System

Abstract

To provide a system for proposing a carry-in destination by rapidly searching an appropriate hospital in emergency conveyance.SOLUTION: A system includes a reception section, an analysis section, a search section, and a proposal section. The reception section receives information on a patient. The analysis section analyzes information received by the reception section. The search section searches a hospital on the basis of information analyzed by the analysis section. The proposal section proposes a carry-in destination on the basis of a result searched by the search section.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of quickly searching for an appropriate hospital and deciding on the hospital to take a patient to in an emergency relied on telephone confirmation, which was time-consuming.

[0005] The system according to the embodiment aims to quickly search for an appropriate hospital in the event of emergency transport and to suggest a hospital destination. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a suggestion unit. The reception unit receives patient information. The analysis unit analyzes the information received by the reception unit. The search unit searches for hospitals based on the information analyzed by the analysis unit. The suggestion unit suggests a delivery destination based on the results of the search by the search unit. Effect of the Invention

[0007] The system according to the embodiment can quickly search for an appropriate hospital in the event of emergency transport and suggest a hospital destination. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The emergency transport support system according to the embodiment of the present invention is a system that instantly searches for an available hospital room or a specialized field in emergency transport at a hospital and determines the optimal destination. This emergency transport support system can instantly search by using a generating AI. For example, an ambulance crew member inputs information about the patient. Next, the generating AI analyzes the information and searches for an available hospital room or a hospital with a specialized field. Based on the search results, the optimal destination is proposed. This system significantly improves the efficiency of emergency transport and enables rapid treatment of the patient. For example, an ambulance crew member inputs information about the patient's symptoms, urgency, and required specialized field. This information is input to the generating AI. Next, the generating AI analyzes the input information. Based on the patient's information, the generating AI searches for an available hospital room or a hospital with a specialized field. For example, in the case of a patient with a heart attack, a hospital specializing in heart disease is searched for. Based on the search results, the generating AI proposes the optimal destination. For example, a hospital with an available hospital room and a doctor specializing in heart disease is proposed. This proposal is instantly provided to the ambulance crew member. This system significantly improves the efficiency of emergency transport. Paramedics can instantly determine the optimal transport destination without having to check by phone. This also enables patients to receive treatment more quickly, which is expected to improve the survival rate. This allows the emergency transport support system to efficiently accept, analyze, search, and suggest patient information.

[0029] The emergency transport support system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit accepts information on the patient. The patient information includes, for example, medical history, symptoms, personal information, etc., but is not limited to such examples. The reception unit inputs, for example, information on the patient's symptoms, urgency, and required specialty. The analysis unit uses a generation AI to analyze the information accepted by the reception unit. The analysis is performed, for example, by a method such as data mining or statistical analysis, but is not limited to such examples. The search unit uses a generation AI to search for a hospital based on the information analyzed by the analysis unit. The search is performed, for example, based on a search algorithm or filtering conditions, but is not limited to such examples. The proposal unit proposes an optimal delivery destination based on the results searched by the search unit. The proposal is performed, for example, based on a proposal algorithm or evaluation criteria, but is not limited to such examples. As a result, the emergency transport support system according to the embodiment can efficiently accept, analyze, search, and propose patient information. For example, the reception unit provides an interface for inputting patient information. The analysis unit uses generative AI to analyze patient information and identify the required specialty and available hospital rooms. The search unit uses generative AI to search for hospitals based on the analysis results and identify the optimal destination for the patient. The suggestion unit suggests the optimal destination for the patient based on the search results and provides it to emergency personnel. This significantly improves the efficiency of emergency transport and enables faster treatment for patients.

[0030] The reception unit receives information on the patient. The patient information includes, but is not limited to, for example, medical history, symptoms, and personal information. The reception unit inputs information such as, for example, the patient's symptoms, urgency, and required specialty. Specifically, the reception unit provides an interface designed to enable ambulance personnel and medical staff to input information quickly and accurately. This interface is available on tablets, smartphones, dedicated input devices, and the like, and also has functions such as voice input and barcode scanning. For example, an ambulance personnel can check the patient's symptoms on-site and record the symptoms using voice input. It is also possible to automatically obtain past medical history, allergy information, and the like by scanning the patient's medical ID card. Furthermore, the reception unit can transmit the input information to a central database in real time and share it with other departments. This allows the reception unit to quickly and accurately collect patient information and improve the efficiency of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed, for example, by a method such as data mining or statistical analysis, but is not limited to such examples. Specifically, the generation AI identifies the optimal treatment method and the necessary specialty based on information such as the patient's symptoms, medical history, and urgency. For example, the generation AI uses natural language processing technology to analyze the text data of the input symptoms and identify the severity of the symptoms and related diseases. It can also analyze the treatment results for similar cases based on past data and propose the optimal treatment method. Furthermore, the generation AI considers the availability of hospitals and doctor schedule information, which are updated in real time, to identify the optimal destination. As a result, the analysis unit can quickly and accurately analyze the patient's information and identify the optimal treatment method and destination.

[0032] The search unit uses the generation AI to search for a hospital based on the information analyzed by the analysis unit. The search is performed, for example, based on a search algorithm and filtering conditions, but is not limited to such examples. Specifically, the generation AI searches for the most suitable hospital based on information such as the patient's symptoms and urgency, the required specialty, and the availability of hospitals. For example, the generation AI uses a geographic information system (GIS) to identify the hospital closest to the patient's current location and calculates the optimal route taking into account traffic conditions and travel time. In addition, the generation AI can identify the hospital that is most suitable for the patient's symptoms based on the hospital's specialty, facilities, and doctor's schedule information. Furthermore, the generation AI identifies the optimal destination taking into account the availability of hospitals, which is updated in real time. As a result, the search unit can quickly and accurately search for the most suitable hospital based on the patient's information and support the selection of the destination.

