System

Abstract

An object of a system according to an embodiment is to quickly grasp the dispatch situation of ambulances and the information of on-duty doctors in an emergency department and determine a transfer route to an appropriate hospital.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a cooperation unit, and a determination unit. The collection part collects information on a dispatch situation of an ambulance or a first-aid doctor in an emergency room. The analysis unit performs analysis based on the information collected by the collection unit. The cooperation unit acquires the personal identification information based on the result obtained by the analysis unit. The determination unit determines a transfer route to the hospital based on the personal identification information acquired by the cooperation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to quickly grasp information about ambulance dispatch status and the doctor on duty in the emergency room, and to determine the appropriate transport route to the hospital.

[0005] The system according to the embodiment aims to quickly grasp the status of ambulance dispatches and information on doctors on duty at emergency outpatient clinics, and to determine an appropriate transport route to a hospital. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a collaboration unit, and a determination unit. The collection unit collects information on ambulance dispatch status or doctor on duty at the emergency room. The analysis unit performs analysis based on the information collected by the collection unit. The collaboration unit acquires personal identification information based on the results obtained by the analysis unit. The determination unit determines a transport route to the hospital based on the personal identification information acquired by the collaboration unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly grasp the status of ambulance dispatches and information on doctors on duty at emergency outpatient clinics, and determine an appropriate transport route to a hospital. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention constantly monitors ambulance dispatch status and emergency outpatient doctor on-call information, enabling the patient to be transported to a hospital as quickly as possible. This system collects and analyzes information about ambulance dispatch status and emergency outpatient doctor on-call information in real time. Next, in conjunction with personal identification information, the system ascertains the patient's past medical history and information about their regular hospital. This allows for rapid transport to the most appropriate hospital. For example, the system selects the nearest and most appropriate hospital based on the ambulance's location information and information about the hospital's doctor on-call, and determines the transport route taking into account traffic conditions and hospital availability. Furthermore, by inputting the patient's personal identification information, the system quickly obtains information about the patient's past medical history and regular hospital, providing all the information needed to provide optimal treatment. This allows the system to constantly monitor ambulance dispatch status and emergency outpatient doctor on-call information, enabling the patient to be transported to a hospital as quickly as possible. Furthermore, in conjunction with personal identification information, the system can quickly obtain information about the patient's past medical history and regular hospital, enabling the patient to be provided optimal treatment.

[0029] An emergency transport support system according to an embodiment includes a collection unit, an analysis unit, a linking unit, and a determination unit. The collection unit collects information on ambulance dispatch status or on-duty doctors at emergency outpatient clinics. The collection unit collects, for example, location information and dispatch status of each ambulance, and information on on-duty doctors at emergency outpatient clinics at each hospital. The collection unit can also update the location information of ambulances in real time and adjust the optimal collection timing. The collection unit can also predict the schedule of on-duty doctors at emergency outpatient clinics at each hospital and optimize the frequency of information collection. The analysis unit performs analysis based on the information collected by the collection unit. For example, the analysis unit selects an optimal hospital based on the collected information. The analysis unit can also predict the current situation by referring to past data and select an optimal hospital. The analysis unit can also apply different analysis algorithms depending on the patient's symptoms and urgency. The linking unit acquires personal identification information based on the results obtained by the analysis unit. For example, the linking unit acquires the personal identification information and ascertains the patient's past medical history and information on their regular hospital. The coordinating unit can also automatically acquire the patient's family contact information and emergency contact information. Furthermore, the coordinating unit can also confirm the patient's insurance information and ability to pay. The determining unit determines the optimal transport route to the hospital based on the personal identification information acquired by the coordinating unit. The determining unit determines the optimal transport route to the hospital, taking into account, for example, traffic conditions and hospital admissions. Furthermore, the determining unit can also reflect changes in traffic conditions in real time when determining the transport route. Furthermore, the determining unit can also select a route that minimizes the ambulance's fuel consumption and mileage. As a result, the emergency transport support system according to the embodiment constantly monitors the ambulance dispatch status and information on the doctor on duty in the emergency room, enabling the patient to be transported to the hospital as quickly as possible.

[0030] The collection unit can collect the location information and dispatch status of each ambulance, and information on the doctor on duty at each hospital's emergency outpatient department. For example, the collection unit collects the location information of each ambulance as GPS coordinates. The collection unit can also collect the dispatch status of each ambulance as the number of dispatches, the destination, and the time of dispatch. Furthermore, the collection unit can collect information on the doctor on duty at each hospital's emergency outpatient department as the doctor's name, specialty, and working hours. In this way, collecting the location information and dispatch status of each ambulance, and information on the doctor on duty at each hospital's emergency outpatient department enables a rapid response.

[0031] The analysis unit can select a hospital based on the collected information. The analysis unit, for example, selects the most suitable hospital based on the collected information. For example, the analysis unit can select the closest hospital based on the collected information. The analysis unit can also select a hospital based on the collected information, taking into account the availability of specialized departments and the hospital's capacity. Furthermore, the analysis unit can also select a hospital based on the collected information, taking into account traffic conditions and the hospital's acceptance status. This allows for rapid transfer by selecting the most suitable hospital based on the collected information.

[0032] The linking unit can acquire personal identification information and ascertain the patient's past medical history and information on the patient's regular hospital. The linking unit, for example, acquires personal identification information and ascertains the patient's past medical history and information on the patient's regular hospital. For example, the linking unit can acquire the patient's name, address, telephone number, and medical ID. The linking unit can also ascertain the patient's past medical history as a diagnosis, treatment details, and treatment period. Furthermore, the linking unit can ascertain information on the patient's regular hospital as the hospital name, location, and contact information. In this way, by acquiring personal identification information and ascertaining the patient's past medical history and information on the patient's regular hospital, optimal treatment can be provided.

