allocation. The allocation unit receives the schedules of medical staff as input, the AI ​​analyzes those schedules, and makes the optimal resource allocation. It can also receive the utilization status of facilities as input, the AI ​​analyzes that utilization status, and makes the optimal resource allocation. Furthermore, it can analyze the medical staff schedules and facility utilization status in combination to make the optimal resource allocation. Step 3: The prediction unit performs predictive analysis based on the resources allocated by the allocation unit. For example, the prediction unit uses AI to analyze historical data and external factors to predict future patient influx and resource needs. The prediction unit receives historical data as input, and the AI ​​analyzes that data to predict future patient influx and resource needs. It can also receive external factors as input, and the AI ​​analyzes those factors to predict future patient influx and resource needs. Furthermore, it can combine historical data and external factors in its analysis to predict future patient influx and resource needs. Step 4: The navigation unit performs navigation based on the information predicted by the prediction unit. For example, the navigation unit uses AI to analyze the patient's location information and the congestion status of medical facilities to provide the optimal route. The navigation unit receives the patient's location information as input, the AI ​​analyzes that information, and provides the optimal route. It can also receive the congestion status of medical facilities as input, the AI ​​analyzes that status, and provides the optimal route. Furthermore, it can combine and analyze the patient's location information and the congestion status of medical facilities to provide the optimal route.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the evaluation unit, allocation unit, prediction unit, navigation unit, multilingual support unit, and telemedicine unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart device 14 and analyzes the patient's symptoms and medical history. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the allocation of medical resources. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future patient influx and resource needs. The navigation unit is implemented by the control unit 46A of the smart device 14 and provides the optimal route by analyzing the patient's location information and the congestion status of medical facilities. The multilingual support unit is implemented by the control unit 46A of the smart device 14 and supports patients who speak different languages. The telemedicine unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides medical services from remote locations. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the evaluation unit, allocation unit, prediction unit, navigation unit, multilingual support unit, and telemedicine unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the patient's symptoms and medical history. The allocation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes the allocation of medical resources. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future patient influx and resource needs. The navigation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and analyzes the patient's location information and the congestion status of medical facilities to provide the optimal route. The multilingual support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and supports patients who speak different languages. The telemedicine unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides medical services from remote locations. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the evaluation unit, allocation unit, prediction unit, navigation unit, multilingual support unit, and telemedicine unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the patient's symptoms and medical history. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the allocation of medical resources. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future patient influx and resource needs. The navigation unit is implemented by the control unit 46A of the headset terminal 314 and provides the optimal route by analyzing the patient's location information and the congestion status of medical facilities. The multilingual support unit is implemented by the control unit 46A of the headset terminal 314 and supports patients who speak different languages. The telemedicine unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides medical services from remote locations. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the evaluation unit, allocation unit, prediction unit, navigation unit, multilingual support unit, and telemedicine unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the robot 414 and analyzes the patient's symptoms and medical history. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the allocation of medical resources. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts future patient influx and resource needs. The navigation unit is implemented by the control unit 46A of the robot 414 and provides the optimal route by analyzing the patient's location information and the congestion status of medical facilities. The multilingual support unit is implemented by the control unit 46A of the robot 414 and supports patients who speak different languages. The telemedicine unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides medical services from remote locations. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) The evaluation department evaluates the patient's symptoms and medical history, A distribution unit that allocates resources in real time based on the information evaluated by the evaluation unit, A prediction unit that performs predictive analysis based on the resources allocated by the allocation unit, The system includes a navigation unit that performs navigation based on the information predicted by the prediction unit. A system characterized by the following features. (Note 2) It is equipped with a multilingual support unit that handles multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has a telemedicine department that integrates telemedicine services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, The system estimates the patient's emotions and adjusts the priority of evaluations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Analyze patients' past treatment outcomes to improve the accuracy of evaluations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Incorporate patient lifestyle data and reflect it in the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The evaluation unit, The system estimates the patient's emotions and adjusts the feedback method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit, Consider the patient's geographical location to