[0033] The suggestion unit suggests an optimal destination based on the results searched by the search unit. The suggestion is made based on, for example, a suggestion algorithm or an evaluation criterion, but is not limited to such examples. Specifically, the suggestion unit uses the generation AI to identify the optimal destination based on the search results and provide it to the ambulance crew. For example, the generation AI comprehensively evaluates information such as the patient's symptoms and urgency, and the availability of hospitals, and suggests the optimal destination. In addition, the suggestion unit provides an interface for displaying the suggested content in an easy-to-understand manner to the ambulance crew. For example, information such as the name, address, contact information, and travel route of the optimal destination hospital can be displayed on the screen of a tablet or smartphone, and attention can be drawn by voice guidance, vibration notification, etc. Furthermore, the suggestion unit can update the suggested content in real time and make suggestions based on the latest information. As a result, the suggestion unit can provide the ambulance crew with prompt and accurate suggestions of destinations, and support prompt treatment of the patient.

[0034] The emergency transport support system includes an update unit that manages the update frequency of the database. The update unit manages the update frequency of the database. The update frequency includes, for example, periodic update and real-time update, but is not limited to such examples. For example, the update unit sets an update schedule for the database and updates the data periodically. The update unit also has a function of updating data in real time. For example, the update unit receives the latest information from a hospital in real time and updates the database. In this way, the update frequency of the database can be managed to provide the latest information. A part or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit inputs information from the hospital to the generation AI and causes the generation AI to update the database.

[0035] The emergency transport support system includes a providing unit that manages the information provided by the hospital. The providing unit manages the information provided by the hospital. The information provided includes, for example, a data format and a timing of providing, but is not limited to such examples. For example, the providing unit receives information from the hospital and registers it in a database. The providing unit also has a function of managing the timing of the information provided by the hospital. For example, the providing unit sets a schedule for the information provided by the hospital and receives information periodically. This allows accurate information to be provided by managing the information provided by the hospital. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs information from the hospital to a generating AI and allows the generating AI to manage the information provided.

[0036] The emergency transport support system includes a protection unit that protects the privacy of the patient. The protection unit protects the privacy of the patient. Privacy protection includes, for example, data encryption and access control, but is not limited to such examples. For example, the protection unit encrypts the patient's information to prevent a third party from accessing it. In addition, the protection unit performs access control so that only specific users can access the patient's information. For example, the protection unit encrypts the patient's information and stores it in a database. In addition, the protection unit creates an access control list so that only specific users can access the information. This protects the patient's privacy and allows the system to be used with peace of mind. Some or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit inputs the patient's information into the generation AI and causes the generation AI to execute the privacy protection processing.

[0037] The emergency transport support system includes a priority section that performs prioritization. The priority section performs prioritization. Prioritization includes, for example, urgency and importance, but is not limited to such examples. The priority section determines the priority of information based on, for example, the symptoms and urgency of the patient. The priority section also has a function of determining the priority of information based on importance. For example, when the urgency of the patient is high, the priority section displays only the minimum necessary input items. Also, when the health condition of the patient is stable, the priority section displays detailed input items. This enables efficient processing by performing prioritization. Part or all of the above-mentioned processing in the priority section may be performed, for example, using AI, or may be performed without using AI. For example, the priority section can input patient information to the generation AI and cause the generation AI to execute the prioritization processing.

[0038] The search unit can search for a hospital based on the patient's symptoms or urgency. The search unit searches for a hospital based on the patient's symptoms or urgency, for example. The symptoms include, but are not limited to, the degree of pain and fever. The urgency includes, but is not limited to, the degree of life danger and the rate at which the symptoms progress. For example, in the case of a patient with a heart attack, the search unit searches for a hospital that specializes in heart disease. In addition, when the urgency is high, the search unit can also search for the nearest hospital with priority. This makes it possible to quickly find an appropriate hospital by searching for a hospital based on the patient's symptoms and urgency. Part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input patient information to the generation AI and cause the generation AI to search for a hospital.

[0039] The suggestion unit can suggest a destination considering the distance to the hospital or traffic conditions. The suggestion unit suggests a destination considering, for example, the distance to the hospital and traffic conditions. The distance includes, for example, a straight-line distance and a travel time, but is not limited to such examples. The traffic conditions include, for example, traffic congestion and the operation status of public transportation, but is not limited to such examples. The suggestion unit, for example, preferentially suggests a hospital that is closest to the patient's current location. The suggestion unit can also suggest a hospital that can be reached most quickly, taking into account traffic conditions. This makes it possible to quickly transport the patient by proposing an optimal destination considering the distance to the hospital and traffic conditions. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the patient to the generation AI and cause the generation AI to execute the proposal of the destination.

[0040] The reception unit can analyze the patient's past medical history and provide an optimal information input format. The reception unit, for example, analyzes the patient's past medical history and provides an optimal information input format. The medical history includes, for example, past medical records and prescription history, but is not limited to such examples. For example, the reception unit automatically displays related input items based on the patient's past medical history. The reception unit can also automatically select a required specialty from the patient's past treatment history. Furthermore, the reception unit can also refer to the patient's past medical data and customize the input items. In this way, the patient's past medical history can be analyzed to provide an optimal information input format. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's medical history data to the generation AI and cause the generation AI to provide an optimal information input format.