[0033] The determination unit can determine a transfer route to the hospital based on traffic conditions or the hospital's acceptance status. The determination unit determines a transfer route to the hospital based on, for example, traffic conditions. For example, the determination unit can determine an optimal transfer route based on real-time traffic congestion information. The determination unit can also determine a transfer route to the hospital based on the hospital's acceptance status. For example, the determination unit can determine an optimal transfer route based on the availability of hospital beds and the availability of medical staff. Furthermore, the determination unit can determine a transfer route that reflects changes in traffic conditions in real time. This enables rapid transfer by determining an optimal transfer route to the hospital in consideration of traffic conditions and the hospital's acceptance status.

[0034] The collection unit updates the location information of each ambulance in real time and can adjust the optimal collection timing. For example, the collection unit acquires GPS data from each ambulance in real time and keeps the location information always up to date. The collection unit can also update the location information every time the ambulance moves and adjust the optimal collection timing. Furthermore, the collection unit can automatically calculate and update the optimal collection timing using AI based on the ambulance's location information. This allows the optimal collection timing to be adjusted by updating the location information of each ambulance in real time.

[0035] The collection unit can predict the schedules of doctors on duty at each hospital's emergency outpatient department and optimize the frequency of information collection. For example, the collection unit predicts the schedules of doctors on duty at each hospital and adjusts the frequency of information collection. For example, the collection unit uses past data analysis and AI predictions to predict the shift times of doctors on duty and updates the information at that timing. The collection unit can also use AI to automatically optimize the frequency of information collection based on the schedules of doctors on duty. This makes it possible to optimize the frequency of information collection by predicting the schedules of doctors on duty at each hospital's emergency outpatient department.

[0036] The collection unit can include the equipment status of ambulances and the skill levels of medical staff in the information it collects. For example, the collection unit collects the equipment status of each ambulance and checks whether the necessary medical equipment is available. The collection unit can also collect the skill levels of the medical staff of each ambulance and evaluate whether an appropriate response is possible. Furthermore, the collection unit can select the most appropriate ambulance based on the equipment status of the ambulance and the skill levels of the medical staff. In this way, by including the equipment status of ambulances and the skill levels of medical staff in the information it collects, more appropriate responses can be made.

[0037] The collection unit can collect the fuel status and maintenance status of each ambulance and evaluate the possibility of dispatch. For example, the collection unit collects the fuel status of each ambulance and evaluates whether it is possible to dispatch. The collection unit can also collect the maintenance status of each ambulance and check whether there are any obstacles to dispatch. Furthermore, the collection unit can select ambulances that can be dispatched based on the fuel status and maintenance status. In this way, by collecting the fuel status and maintenance status of each ambulance, the possibility of dispatch can be evaluated.

[0038] The collection unit can collect information on available beds and the operating status of medical equipment at each hospital to evaluate its capacity. For example, the collection unit collects information on available beds at each hospital to evaluate whether it is possible to accept patients. The collection unit can also collect information on the operating status of medical equipment at each hospital to confirm whether the necessary treatment is possible. Furthermore, the collection unit can select hospitals that can accept patients based on the available beds and the operating status of medical equipment. In this way, by collecting information on available beds and the operating status of medical equipment at each hospital, it is possible to evaluate its capacity.

[0039] The collection unit can include local disaster information and traffic accident information in the information it collects. For example, the collection unit collects local disaster information and checks whether it will affect ambulance dispatches. The collection unit can also collect traffic accident information and select the optimal transport route. Furthermore, the collection unit can plan the optimal response based on the disaster information and traffic accident information. Thus, by including local disaster information and traffic accident information in the information it collects, more appropriate responses can be made.

[0040] The analysis unit can dynamically change the selection criteria for the most suitable hospital based on the collected information. For example, the analysis unit changes the selection criteria for the most suitable hospital in real time based on the collected information. The analysis unit can also dynamically adjust the selection criteria taking into account the hospital's acceptance situation and traffic conditions. Furthermore, the analysis unit can also change the selection criteria depending on the patient's symptoms and urgency. This makes it possible to select a more appropriate hospital by dynamically changing the selection criteria for the most suitable hospital based on the collected information.

[0041] During analysis, the analysis unit can refer to past data to predict the current situation and select the most appropriate hospital. For example, the analysis unit refers to past data to predict the current situation and select the most appropriate hospital. The analysis unit can also predict the most appropriate hospital based on the past history of ambulance dispatches. Furthermore, the analysis unit can also refer to the past hospital acceptance status and select a hospital that is currently able to accept patients. This makes it possible to select a more appropriate hospital by predicting the current situation by referring to past data.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the patient's symptoms and urgency. For example, the analysis unit applies the optimal analysis algorithm depending on the patient's symptoms. The analysis unit can also apply different analysis algorithms depending on the patient's urgency. Furthermore, the analysis unit can select the optimal analysis algorithm taking into consideration the patient's symptoms and urgency. This allows for more appropriate hospital selection by applying different analysis algorithms depending on the patient's symptoms and urgency.

[0043] During analysis, the analysis unit can select the most appropriate hospital by taking into account the distribution of medical resources in the region. For example, the analysis unit selects the most appropriate hospital by taking into account the distribution of medical resources in the region. The analysis unit can also select the most appropriate hospital based on the distribution of medical resources. Furthermore, the analysis unit can analyze the distribution of medical resources in the region in real time and select the most appropriate hospital. This makes it possible to select a more appropriate hospital by taking into account the distribution of medical resources in the region.

[0044] During the analysis, the analysis unit can select the most appropriate hospital by taking into consideration whether each hospital has a specialized department. For example, the analysis unit selects the most appropriate hospital by taking into consideration whether each hospital has a specialized department. The analysis unit can also select a hospital with a specialized department depending on the patient's symptoms. Furthermore, the analysis unit can analyze in real time whether each hospital has a specialized department and select the most appropriate hospital. This makes it possible to select a more appropriate hospital by taking into consideration whether each hospital has a specialized department.

[0045] During analysis, the analysis unit can select the most appropriate hospital by taking into consideration attribute information such as the patient's age and gender. The analysis unit selects the most appropriate hospital by taking into consideration, for example, the patient's age. The analysis unit can also select the most appropriate hospital by taking into consideration the patient's gender. Furthermore, the analysis unit can also select the most appropriate hospital based on attribute information such as the patient's age and gender. This makes it possible to select a more appropriate hospital by taking into consideration attribute information such as the patient's age and gender.