reflect region-specific health risks in the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit, Analyze patients' social media activity and incorporate health-related information into the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The distribution unit is, Estimate the patient's emotions and adjust resource allocation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The distribution unit is, Analyze past resource usage data to improve allocation efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 12) The distribution unit is, We will allocate resources optimally, taking into account the skill sets of medical staff. The system described in Appendix 1, characterized by the features described herein. (Note 13) The distribution unit is, The system estimates the patient's emotions and adjusts how resource allocation notifications are made based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The distribution unit is, We will allocate resources optimally, taking into account the geographical location of medical facilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The distribution unit is, Monitor the work status of medical staff in real time and dynamically adjust their allocation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, It estimates the patient's emotions and improves the accuracy of predictions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, Analyze past patient data to optimize predictive models. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, Incorporate seasonal and weather data and incorporate it into the forecast. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, The system estimates the patient's emotions and adjusts the notification method of the prediction results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, Improve prediction accuracy by considering local health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, We enhance our predictive models by referencing external health-related databases. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is It estimates the patient's emotions and adjusts the navigation route based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is Analyze the patient's past travel history and suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is The system monitors the congestion levels of medical facilities in real time and dynamically adjusts routes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned navigation unit is The system estimates the patient's emotions and adjusts the navigation guidance method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned navigation unit is We propose the optimal route, taking into account geographical obstacles and traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned navigation unit is Providing the optimal navigation method while considering the patient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned multilingual support unit is The system estimates the patient's emotions and adjusts the priority of multilingual support based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned multilingual support unit is We analyze past multilingual support data to improve the accuracy of our support. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned multilingual support unit is Provide optimal multilingual support based on the patient's language settings. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned multilingual support unit is The system estimates the patient's emotions and adjusts the multilingual notification method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned multilingual support unit is We will implement optimal multilingual support, taking into account the local language distribution. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned multilingual support unit is Evaluate patients' language skills and provide optimal multilingual support. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned telemedicine department, Estimate the patient's emotions and adjust telemedicine priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned telemedicine department, Analyzing past telemedicine data to improve the accuracy of responses. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned telemedicine department, Monitor patients' health status in real time and dynamically adjust telemedicine responses. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned telemedicine department, The system estimates the patient's emotions and adjusts the telemedicine notification method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned telemedicine department, We will provide the most appropriate telemedicine services, taking geographical constraints into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned telemedicine department, We will provide the most appropriate telemedicine service, taking into account the patient's internet connectivity. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0199] 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 evaluation department evaluates the patient's symptoms and medical history, A distribution unit that allocates resources in real time based on the information evaluated by the evaluation unit, A prediction unit that performs predictive analysis based on the resources allocated by the allocation unit, The system includes a navigation unit that performs navigation based on the information predicted by the prediction unit. A system characterized by the following features.

2. It is equipped with a multilingual support unit that handles multiple languages. The system according to feature 1.

3. The company has a telemedicine department that integrates telemedicine services. The system according to feature 1.

4. The evaluation unit, The system estimates the patient's emotions and adjusts the priority of evaluations based on the estimated emotions. The system according to feature 1.

5. The evaluation unit, Analyze patients' past treatment outcomes to improve the accuracy of evaluations. The system according to feature 1.

6. The evaluation unit, Incorporate patient lifestyle data and reflect it in the evaluation. The system according to feature 1.

7. The evaluation unit, The system estimates the patient's emotions and adjusts the feedback method based on the estimated emotions. The system according to feature 1.

8. The evaluation unit, Consider the patient's geographical location to reflect region-specific health risks in the assessment. The system according to feature 1.

9. The evaluation unit, Analyze patients' social media activity and incorporate health-related information into the assessment. The system according to feature 1.

10. The distribution unit is, Estimate the patient's emotions and adjust resource allocation priorities based on those estimated emotions. The system according to feature 1.

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