[0041] The reception unit can dynamically change the input items based on the patient's current health condition and urgency when inputting information. The reception unit dynamically changes the input items based on the patient's current health condition and urgency when inputting information, for example. The health condition includes, but is not limited to, vital signs and self-reporting. The urgency includes, but is not limited to, the degree of life danger and the rate of progression of symptoms. For example, when the urgency of the patient is high, the reception unit displays only the minimum necessary input items. In addition, when the patient's health condition is stable, the reception unit can also display detailed input items. Furthermore, the reception unit can dynamically change the input items according to the patient's symptoms. This makes it possible to input appropriate information by dynamically changing the input items according to the patient's health condition and urgency. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's health condition data to the generation AI and cause the generation AI to dynamically change the input items.

[0042] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the patient when inputting information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the patient when inputting information. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the reception unit preferentially inputs nearby hospital information based on the patient's current location. In addition, the reception unit can also refer to the geographical location information of the patient and preferentially input information of related medical institutions. Furthermore, the reception unit can also suggest an optimal delivery destination based on the patient's location information. In this way, by considering the geographical location information of the patient, highly relevant information can be preferentially input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's location information data to the generation AI and cause the generation AI to execute the preferential input of highly relevant information.

[0043] The reception unit can analyze the social media activity of the patient at the time of inputting information and input related information. The reception unit, for example, analyzes the social media activity of the patient at the time of inputting information and inputs related information. The social media activity includes, for example, the content of the post and the number of likes, but is not limited to such examples. For example, the reception unit analyzes the social media post of the patient and inputs information related to the health condition. The reception unit can also estimate the past medical history from the social media activity of the patient and customize the input items. Furthermore, the reception unit can automatically select the required specialty based on the social media data of the patient. In this way, the related information can be input by analyzing the social media activity of the patient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the patient to the generation AI and cause the generation AI to input the related information.

[0044] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during the analysis. The analysis unit, for example, optimizes the analysis algorithm by referring to the patient's past medical data during the analysis. The medical data includes, for example, medical records and test results, but is not limited to such examples. For example, the analysis unit selects an optimal analysis algorithm based on the patient's past medical history. The analysis unit can also adjust the analysis algorithm by referring to the patient's past treatment history. Furthermore, the analysis unit can analyze the patient's past medical data and optimize the analysis algorithm. As a result, the analysis algorithm can be optimized by referring to the patient's past medical data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the patient's medical data to the generation AI and cause the generation AI to execute optimization of the analysis algorithm.

[0045] The analysis unit can apply different analysis methods depending on the symptoms and urgency of the patient during analysis. The analysis unit applies different analysis methods depending on the symptoms and urgency of the patient during analysis, for example. Symptoms include, but are not limited to, the degree of pain and fever. Urgency includes, but are not limited to, the degree of life danger and the rate of progression of symptoms. For example, the analysis unit applies a rapid analysis method when the urgency of the patient is high. The analysis unit can also select an optimal analysis method depending on the symptoms of the patient. Furthermore, the analysis unit can dynamically change the analysis method based on the urgency of the patient. This makes it possible to perform appropriate analysis by applying the analysis method depending on the symptoms and urgency of the patient. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the symptom data of the patient to the generation AI and cause the generation AI to apply the analysis method.

[0046] The analysis unit can adjust the analysis result in consideration of the geographical location information of the patient during the analysis. The analysis unit, for example, adjusts the analysis result in consideration of the geographical location information of the patient during the analysis. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the analysis unit provides an optimal analysis result based on the current location of the patient. The analysis unit can also refer to the geographical location information of the patient and reflect information of related medical institutions in the analysis result. Furthermore, the analysis unit can also suggest an optimal delivery destination based on the location information of the patient. In this way, by considering the geographical location information of the patient, an appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the location information data of the patient to the generation AI and cause the generation AI to adjust the analysis result.

[0047] The analysis unit can improve the accuracy of the analysis by referring to the patient's related literature during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the patient's related literature during the analysis, for example. The related literature includes, but is not limited to, academic papers and medical guidelines. For example, the analysis unit refers to the latest medical literature related to the patient's symptoms and reflects it in the analysis result. The analysis unit can also optimize the analysis algorithm by referring to literature related to the patient's past treatment history. Furthermore, the analysis unit can automatically search for related literature based on the patient's symptoms and reflect it in the analysis result. Thereby, the accuracy of the analysis can be improved by referring to the patient's related literature. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the patient's symptom data to the generation AI and cause the generation AI to execute the reference of related literature.

[0048] The search unit can improve the accuracy of the search by considering the mutual relationships of the hospitals at the time of the search. The search unit, for example, improves the accuracy of the search by considering the mutual relationships of the hospitals at the time of the search. The mutual relationships include, for example, partnership relationships and introduction records, but are not limited to such examples. For example, the search unit searches for an optimal delivery destination based on cooperation information between hospitals. In addition, the search unit can optimize the search results by considering the specialty fields and facility information of the hospitals. Furthermore, the search unit can analyze the mutual relationships of the hospitals and propose the most suitable hospital. As a result, the accuracy of the search can be improved by considering the mutual relationships of the hospitals. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the mutual relationship data of the hospitals to the generation AI and cause the generation AI to execute the improvement of the search accuracy.

[0049] The search unit can perform a search while considering the specialty and equipment information of the hospital. The search unit performs a search while considering the specialty and equipment information of the hospital, for example, while performing a search. The specialty includes, for example, internal medicine, surgery, obstetrics and gynecology, but is not limited to such examples. The equipment information includes, for example, the number of MRI devices and operating rooms, but is not limited to such examples. For example, the search unit searches for a hospital with a specialty according to the patient's symptoms. The search unit can also search for an optimal delivery destination based on the equipment information of the hospital. Furthermore, the search unit can combine the specialty and equipment information of the hospital to propose an optimal hospital. In this way, an appropriate hospital can be searched for by considering the specialty and equipment information of the hospital. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the specialty data of the hospital to the generation AI and cause the generation AI to execute the search.