[0046] The linking unit can include the patient's past treatment history and allergy information when acquiring personal identification information. For example, the linking unit includes the patient's past treatment history and allergy information when acquiring personal identification information. For example, the linking unit acquires the patient's past treatment history as a diagnosis, treatment content, and treatment period. The linking unit can also acquire the patient's allergy information as the type of allergen and the degree of allergic reaction. Furthermore, the linking unit can provide optimal treatment based on the past treatment history and allergy information. In this way, by including the patient's past treatment history and allergy information, more appropriate treatment can be provided.

[0047] The linking unit can automatically acquire the patient's family contact information and emergency contact information when acquiring personal identification information. For example, the linking unit automatically acquires the patient's family contact information and emergency contact information when acquiring personal identification information. For example, the linking unit acquires the patient's family contact information as name, telephone number, and address. The linking unit can also acquire the patient's emergency contact information as name, telephone number, and address. Furthermore, the linking unit can plan the optimal response based on the family contact information and emergency contact information. In this way, by automatically acquiring the patient's family contact information and emergency contact information, a prompt response is possible.

[0048] The coordination unit can confirm the patient's insurance information and ability to pay when acquiring personal identification information. For example, the coordination unit confirms the patient's insurance information and ability to pay when acquiring personal identification information. For example, the coordination unit acquires the patient's insurance information as the insurance company name, insurance number, and type of insurance. The coordination unit can also confirm the patient's ability to pay as income, expenses, and savings. Furthermore, the coordination unit can plan optimal treatment based on the insurance information and ability to pay. In this way, by confirming the patient's insurance information and ability to pay, more appropriate treatment can be provided.

[0049] The linking unit can automatically acquire the patient's current location information when acquiring personal identification information. For example, the linking unit automatically acquires the patient's current location information when acquiring personal identification information. For example, when the patient opens the app, the linking unit automatically acquires the patient's current location and reflects it in the personal identification information. In addition, when the patient uses the app while on the move, the linking unit can also update the patient's current location in real time and reflect it in the personal identification information. Furthermore, the linking unit can plan the optimal response based on the patient's current location information. As a result, automatically acquiring the patient's current location information enables more appropriate responses.

[0050] The linking unit can refer to the patient's past medical institution visit history when acquiring personal identification information. For example, the linking unit refers to the patient's past medical institution visit history when acquiring personal identification information. For example, the linking unit acquires the patient's past medical institution visit history including the visit date and time, medical treatment details, and medical treatment results. The linking unit can also select the most appropriate medical institution based on the past visit history. Furthermore, the linking unit can refer to the patient's past visit history and plan the most appropriate treatment. As a result, more appropriate treatment can be provided by referring to the patient's past medical institution visit history.

[0051] The linking unit can take into account the patient's lifestyle and health condition when acquiring personal identification information. For example, the linking unit takes into account the patient's lifestyle and health condition when acquiring personal identification information. For example, the linking unit acquires the patient's lifestyle as diet, exercise, and sleep. The linking unit can also acquire the patient's health condition as medical history and current symptoms. Furthermore, the linking unit can plan optimal treatment based on the patient's lifestyle and health condition. This makes it possible to provide more appropriate treatment by taking into account the patient's lifestyle and health condition.

[0052] The determination unit can reflect changes in traffic conditions in real time when determining a transportation route. The determination unit determines an optimal transportation route based on, for example, real-time traffic congestion information. The determination unit can also propose a detour route based on real-time road construction information. Furthermore, the determination unit can also determine an optimal transportation route based on real-time traffic accident information. In this way, by reflecting changes in traffic conditions in real time, a more appropriate transportation route can be selected.

[0053] The determination unit can minimize the fuel consumption and travel distance of the ambulance when determining the transport route. For example, the determination unit selects a route that minimizes the fuel consumption of the ambulance. The determination unit can also select a route that minimizes the travel distance of the ambulance. Furthermore, the determination unit can also determine an optimal transport route based on the fuel consumption and travel distance. This enables efficient transport by minimizing the fuel consumption and travel distance of the ambulance.

[0054] When determining a transfer route, the determination unit can compare the acceptance situations of multiple hospitals and select the optimal route. The determination unit, for example, compares the acceptance situations of multiple hospitals and selects the optimal transfer route. The determination unit can also analyze the acceptance situations of hospitals in real time and select the optimal route. Furthermore, the determination unit can also determine a transfer route to the optimal hospital based on the acceptance situations. In this way, by comparing the acceptance situations of multiple hospitals, a more appropriate transfer route can be selected.

[0055] When determining a transportation route, the determination unit can select an optimal route by taking into consideration local weather information. For example, the determination unit selects a route that is less slippery when it is raining. The determination unit can also select the shortest route when it is sunny. Furthermore, the determination unit can also select a route that has been cleared of snow on a snowy day. In this way, a more appropriate transportation route can be selected by taking into consideration local weather information.

[0056] The determination unit can take into account the experience and skills of the ambulance driver when determining the transport route. For example, the determination unit selects the optimal route by taking into account the experience of the ambulance driver. The determination unit can also select the optimal route by taking into account the driver's skills. Furthermore, the determination unit can also determine the optimal transport route based on the driver's experience and skills. In this way, by taking into account the experience and skills of the ambulance driver, a more appropriate transport route can be selected.

[0057] When determining a transport route, the determination unit can select different routes depending on the urgency of the patient's symptoms. The determination unit selects the optimal route, for example, depending on the urgency of the patient's symptoms. The determination unit can also select the shortest route for patients with a high level of urgency. Furthermore, the determination unit can also select a route including detailed information for patients with a low level of urgency. This allows for more appropriate transport by selecting different routes depending on the urgency of the patient's symptoms.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can also collect local weather information in addition to ambulance dispatch status and information on doctors on duty in emergency departments. For example, the collection unit collects information that will affect ambulance movement, taking into account road conditions and poor visibility during rainy weather. The collection unit can also collect information on snow removal status and the risk of freezing on snowy days, and provide information to select the optimal transport route. Furthermore, the collection unit can collect meteorological information such as strong winds and typhoons in real time to support the safe movement of ambulances. This enables swift and safe hospital transport that takes meteorological information into account.