[0050] The search unit can perform a search while taking into consideration the geographical distribution of hospitals. For example, the search unit performs a search while taking into consideration the geographical distribution of hospitals. The geographical distribution includes, for example, urban areas and rural areas, but is not limited to such examples. For example, the search unit preferentially searches nearby hospitals based on the patient's current location. The search unit can also refer to the geographical distribution of hospitals and suggest the optimal delivery destination. Furthermore, the search unit can search for the optimal hospital based on the patient's location information. In this way, an appropriate hospital can be searched for by taking into consideration the geographical distribution of hospitals. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the geographical distribution data of hospitals to the generation AI and cause the generation AI to execute the search.

[0051] The search unit can improve the accuracy of the search by referring to the related literature of the hospital at the time of the search. The search unit can improve the accuracy of the search by referring to the related literature of the hospital at the time of the search, for example. The related literature includes, but is not limited to, academic papers and medical guidelines. For example, the search unit can refer to the latest medical literature related to the specialty of the hospital and reflect it in the search result. The search unit can also optimize the search algorithm by referring to the literature related to the equipment information of the hospital. Furthermore, the search unit can automatically search for related literature based on the specialty of the hospital and reflect it in the search result. Thereby, the accuracy of the search can be improved by referring to the related literature of the hospital. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the related literature data of the hospital to the generation AI and cause the generation AI to execute the improvement of the search accuracy.

[0052] The suggestion unit can adjust the level of detail of the proposal based on the importance of the hospital at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the hospital at the time of suggestion. The importance includes, for example, the size and specialty of the hospital, but is not limited to such examples. For example, the suggestion unit preferentially suggests hospitals with high importance. In addition, the suggestion unit can adjust the level of detail of the proposal according to the importance of the hospital. Furthermore, the suggestion unit can make a detailed proposal for a hospital with high importance. As a result, an appropriate proposal can be made by adjusting the level of detail of the proposal based on the importance of the hospital. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the hospital to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0053] The suggestion unit can apply different proposal algorithms depending on the category of the hospital at the time of suggestion. For example, the suggestion unit applies different proposal algorithms depending on the category of the hospital at the time of suggestion. The categories include, for example, general hospitals and specialized hospitals, but are not limited to such examples. For example, the suggestion unit applies a quick proposal algorithm to a hospital with a high degree of urgency. In addition, the suggestion unit can also apply a detailed proposal algorithm to a hospital specialized in a specialized field. Furthermore, the suggestion unit can also apply a standard proposal algorithm to a general hospital. As a result, appropriate proposals can be made by applying different proposal algorithms depending on the category of the hospital. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input hospital category data to the generation AI and cause the generation AI to execute the application of the proposal algorithm.

[0054] The suggestion unit can determine the priority of the proposal in consideration of the distance of the hospital and the traffic conditions at the time of proposal. The suggestion unit, for example, determines the priority of the proposal in consideration of the distance of the hospital and the traffic conditions at the time of proposal. The distance includes, for example, a straight-line distance and a travel time, but is not limited to such examples. The traffic conditions include, for example, traffic congestion and the operation conditions of public transportation, but is not limited to such examples. For example, the suggestion unit preferentially suggests a hospital that is closest to the patient's current location. In addition, the suggestion unit can also consider the traffic conditions and propose a hospital that can be reached most quickly. Furthermore, the suggestion unit can comprehensively judge the distance and the traffic conditions and propose an optimal hospital. As a result, appropriate proposals can be made by considering the distance of the hospital and the traffic conditions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the distance of the hospital and the traffic conditions data to the generation AI and cause the generation AI to execute the priority decision of the proposal.

[0055] The suggestion unit can adjust the order of the suggestions based on the relevance of the hospitals at the time of suggestion. The suggestion unit, for example, adjusts the order of the suggestions based on the relevance of the hospitals at the time of suggestion. The relevance includes, for example, a match of medical departments and a past referral record, but is not limited to such examples. For example, the suggestion unit preferentially suggests a hospital that is most related to the patient's symptoms. The suggestion unit can also suggest a highly related hospital based on the hospital's specialty. Furthermore, the suggestion unit can also preferentially suggest a highly related hospital according to the urgency of the patient. This makes it possible to make an appropriate suggestion by adjusting the order of the suggestions based on the relevance of the hospitals. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input hospital relevance data to the generation AI and cause the generation AI to execute the adjustment of the order of the suggestions.

[0056] The update unit can optimize the update algorithm by referring to past update data at the time of updating. The update unit optimizes the update algorithm by referring to past update data, for example, at the time of updating. The update data includes, for example, past medical records and patient feedback, but is not limited to such examples. For example, the update unit analyzes past update data and selects an optimal update algorithm. The update unit can also adjust the update algorithm based on the update history. Furthermore, the update unit can also optimize the update algorithm by referring to past update data. As a result, the update algorithm can be optimized by referring to the past update data. A part or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit inputs past update data to the generation AI and causes the generation AI to execute optimization of the update algorithm.

[0057] The update unit can weight the update data in consideration of the geographical location information of the hospital at the time of update. The update unit, for example, weights the update data in consideration of the geographical location information of the hospital at the time of update. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the update unit preferentially updates data of a hospital close to the patient's current location. The update unit can also weight the update data based on the geographical location information of the hospital. Furthermore, the update unit can also select optimal update data by referring to the patient's location information. In this way, appropriate data can be preferentially updated by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input geographical location information data of the hospital to the generation AI and cause the generation AI to execute weighting of the update data.