[0060] The analysis unit can also select a route that minimizes the ambulance's fuel consumption based on the collected information. For example, the analysis unit can analyze the ambulance's remaining fuel and fuel consumption data to propose the most efficient route. The analysis unit can also select a route that reduces fuel consumption by taking into account traffic congestion and waiting time at traffic lights. Furthermore, the analysis unit can select the shortest route to minimize the ambulance's driving distance. This enables efficient hospital transport with reduced fuel consumption.

[0061] The liaison unit can also grasp the patient's lifestyle habits and health condition in addition to the patient's past treatment history and allergy information. For example, the liaison unit obtains information about the patient's lifestyle habits, such as diet, exercise, and sleep, and creates an optimal treatment plan. The liaison unit can also understand the patient's medical history and current symptoms and select an appropriate medical institution. Furthermore, the liaison unit can determine treatment priorities based on the patient's health condition. This makes it possible to provide optimal treatment that takes into account the patient's lifestyle habits and health condition.

[0062] The decision unit can also determine the transport route by taking into account local disaster information in addition to traffic conditions and hospital acceptance status. For example, when a disaster such as an earthquake or flood occurs, the decision unit selects the optimal route by taking into account evacuation routes and the situation in the affected area. The decision unit can also reflect traffic restrictions and road closures during a disaster in real time and propose a safe transport route. Furthermore, the decision unit can select a route that minimizes risks affecting the movement of ambulances based on disaster information. This enables quick and safe transport to hospitals even during disasters.

[0063] The collection unit can collect the fuel status and maintenance status of each ambulance and evaluate the possibility of dispatch. For example, the collection unit collects the fuel status of each ambulance and evaluates whether it is possible to dispatch. The collection unit can also collect the maintenance status of each ambulance and check whether there are any obstacles to dispatch. Furthermore, the collection unit can select ambulances that can be dispatched based on the fuel status and maintenance status. In this way, by collecting the fuel status and maintenance status of each ambulance, the possibility of dispatch can be evaluated.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects information on ambulance dispatch status and doctors on duty at emergency outpatient clinics. Specifically, it collects information on the location and dispatch status of each ambulance, as well as information on doctors on duty at each hospital's emergency outpatient clinic. The collection unit also updates ambulance location information in real time, allowing it to adjust the optimal collection timing. It can also predict the schedules of doctors on duty at each hospital's emergency outpatient clinic and optimize the frequency of information collection. Step 2: The analysis unit performs analysis based on the information collected by the collection unit. Specifically, it selects the most suitable hospital based on the collected information. It can also refer to past data to predict the current situation and select the most suitable hospital. It can also apply different analysis algorithms depending on the patient's symptoms and level of urgency. Step 3: The linking unit acquires personal identification information based on the results obtained by the analysis unit. Specifically, it acquires personal identification information and ascertains the patient's past medical history and information about their regular hospital. It can also automatically acquire contact information for the patient's family and emergency contacts. It can also confirm the patient's insurance information and ability to pay. Step 4: The determination unit determines the optimal transport route to the hospital based on the personal identification information acquired by the collaboration unit. Specifically, the determination unit determines the optimal transport route to the hospital taking into account traffic conditions and hospital acceptance status. It can also reflect changes in traffic conditions in real time. It can also select a route that minimizes the ambulance's fuel consumption and driving distance.

[0066] (Example 2) A system according to an embodiment of the present invention constantly monitors ambulance dispatch status and emergency outpatient doctor on-call information, enabling the patient to be transported to a hospital as quickly as possible. This system collects and analyzes information about ambulance dispatch status and emergency outpatient doctor on-call information in real time. Next, in conjunction with personal identification information, the system ascertains the patient's past medical history and information about their regular hospital. This allows for rapid transport to the most appropriate hospital. For example, the system selects the nearest and most appropriate hospital based on the ambulance's location information and information about the hospital's doctor on-call, and determines the transport route taking into account traffic conditions and hospital availability. Furthermore, by inputting the patient's personal identification information, the system quickly obtains information about the patient's past medical history and regular hospital, providing all the information needed to provide optimal treatment. This allows the system to constantly monitor ambulance dispatch status and emergency outpatient doctor on-call information, enabling the patient to be transported to a hospital as quickly as possible. Furthermore, in conjunction with personal identification information, the system can quickly obtain information about the patient's past medical history and regular hospital, enabling the patient to be provided optimal treatment.

[0067] An emergency transport support system according to an embodiment includes a collection unit, an analysis unit, a linking unit, and a determination unit. The collection unit collects information on ambulance dispatch status or on-duty doctors at emergency outpatient clinics. The collection unit collects, for example, location information and dispatch status of each ambulance, and information on on-duty doctors at emergency outpatient clinics at each hospital. The collection unit can also update the location information of ambulances in real time and adjust the optimal collection timing. The collection unit can also predict the schedule of on-duty doctors at emergency outpatient clinics at each hospital and optimize the frequency of information collection. The analysis unit performs analysis based on the information collected by the collection unit. For example, the analysis unit selects an optimal hospital based on the collected information. The analysis unit can also predict the current situation by referring to past data and select an optimal hospital. The analysis unit can also apply different analysis algorithms depending on the patient's symptoms and urgency. The linking unit acquires personal identification information based on the results obtained by the analysis unit. For example, the linking unit acquires the personal identification information and ascertains the patient's past medical history and information on their regular hospital. The coordinating unit can also automatically acquire the patient's family contact information and emergency contact information. Furthermore, the coordinating unit can also confirm the patient's insurance information and ability to pay. The determining unit determines the optimal transport route to the hospital based on the personal identification information acquired by the coordinating unit. The determining unit determines the optimal transport route to the hospital, taking into account, for example, traffic conditions and hospital admissions. Furthermore, the determining unit can also reflect changes in traffic conditions in real time when determining the transport route. Furthermore, the determining unit can also select a route that minimizes the ambulance's fuel consumption and mileage. As a result, the emergency transport support system according to the embodiment constantly monitors the ambulance dispatch status and information on the doctor on duty in the emergency room, enabling the patient to be transported to the hospital as quickly as possible.

[0068] The collection unit can collect the location information and dispatch status of each ambulance, and information on the doctor on duty at each hospital's emergency outpatient department. For example, the collection unit collects the location information of each ambulance as GPS coordinates. The collection unit can also collect the dispatch status of each ambulance as the number of dispatches, the destination, and the time of dispatch. Furthermore, the collection unit can collect information on the doctor on duty at each hospital's emergency outpatient department as the doctor's name, specialty, and working hours. In this way, collecting the location information and dispatch status of each ambulance, and information on the doctor on duty at each hospital's emergency outpatient department enables a rapid response.

[0069] The analysis unit can select a hospital based on the collected information. The analysis unit, for example, selects the most suitable hospital based on the collected information. For example, the analysis unit can select the closest hospital based on the collected information. The analysis unit can also select a hospital based on the collected information, taking into account the availability of specialized departments and the hospital's capacity. Furthermore, the analysis unit can also select a hospital based on the collected information, taking into account traffic conditions and the hospital's acceptance status. This allows for rapid transfer by selecting the most suitable hospital based on the collected information.

[0070] The linking unit can acquire personal identification information and ascertain the patient's past medical history and information on the patient's regular hospital. The linking unit, for example, acquires personal identification information and ascertains the patient's past medical history and information on the patient's regular hospital. For example, the linking unit can acquire the patient's name, address, telephone number, and medical ID. The linking unit can also ascertain the patient's past medical history as a diagnosis, treatment details, and treatment period. Furthermore, the linking unit can ascertain information on the patient's regular hospital as the hospital name, location, and contact information. In this way, by acquiring personal identification information and ascertaining the patient's past medical history and information on the patient's regular hospital, optimal treatment can be provided.

[0071] The determination unit can determine a transfer route to the hospital based on traffic conditions or the hospital's acceptance status. The determination unit determines a transfer route to the hospital based on, for example, traffic conditions. For example, the determination unit can determine an optimal transfer route based on real-time traffic congestion information. The determination unit can also determine a transfer route to the hospital based on the hospital's acceptance status. For example, the determination unit can determine an optimal transfer route based on the availability of hospital beds and the availability of medical staff. Furthermore, the determination unit can determine a transfer route that reflects changes in traffic conditions in real time. This enables rapid transfer by determining an optimal transfer route to the hospital in consideration of traffic conditions and the hospital's acceptance status.

[0072] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize collecting ambulance location information to ensure a prompt response. Furthermore, if the user is relaxed, the collection unit can prioritize collecting information on doctors on duty at hospitals and provide detailed information. Furthermore, if the user is anxious, the collection unit can prioritize collecting information on the admission status of the nearest hospital to support prompt transportation. This enables more appropriate information collection by determining the priority of information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit updates the location information of each ambulance in real time and can adjust the optimal collection timing. For example, the collection unit acquires GPS data from each ambulance in real time and keeps the location information always up to date. The collection unit can also update the location information every time the ambulance moves and adjust the optimal collection timing. Furthermore, the collection unit can automatically calculate and update the optimal collection timing using AI based on the ambulance's location information. This allows the optimal collection timing to be adjusted by updating the location information of each ambulance in real time.

[0074] The collection unit can predict the schedules of doctors on duty at each hospital's emergency outpatient department and optimize the frequency of information collection. For example, the collection unit predicts the schedules of doctors on duty at each hospital and adjusts the frequency of information collection. For example, the collection unit uses past data analysis and AI predictions to predict the shift times of doctors on duty and updates the information at that timing. The collection unit can also use AI to automatically optimize the frequency of information collection based on the schedules of doctors on duty. This makes it possible to optimize the frequency of information collection by predicting the schedules of doctors on duty at each hospital's emergency outpatient department.

[0075] The collection unit can include the equipment status of ambulances and the skill levels of medical staff in the information it collects. For example, the collection unit collects the equipment status of each ambulance and checks whether the necessary medical equipment is available. The collection unit can also collect the skill levels of the medical staff of each ambulance and evaluate whether an appropriate response is possible. Furthermore, the collection unit can select the most appropriate ambulance based on the equipment status of the ambulance and the skill levels of the medical staff. In this way, by including the equipment status of ambulances and the skill levels of medical staff in the information it collects, more appropriate responses can be made.

[0076] The collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. For example, if the user is nervous, the collection unit can provide a simple, highly visible display method. If the user is relaxed, the collection unit can also provide a display method including detailed information. If the user is impatient, the collection unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the collected information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The collection unit can collect the fuel status and maintenance status of each ambulance and evaluate the possibility of dispatch. For example, the collection unit collects the fuel status of each ambulance and evaluates whether it is possible to dispatch. The collection unit can also collect the maintenance status of each ambulance and check whether there are any obstacles to dispatch. Furthermore, the collection unit can select ambulances that can be dispatched based on the fuel status and maintenance status. In this way, by collecting the fuel status and maintenance status of each ambulance, the possibility of dispatch can be evaluated.

[0078] The collection unit can collect information on available beds and the operating status of medical equipment at each hospital to evaluate its capacity. For example, the collection unit collects information on available beds at each hospital to evaluate whether it is possible to accept patients. The collection unit can also collect information on the operating status of medical equipment at each hospital to confirm whether the necessary treatment is possible. Furthermore, the collection unit can select hospitals that can accept patients based on the available beds and the operating status of medical equipment. In this way, by collecting information on available beds and the operating status of medical equipment at each hospital, it is possible to evaluate its capacity.

[0079] The collection unit can include local disaster information and traffic accident information in the information it collects. For example, the collection unit collects local disaster information and checks whether it will affect ambulance dispatches. The collection unit can also collect traffic accident information and select the optimal transport route. Furthermore, the collection unit can plan the optimal response based on the disaster information and traffic accident information. Thus, by including local disaster information and traffic accident information in the information it collects, more appropriate responses can be made.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is impatient, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The analysis unit can dynamically change the selection criteria for the most suitable hospital based on the collected information. For example, the analysis unit changes the selection criteria for the most suitable hospital in real time based on the collected information. The analysis unit can also dynamically adjust the selection criteria taking into account the hospital's acceptance situation and traffic conditions. Furthermore, the analysis unit can also change the selection criteria depending on the patient's symptoms and urgency. This makes it possible to select a more appropriate hospital by dynamically changing the selection criteria for the most suitable hospital based on the collected information.