[0058] The providing unit can select the optimal information providing method by referring to the hospital's past providing history when providing information. For example, the providing unit selects the optimal information providing method by referring to the hospital's past providing history when providing information. The providing history includes, for example, past providing data and feedback of the providing result, but is not limited to such examples. For example, the providing unit selects the optimal information providing method based on the hospital's past providing history. In addition, the providing unit can also adjust the information providing method by referring to the providing history. Furthermore, the providing unit can analyze the past providing history and select the optimal information providing method. As a result, the optimal information providing method can be selected by referring to the hospital's past providing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past providing history data to the generating AI and cause the generating AI to select the optimal information providing method.

[0059] The providing unit can select the optimal providing method in consideration of the geographical location information of the hospital when providing information. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the hospital when providing information. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the providing unit preferentially provides information of a hospital close to the patient's current location. In addition, the providing unit can also select the optimal information providing method based on the geographical location information of the hospital. Furthermore, the providing unit can also select the optimal information providing method by referring to the patient's location information. As a result, appropriate information can be provided by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the hospital to the generating AI and cause the generating AI to select the optimal providing method.

[0060] The protection unit can optimize the protection algorithm by referring to past protection data during privacy protection. The protection unit optimizes the protection algorithm by referring to past protection data during privacy protection, for example. The protection data includes, for example, past protection records and feedback of protection results, but is not limited to such examples. For example, the protection unit analyzes past protection data and selects an optimal protection algorithm. The protection unit can also adjust the protection algorithm based on the protection history. Furthermore, the protection unit can also optimize the protection algorithm by referring to past protection data. As a result, the protection algorithm can be optimized by referring to the past protection data. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input past protection data to the generation AI and cause the generation AI to execute optimization of the protection algorithm.

[0061] The protection unit can weight the protection data in consideration of the geographical location information of the hospital during privacy protection. The protection unit, for example, weights the protection data in consideration of the geographical location information of the hospital during privacy protection. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the protection unit preferentially protects data of a hospital close to the patient's current location. The protection unit can also weight the protection data based on the geographical location information of the hospital. Furthermore, the protection unit can also refer to the patient's location information and select optimal protection data. As a result, appropriate data can be preferentially protected by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the geographical location information data of the hospital to the generation AI and cause the generation AI to execute weighting of the protection data.

[0062] The priority unit can optimize the priority algorithm by referring to past priority data when prioritizing. The priority unit optimizes the priority algorithm by referring to past priority data when prioritizing, for example. The priority data includes, for example, past priority records and feedback of priority results, but is not limited to such examples. For example, the priority unit analyzes past priority data and selects an optimal priority algorithm. The priority unit can also adjust the priority algorithm based on the priority history. Furthermore, the priority unit can also optimize the priority algorithm by referring to past priority data. In this way, the priority algorithm can be optimized by referring to the past priority data. A part or all of the above-mentioned processing in the priority unit may be performed, for example, using AI, or may be performed without using AI. For example, the priority unit can input past priority data to the generation AI and cause the generation AI to execute optimization of the priority algorithm.

[0063] The priority unit can weight the priority data in consideration of the geographical location information of the hospital when prioritizing. For example, the priority unit weights the priority data in consideration of the geographical location information of the hospital when prioritizing. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the priority unit preferentially processes data of a hospital close to the patient's current location. The priority unit can also weight the priority data based on the geographical location information of the hospital. Furthermore, the priority unit can also refer to the patient's location information and select optimal priority data. In this way, appropriate data can be preferentially processed by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the priority unit may be performed, for example, using AI, or may be performed without using AI. For example, the priority unit can input geographical location information data of the hospital to the generation AI and cause the generation AI to execute weighting of the priority data.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The emergency transport support system may further include a monitoring unit that monitors the patient's vital signs in real time. The monitoring unit continuously monitors vital signs, such as heart rate, blood pressure, and oxygen saturation, and immediately notifies the patient if an abnormality is detected. The monitoring unit may also transmit vital sign data of the patient to an analysis unit, which may then reevaluate the optimal destination based on the data. The monitoring unit may also record changes in the patient's vital signs and use the data for subsequent medical treatment. This allows the patient's condition to be understood in real time, enabling prompt and appropriate response.

[0066] The emergency transport support system can further include a notification unit that automatically notifies the patient's family and emergency contacts. For example, when the patient's transport destination has been decided, the notification unit sends a notification to the patient's family and emergency contacts by SMS or email. The notification unit can also share the patient's condition and the progress of the transport in real time. Furthermore, the notification unit can receive replies from the family and emergency contacts and provide information to emergency personnel as necessary. This makes it possible to provide the patient's family and emergency contacts with quick and accurate information, providing a sense of security.

[0067] The emergency transport support system may further include a cloud management unit that manages the patient's past medical data on the cloud. The cloud management unit, for example, stores the patient's medical records and test results in the cloud and allows them to be accessed as needed. The cloud management unit also facilitates data sharing between medical institutions and enables rapid provision of information. Furthermore, the cloud management unit can also regularly back up data to ensure data safety. This allows the patient's medical data to be managed efficiently and necessary information to be provided quickly.