[0082] During analysis, the analysis unit can refer to past data to predict the current situation and select the most appropriate hospital. For example, the analysis unit refers to past data to predict the current situation and select the most appropriate hospital. The analysis unit can also predict the most appropriate hospital based on the past history of ambulance dispatches. Furthermore, the analysis unit can also refer to the past hospital acceptance status and select a hospital that is currently able to accept patients. This makes it possible to select a more appropriate hospital by predicting the current situation by referring to past data.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the patient's symptoms and urgency. For example, the analysis unit applies the optimal analysis algorithm depending on the patient's symptoms. The analysis unit can also apply different analysis algorithms depending on the patient's urgency. Furthermore, the analysis unit can select the optimal analysis algorithm taking into consideration the patient's symptoms and urgency. This allows for more appropriate hospital selection by applying different analysis algorithms depending on the patient's symptoms and urgency.

[0084] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the priority of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important information. Furthermore, if the user is relaxed, the analysis unit can prioritize displaying detailed information. Furthermore, if the user is impatient, the analysis unit can prioritize displaying information that focuses on the main points. This enables more appropriate information to be provided by determining the priority of the analysis results based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During analysis, the analysis unit can select the most appropriate hospital by taking into account the distribution of medical resources in the region. For example, the analysis unit selects the most appropriate hospital by taking into account the distribution of medical resources in the region. The analysis unit can also select the most appropriate hospital based on the distribution of medical resources. Furthermore, the analysis unit can analyze the distribution of medical resources in the region in real time and select the most appropriate hospital. This makes it possible to select a more appropriate hospital by taking into account the distribution of medical resources in the region.

[0086] During the analysis, the analysis unit can select the most appropriate hospital by taking into consideration whether each hospital has a specialized department. For example, the analysis unit selects the most appropriate hospital by taking into consideration whether each hospital has a specialized department. The analysis unit can also select a hospital with a specialized department depending on the patient's symptoms. Furthermore, the analysis unit can analyze in real time whether each hospital has a specialized department and select the most appropriate hospital. This makes it possible to select a more appropriate hospital by taking into consideration whether each hospital has a specialized department.

[0087] During analysis, the analysis unit can select the most appropriate hospital by taking into consideration attribute information such as the patient's age and gender. The analysis unit selects the most appropriate hospital by taking into consideration, for example, the patient's age. The analysis unit can also select the most appropriate hospital by taking into consideration the patient's gender. Furthermore, the analysis unit can also select the most appropriate hospital based on attribute information such as the patient's age and gender. This makes it possible to select a more appropriate hospital by taking into consideration attribute information such as the patient's age and gender.

[0088] The linking unit can estimate the user's emotion and adjust the method for acquiring personal identification information based on the estimated user emotion. For example, the linking unit can estimate the user's emotion and adjust the method for acquiring personal identification information based on the estimated user emotion. For example, if the user is nervous, the linking unit can acquire personal identification information using a simple interface. Alternatively, if the user is relaxed, the linking unit can provide detailed input options and acquire personal identification information. Alternatively, if the user is impatient, the linking unit can prioritize voice input and quickly acquire personal identification information. This enables more appropriate information acquisition by adjusting the method for acquiring personal identification information based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The linking unit can include the patient's past treatment history and allergy information when acquiring personal identification information. For example, the linking unit includes the patient's past treatment history and allergy information when acquiring personal identification information. For example, the linking unit acquires the patient's past treatment history as a diagnosis, treatment content, and treatment period. The linking unit can also acquire the patient's allergy information as the type of allergen and the degree of allergic reaction. Furthermore, the linking unit can provide optimal treatment based on the past treatment history and allergy information. In this way, by including the patient's past treatment history and allergy information, more appropriate treatment can be provided.

[0090] The linking unit can automatically acquire the patient's family contact information and emergency contact information when acquiring personal identification information. For example, the linking unit automatically acquires the patient's family contact information and emergency contact information when acquiring personal identification information. For example, the linking unit acquires the patient's family contact information as name, telephone number, and address. The linking unit can also acquire the patient's emergency contact information as name, telephone number, and address. Furthermore, the linking unit can plan the optimal response based on the family contact information and emergency contact information. In this way, by automatically acquiring the patient's family contact information and emergency contact information, a prompt response is possible.

[0091] The coordination unit can confirm the patient's insurance information and ability to pay when acquiring personal identification information. For example, the coordination unit confirms the patient's insurance information and ability to pay when acquiring personal identification information. For example, the coordination unit acquires the patient's insurance information as the insurance company name, insurance number, and type of insurance. The coordination unit can also confirm the patient's ability to pay as income, expenses, and savings. Furthermore, the coordination unit can plan optimal treatment based on the insurance information and ability to pay. In this way, by confirming the patient's insurance information and ability to pay, more appropriate treatment can be provided.

[0092] The linking unit can estimate the user's emotion and adjust the display method of the personal identification information based on the estimated user emotion. For example, the linking unit can estimate the user's emotion and adjust the display method of the personal identification information based on the estimated user emotion. For example, if the user is nervous, the linking unit can provide a simple, highly visible display method. If the user is relaxed, the linking unit can also provide a display method including detailed information. If the user is impatient, the linking unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the personal identification information based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The linking unit can automatically acquire the patient's current location information when acquiring personal identification information. For example, the linking unit automatically acquires the patient's current location information when acquiring personal identification information. For example, when the patient opens the app, the linking unit automatically acquires the patient's current location and reflects it in the personal identification information. In addition, when the patient uses the app while on the move, the linking unit can also update the patient's current location in real time and reflect it in the personal identification information. Furthermore, the linking unit can plan the optimal response based on the patient's current location information. As a result, automatically acquiring the patient's current location information enables more appropriate responses.