[0068] The emergency transport support system can further include a multilingual support unit that provides information according to the patient's language and culture. For example, the multilingual support unit translates information according to the patient's language and provides it in the appropriate language. The multilingual support unit can also provide information that takes into consideration the cultural background and provides information in a form that is easy for the patient to understand. Furthermore, the multilingual support unit can support medical staff to communicate smoothly with patients who speak different languages. This makes it possible to remove communication barriers due to differences in language and culture and provide appropriate medical services.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The reception unit receives patient information. The patient information includes medical history, symptoms, personal information, etc. The reception unit provides an interface for inputting information such as the patient's symptoms, urgency, and required specialty. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining and statistical analysis. The analysis unit analyzes the patient information and identifies the required specialty and available hospital rooms. Step 3: The search unit uses the generation AI to search for hospitals based on the information analyzed by the analysis unit. The search is performed based on the search algorithm and filtering conditions. Step 4: The suggestion unit proposes the optimal delivery destination based on the results searched by the search unit. The proposal is made based on the proposal algorithm and evaluation criteria. The suggestion unit proposes the optimal delivery destination and provides it to the ambulance crew.

[0071] (Example 2) The emergency transport support system according to the embodiment of the present invention is a system that instantly searches for an available hospital room or a specialized field in emergency transport at a hospital and determines the optimal destination. This emergency transport support system can instantly search by using a generating AI. For example, an ambulance crew member inputs information about the patient. Next, the generating AI analyzes the information and searches for an available hospital room or a hospital with a specialized field. Based on the search results, the optimal destination is proposed. This system significantly improves the efficiency of emergency transport and enables rapid treatment of the patient. For example, an ambulance crew member inputs information about the patient's symptoms, urgency, and required specialized field. This information is input to the generating AI. Next, the generating AI analyzes the input information. Based on the patient's information, the generating AI searches for an available hospital room or a hospital with a specialized field. For example, in the case of a patient with a heart attack, a hospital specializing in heart disease is searched for. Based on the search results, the generating AI proposes the optimal destination. For example, a hospital with an available hospital room and a doctor specializing in heart disease is proposed. This proposal is instantly provided to the ambulance crew member. This system significantly improves the efficiency of emergency transport. Paramedics can instantly determine the optimal transport destination without having to check by phone. This also enables patients to receive treatment more quickly, which is expected to improve the survival rate. This allows the emergency transport support system to efficiently accept, analyze, search, and suggest patient information.

[0072] The emergency transport support system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit accepts information on the patient. The patient information includes, for example, medical history, symptoms, personal information, etc., but is not limited to such examples. The reception unit inputs, for example, information on the patient's symptoms, urgency, and required specialty. The analysis unit uses a generation AI to analyze the information accepted by the reception unit. The analysis is performed, for example, by a method such as data mining or statistical analysis, but is not limited to such examples. The search unit uses a generation AI to search for a hospital based on the information analyzed by the analysis unit. The search is performed, for example, based on a search algorithm or filtering conditions, but is not limited to such examples. The proposal unit proposes an optimal delivery destination based on the results searched by the search unit. The proposal is performed, for example, based on a proposal algorithm or evaluation criteria, but is not limited to such examples. As a result, the emergency transport support system according to the embodiment can efficiently accept, analyze, search, and propose patient information. For example, the reception unit provides an interface for inputting patient information. The analysis unit uses generative AI to analyze patient information and identify the required specialty and available hospital rooms. The search unit uses generative AI to search for hospitals based on the analysis results and identify the optimal destination for the patient. The suggestion unit suggests the optimal destination for the patient based on the search results and provides it to emergency personnel. This significantly improves the efficiency of emergency transport and enables faster treatment for patients.

[0073] The reception unit receives information on the patient. The patient information includes, but is not limited to, for example, medical history, symptoms, and personal information. The reception unit inputs information such as, for example, the patient's symptoms, urgency, and required specialty. Specifically, the reception unit provides an interface designed to enable ambulance personnel and medical staff to input information quickly and accurately. This interface is available on tablets, smartphones, dedicated input devices, and the like, and also has functions such as voice input and barcode scanning. For example, an ambulance personnel can check the patient's symptoms on-site and record the symptoms using voice input. It is also possible to automatically obtain past medical history, allergy information, and the like by scanning the patient's medical ID card. Furthermore, the reception unit can transmit the input information to a central database in real time and share it with other departments. This allows the reception unit to quickly and accurately collect patient information and improve the efficiency of the entire system.

[0074] The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed, for example, by a method such as data mining or statistical analysis, but is not limited to such examples. Specifically, the generation AI identifies the optimal treatment method and the necessary specialty based on information such as the patient's symptoms, medical history, and urgency. For example, the generation AI uses natural language processing technology to analyze the text data of the input symptoms and identify the severity of the symptoms and related diseases. It can also analyze the treatment results for similar cases based on past data and propose the optimal treatment method. Furthermore, the generation AI considers the availability of hospitals and doctor schedule information, which are updated in real time, to identify the optimal destination. As a result, the analysis unit can quickly and accurately analyze the patient's information and identify the optimal treatment method and destination.

[0075] The search unit uses the generation AI to search for a hospital based on the information analyzed by the analysis unit. The search is performed, for example, based on a search algorithm and filtering conditions, but is not limited to such examples. Specifically, the generation AI searches for the most suitable hospital based on information such as the patient's symptoms and urgency, the required specialty, and the availability of hospitals. For example, the generation AI uses a geographic information system (GIS) to identify the hospital closest to the patient's current location and calculates the optimal route taking into account traffic conditions and travel time. In addition, the generation AI can identify the hospital that is most suitable for the patient's symptoms based on the hospital's specialty, facilities, and doctor's schedule information. Furthermore, the generation AI identifies the optimal destination taking into account the availability of hospitals, which is updated in real time. As a result, the search unit can quickly and accurately search for the most suitable hospital based on the patient's information and support the selection of the destination.