[0094] The linking unit can refer to the patient's past medical institution visit history when acquiring personal identification information. For example, the linking unit refers to the patient's past medical institution visit history when acquiring personal identification information. For example, the linking unit acquires the patient's past medical institution visit history including the visit date and time, medical treatment details, and medical treatment results. The linking unit can also select the most appropriate medical institution based on the past visit history. Furthermore, the linking unit can refer to the patient's past visit history and plan the most appropriate treatment. As a result, more appropriate treatment can be provided by referring to the patient's past medical institution visit history.

[0095] The linking unit can take into account the patient's lifestyle and health condition when acquiring personal identification information. For example, the linking unit takes into account the patient's lifestyle and health condition when acquiring personal identification information. For example, the linking unit acquires the patient's lifestyle as diet, exercise, and sleep. The linking unit can also acquire the patient's health condition as medical history and current symptoms. Furthermore, the linking unit can plan optimal treatment based on the patient's lifestyle and health condition. This makes it possible to provide more appropriate treatment by taking into account the patient's lifestyle and health condition.

[0096] The determination unit can estimate the user's emotions and adjust the display method of the transportation route based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and adjusts the display method of the transportation route based on the estimated user emotions. For example, if the user is nervous, the determination unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the determination unit can provide a display method that includes detailed information. Furthermore, if the user is impatient, the determination unit can provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the transportation route based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The determination unit can reflect changes in traffic conditions in real time when determining a transportation route. The determination unit determines an optimal transportation route based on, for example, real-time traffic congestion information. The determination unit can also propose a detour route based on real-time road construction information. Furthermore, the determination unit can also determine an optimal transportation route based on real-time traffic accident information. In this way, by reflecting changes in traffic conditions in real time, a more appropriate transportation route can be selected.

[0098] The determination unit can minimize the fuel consumption and travel distance of the ambulance when determining the transport route. For example, the determination unit selects a route that minimizes the fuel consumption of the ambulance. The determination unit can also select a route that minimizes the travel distance of the ambulance. Furthermore, the determination unit can also determine an optimal transport route based on the fuel consumption and travel distance. This enables efficient transport by minimizing the fuel consumption and travel distance of the ambulance.

[0099] When determining a transfer route, the determination unit can compare the acceptance situations of multiple hospitals and select the optimal route. The determination unit, for example, compares the acceptance situations of multiple hospitals and selects the optimal transfer route. The determination unit can also analyze the acceptance situations of hospitals in real time and select the optimal route. Furthermore, the determination unit can also determine a transfer route to the optimal hospital based on the acceptance situations. In this way, by comparing the acceptance situations of multiple hospitals, a more appropriate transfer route can be selected.

[0100] The determination unit can estimate the user's emotions and determine the priority of transportation routes based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and determines the priority of transportation routes based on the estimated user emotions. For example, if the user is nervous, the determination unit can prioritize the quickest route. Furthermore, if the user is relaxed, the determination unit can prioritize a route including detailed information. Furthermore, if the user is impatient, the determination unit can prioritize a route that covers the main points. In this way, by determining the priority of transportation routes based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] When determining a transportation route, the determination unit can select an optimal route by taking into consideration local weather information. For example, the determination unit selects a route that is less slippery when it is raining. The determination unit can also select the shortest route when it is sunny. Furthermore, the determination unit can also select a route that has been cleared of snow on a snowy day. In this way, a more appropriate transportation route can be selected by taking into consideration local weather information.

[0102] The determination unit can take into account the experience and skills of the ambulance driver when determining the transport route. For example, the determination unit selects the optimal route by taking into account the experience of the ambulance driver. The determination unit can also select the optimal route by taking into account the driver's skills. Furthermore, the determination unit can also determine the optimal transport route based on the driver's experience and skills. In this way, by taking into account the experience and skills of the ambulance driver, a more appropriate transport route can be selected.

[0103] When determining a transport route, the determination unit can select different routes depending on the urgency of the patient's symptoms. The determination unit selects the optimal route, for example, depending on the urgency of the patient's symptoms. The determination unit can also select the shortest route for patients with a high level of urgency. Furthermore, the determination unit can also select a route including detailed information for patients with a low level of urgency. This allows for more appropriate transport by selecting different routes depending on the urgency of the patient's symptoms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, linking unit, and determination unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on ambulance dispatch status and on-duty doctors in the emergency room using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, selects the optimal hospital based on the collected information. The linking unit, realized, for example, by the control unit 46A of the smart device 14, acquires personal identification information and identifies the patient's past medical history and information on the patient's regular hospital. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the optimal transport route taking into account traffic conditions and hospital admissions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, collaboration unit, and determination unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on ambulance dispatch status and on-call doctors in the emergency room using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, selects the optimal hospital based on the collected information. The collaboration unit, realized, for example, by the control unit 46A of the smart glasses 214, acquires personal identification information and identifies the patient's past medical history and information on their primary care hospital. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the optimal transport route taking into account traffic conditions and hospital admissions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, linking unit, and determination unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information on ambulance dispatch status and the doctor on duty in the emergency room using the camera 42 and communication I / F 44 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects the optimal hospital based on the collected information. The linking unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and acquires personal identification information to grasp the patient's past medical history and information on the patient's regular hospital. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the optimal transport route taking into account traffic conditions and hospital admissions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, collaboration unit, and determination unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on ambulance dispatch status and the doctor on duty in the emergency room using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects the optimal hospital based on the collected information. The collaboration unit is realized, for example, by the control unit 46A of the robot 414 and acquires personal identification information to grasp the patient's past medical history and information on the patient's regular hospital. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the optimal transport route taking into account traffic conditions and hospital admissions.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can also collect local weather information in addition to ambulance dispatch status and information on doctors on duty in emergency departments. For example, the collection unit collects information that will affect ambulance movement, taking into account road conditions and poor visibility during rainy weather. The collection unit can also collect information on snow removal status and the risk of freezing on snowy days, and provide information to select the optimal transport route. Furthermore, the collection unit can collect meteorological information such as strong winds and typhoons in real time to support the safe movement of ambulances. This enables swift and safe hospital transport that takes meteorological information into account.

[0106] The analysis unit can also select a route that minimizes the ambulance's fuel consumption based on the collected information. For example, the analysis unit can analyze the ambulance's remaining fuel and fuel consumption data to propose the most efficient route. The analysis unit can also select a route that reduces fuel consumption by taking into account traffic congestion and waiting time at traffic lights. Furthermore, the analysis unit can select the shortest route to minimize the ambulance's driving distance. This enables efficient hospital transport with reduced fuel consumption.

[0107] The liaison unit can also grasp the patient's lifestyle habits and health condition in addition to the patient's past treatment history and allergy information. For example, the liaison unit obtains information about the patient's lifestyle habits, such as diet, exercise, and sleep, and creates an optimal treatment plan. The liaison unit can also understand the patient's medical history and current symptoms and select an appropriate medical institution. Furthermore, the liaison unit can determine treatment priorities based on the patient's health condition. This makes it possible to provide optimal treatment that takes into account the patient's lifestyle habits and health condition.

[0108] The decision unit can also determine the transport route by taking into account local disaster information in addition to traffic conditions and hospital acceptance status. For example, when a disaster such as an earthquake or flood occurs, the decision unit selects the optimal route by taking into account evacuation routes and the situation in the affected area. The decision unit can also reflect traffic restrictions and road closures during a disaster in real time and propose a safe transport route. Furthermore, the decision unit can select a route that minimizes risks affecting the movement of ambulances based on disaster information. This enables quick and safe transport to hospitals even during disasters.

[0109] The collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. For example, if the user is nervous, the collection unit can provide a simple, highly visible display method. If the user is relaxed, the collection unit can also provide a display method that includes detailed information. If the user is impatient, the collection unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the collected information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important information. Also, if the user is relaxed, the analysis unit can prioritize displaying detailed information. Also, if the user is impatient, the analysis unit can prioritize displaying information that covers the main points. This enables more appropriate information to be provided by determining the priority of analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The linking unit can estimate the user's emotions and adjust the method for acquiring personal identification information based on the estimated user emotions. For example, if the user is nervous, the linking unit can acquire personal identification information using a simple interface. Alternatively, if the user is relaxed, the linking unit can provide detailed input options and acquire personal identification information. Alternatively, if the user is impatient, the linking unit can prioritize voice input and quickly acquire personal identification information. This allows for more appropriate information acquisition by adjusting the method for acquiring personal identification information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The determination unit can estimate the user's emotions and adjust the display method of the transportation route based on the estimated user emotions. For example, if the user is nervous, the determination unit can provide a simple, highly visible display method. If the user is relaxed, the determination unit can also provide a display method that includes detailed information. If the user is impatient, the determination unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the transportation route based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The determination unit can estimate the user's emotions and determine the priority of transportation routes based on the estimated user emotions. For example, if the user is nervous, the determination unit can prioritize the quickest route. If the user is relaxed, the determination unit can also prioritize a route that includes detailed information. If the user is impatient, the determination unit can also prioritize a route that covers the main points. This enables more appropriate information to be provided by determining the priority of transportation routes based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0114] The collection unit can collect the fuel status and maintenance status of each ambulance and evaluate the possibility of dispatch. For example, the collection unit collects the fuel status of each ambulance and evaluates whether it is possible to dispatch. The collection unit can also collect the maintenance status of each ambulance and check whether there are any obstacles to dispatch. Furthermore, the collection unit can select ambulances that can be dispatched based on the fuel status and maintenance status. In this way, by collecting the fuel status and maintenance status of each ambulance, the possibility of dispatch can be evaluated.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects information on ambulance dispatch status and doctors on duty at emergency outpatient clinics. Specifically, it collects information on the location and dispatch status of each ambulance, as well as information on doctors on duty at each hospital's emergency outpatient clinic. The collection unit also updates ambulance location information in real time, allowing it to adjust the optimal collection timing. It can also predict the schedules of doctors on duty at each hospital's emergency outpatient clinic and optimize the frequency of information collection. Step 2: The analysis unit performs analysis based on the information collected by the collection unit. Specifically, it selects the most suitable hospital based on the collected information. It can also refer to past data to predict the current situation and select the most suitable hospital. It can also apply different analysis algorithms depending on the patient's symptoms and level of urgency. Step 3: The linking unit acquires personal identification information based on the results obtained by the analysis unit. Specifically, it acquires personal identification information and ascertains the patient's past medical history and information about their regular hospital. It can also automatically acquire contact information for the patient's family and emergency contacts. It can also confirm the patient's insurance information and ability to pay. Step 4: The determination unit determines the optimal transport route to the hospital based on the personal identification information acquired by the collaboration unit. Specifically, the determination unit determines the optimal transport route to the hospital taking into account traffic conditions and hospital acceptance status. It can also reflect changes in traffic conditions in real time. It can also select a route that minimizes the ambulance's fuel consumption and driving distance.

[0117] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects information on ambulance dispatch status or doctor on duty at the emergency outpatient clinic; an analysis unit that analyzes the information collected by the collection unit; a linkage unit that acquires personal identification information based on the results obtained by the analysis unit; and a decision unit that determines a transport route to a hospital based on the personal identification information acquired by the linkage unit.

2. The collecting unit Collect information on the location and dispatch status of each ambulance, as well as information on doctors on duty at each hospital's emergency room.

2. The system of claim 1.

3. The system according to claim 1 , wherein the analysis unit selects a hospital based on the collected information.

4. The linking unit is Obtain personal identification information to understand the patient's past medical history and regular hospital information 2. The system of claim 1.

5. The system according to claim 1 , wherein the determination unit determines a transport route to a hospital based on traffic conditions or hospital acceptance conditions.

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Real-time updates of each ambulance's location information to coordinate optimal collection times 2. The system of claim 1.

8. The collecting unit Predict the schedules of doctors on call at each hospital's emergency department and optimize the frequency of information collection.

2. The system of claim 1.

9. The collecting unit Information collected includes ambulance equipment status and medical staff skill level 2. The system of claim 1.

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