[0076] The suggestion unit suggests an optimal destination based on the results searched by the search unit. The suggestion is made based on, for example, a suggestion algorithm or an evaluation criterion, but is not limited to such examples. Specifically, the suggestion unit uses the generation AI to identify the optimal destination based on the search results and provide it to the ambulance crew. For example, the generation AI comprehensively evaluates information such as the patient's symptoms and urgency, and the availability of hospitals, and suggests the optimal destination. In addition, the suggestion unit provides an interface for displaying the suggested content in an easy-to-understand manner to the ambulance crew. For example, information such as the name, address, contact information, and travel route of the optimal destination hospital can be displayed on the screen of a tablet or smartphone, and attention can be drawn by voice guidance, vibration notification, etc. Furthermore, the suggestion unit can update the suggested content in real time and make suggestions based on the latest information. As a result, the suggestion unit can provide the ambulance crew with prompt and accurate suggestions of destinations, and support prompt treatment of the patient.

[0077] The emergency transport support system includes an update unit that manages the update frequency of the database. The update unit manages the update frequency of the database. The update frequency includes, for example, periodic update and real-time update, but is not limited to such examples. For example, the update unit sets an update schedule for the database and updates the data periodically. The update unit also has a function of updating data in real time. For example, the update unit receives the latest information from a hospital in real time and updates the database. In this way, the update frequency of the database can be managed to provide the latest information. A part or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit inputs information from the hospital to the generation AI and causes the generation AI to update the database.

[0078] The emergency transport support system includes a providing unit that manages the information provided by the hospital. The providing unit manages the information provided by the hospital. The information provided includes, for example, a data format and a timing of providing, but is not limited to such examples. For example, the providing unit receives information from the hospital and registers it in a database. The providing unit also has a function of managing the timing of the information provided by the hospital. For example, the providing unit sets a schedule for the information provided by the hospital and receives information periodically. This allows accurate information to be provided by managing the information provided by the hospital. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs information from the hospital to a generating AI and allows the generating AI to manage the information provided.

[0079] The emergency transport support system includes a protection unit that protects the privacy of the patient. The protection unit protects the privacy of the patient. Privacy protection includes, for example, data encryption and access control, but is not limited to such examples. For example, the protection unit encrypts the patient's information to prevent a third party from accessing it. In addition, the protection unit performs access control so that only specific users can access the patient's information. For example, the protection unit encrypts the patient's information and stores it in a database. In addition, the protection unit creates an access control list so that only specific users can access the information. This protects the patient's privacy and allows the system to be used with peace of mind. Some or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit inputs the patient's information into the generation AI and causes the generation AI to execute the privacy protection processing.

[0080] The emergency transport support system includes a priority section that performs prioritization. The priority section performs prioritization. Prioritization includes, for example, urgency and importance, but is not limited to such examples. The priority section determines the priority of information based on, for example, the symptoms and urgency of the patient. The priority section also has a function of determining the priority of information based on importance. For example, when the urgency of the patient is high, the priority section displays only the minimum necessary input items. Also, when the health condition of the patient is stable, the priority section displays detailed input items. This enables efficient processing by performing prioritization. Part or all of the above-mentioned processing in the priority section may be performed, for example, using AI, or may be performed without using AI. For example, the priority section can input patient information to the generation AI and cause the generation AI to execute the prioritization processing.

[0081] The search unit can search for a hospital based on the patient's symptoms or urgency. The search unit searches for a hospital based on the patient's symptoms or urgency, for example. The symptoms include, but are not limited to, the degree of pain and fever. The urgency includes, but is not limited to, the degree of life danger and the rate at which the symptoms progress. For example, in the case of a patient with a heart attack, the search unit searches for a hospital that specializes in heart disease. In addition, when the urgency is high, the search unit can also search for the nearest hospital with priority. This makes it possible to quickly find an appropriate hospital by searching for a hospital based on the patient's symptoms and urgency. Part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input patient information to the generation AI and cause the generation AI to search for a hospital.

[0082] The suggestion unit can suggest a destination considering the distance to the hospital or traffic conditions. The suggestion unit suggests a destination considering, for example, the distance to the hospital and traffic conditions. The distance includes, for example, a straight-line distance and a travel time, but is not limited to such examples. The traffic conditions include, for example, traffic congestion and the operation status of public transportation, but is not limited to such examples. The suggestion unit, for example, preferentially suggests a hospital that is closest to the patient's current location. The suggestion unit can also suggest a hospital that can be reached most quickly, taking into account traffic conditions. This makes it possible to quickly transport the patient by proposing an optimal destination considering the distance to the hospital and traffic conditions. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the patient to the generation AI and cause the generation AI to execute the proposal of the destination.

[0083] The reception unit can estimate the patient's emotion and adjust the information input method based on the estimated patient's emotion. The reception unit, for example, estimates the patient's emotion and adjusts the information input method based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the reception unit provides a simple and intuitive interface and minimizes the input procedure when the patient feels anxious. In addition, the reception unit can provide detailed input options and suggest a customizable input method when the patient is relaxed. Furthermore, the reception unit can prioritize voice input and enable the patient to input information quickly when the patient is in a hurry. This allows more appropriate information input by adjusting the information input method according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0084] The reception unit can analyze the patient's past medical history and provide an optimal information input format. The reception unit, for example, analyzes the patient's past medical history and provides an optimal information input format. The medical history includes, for example, past medical records and prescription history, but is not limited to such examples. For example, the reception unit automatically displays related input items based on the patient's past medical history. The reception unit can also automatically select a required specialty from the patient's past treatment history. Furthermore, the reception unit can also refer to the patient's past medical data and customize the input items. In this way, the patient's past medical history can be analyzed to provide an optimal information input format. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's medical history data to the generation AI and cause the generation AI to provide an optimal information input format.

[0085] The reception unit can dynamically change the input items based on the patient's current health condition and urgency when inputting information. The reception unit dynamically changes the input items based on the patient's current health condition and urgency when inputting information, for example. The health condition includes, but is not limited to, vital signs and self-reporting. The urgency includes, but is not limited to, the degree of life danger and the rate of progression of symptoms. For example, when the urgency of the patient is high, the reception unit displays only the minimum necessary input items. In addition, when the patient's health condition is stable, the reception unit can also display detailed input items. Furthermore, the reception unit can dynamically change the input items according to the patient's symptoms. This makes it possible to input appropriate information by dynamically changing the input items according to the patient's health condition and urgency. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's health condition data to the generation AI and cause the generation AI to dynamically change the input items.

[0086] The reception unit can estimate the patient's emotion and determine the priority of information to be input based on the estimated patient's emotion. The reception unit, for example, estimates the patient's emotion and determines the priority of information to be input based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, when the patient feels anxious, the reception unit preferentially inputs the most important information. Furthermore, when the patient is relaxed, the reception unit can also preferentially input detailed information. Furthermore, when the patient is in a hurry, the reception unit can also preferentially input information with a high degree of urgency. In this way, by determining the priority of information to be input according to the patient's emotion, important information can be preferentially input. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the patient when inputting information. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the patient when inputting information. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the reception unit preferentially inputs nearby hospital information based on the patient's current location. In addition, the reception unit can also refer to the geographical location information of the patient and preferentially input information of related medical institutions. Furthermore, the reception unit can also suggest an optimal delivery destination based on the patient's location information. In this way, by considering the geographical location information of the patient, highly relevant information can be preferentially input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the patient's location information data to the generation AI and cause the generation AI to execute the preferential input of highly relevant information.

[0088] The reception unit can analyze the social media activity of the patient at the time of inputting information and input related information. The reception unit, for example, analyzes the social media activity of the patient at the time of inputting information and inputs related information. The social media activity includes, for example, the content of the post and the number of likes, but is not limited to such examples. For example, the reception unit analyzes the social media post of the patient and inputs information related to the health condition. The reception unit can also estimate the past medical history from the social media activity of the patient and customize the input items. Furthermore, the reception unit can automatically select the required specialty based on the social media data of the patient. In this way, the related information can be input by analyzing the social media activity of the patient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the patient to the generation AI and cause the generation AI to input the related information.

[0089] The analysis unit can estimate the patient's emotion and adjust the analysis method based on the estimated patient's emotion. The analysis unit, for example, estimates the patient's emotion and adjusts the analysis method based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the analysis unit applies a quick and concise analysis method when the patient feels anxious. The analysis unit can also apply a detailed analysis method when the patient is relaxed. Furthermore, the analysis unit can also prioritize analysis of the most important information when the patient is in a hurry. This enables more appropriate analysis by adjusting the analysis method according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during the analysis. The analysis unit, for example, optimizes the analysis algorithm by referring to the patient's past medical data during the analysis. The medical data includes, for example, medical records and test results, but is not limited to such examples. For example, the analysis unit selects an optimal analysis algorithm based on the patient's past medical history. The analysis unit can also adjust the analysis algorithm by referring to the patient's past treatment history. Furthermore, the analysis unit can analyze the patient's past medical data and optimize the analysis algorithm. As a result, the analysis algorithm can be optimized by referring to the patient's past medical data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the patient's medical data to the generation AI and cause the generation AI to execute optimization of the analysis algorithm.

[0091] The analysis unit can apply different analysis methods depending on the symptoms and urgency of the patient during analysis. The analysis unit applies different analysis methods depending on the symptoms and urgency of the patient during analysis, for example. Symptoms include, but are not limited to, the degree of pain and fever. Urgency includes, but are not limited to, the degree of life danger and the rate of progression of symptoms. For example, the analysis unit applies a rapid analysis method when the urgency of the patient is high. The analysis unit can also select an optimal analysis method depending on the symptoms of the patient. Furthermore, the analysis unit can dynamically change the analysis method based on the urgency of the patient. This makes it possible to perform appropriate analysis by applying the analysis method depending on the symptoms and urgency of the patient. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the symptom data of the patient to the generation AI and cause the generation AI to apply the analysis method.

[0092] The analysis unit can estimate the patient's emotion and determine the priority of analysis based on the estimated patient's emotion. The analysis unit can estimate the patient's emotion and determine the priority of analysis based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, when the patient feels anxious, the analysis unit prioritizes analysis of the most important information. In addition, when the patient is relaxed, the analysis unit can also prioritize analysis of detailed information. Furthermore, when the patient is in a hurry, the analysis unit can also prioritize analysis of information with a high degree of urgency. In this way, important information can be analyzed preferentially by determining the priority of analysis according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The analysis unit can adjust the analysis result in consideration of the geographical location information of the patient during the analysis. The analysis unit, for example, adjusts the analysis result in consideration of the geographical location information of the patient during the analysis. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the analysis unit provides an optimal analysis result based on the current location of the patient. The analysis unit can also refer to the geographical location information of the patient and reflect information of related medical institutions in the analysis result. Furthermore, the analysis unit can also suggest an optimal delivery destination based on the location information of the patient. In this way, by considering the geographical location information of the patient, an appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the location information data of the patient to the generation AI and cause the generation AI to adjust the analysis result.

[0094] The analysis unit can improve the accuracy of the analysis by referring to the patient's related literature during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the patient's related literature during the analysis, for example. The related literature includes, but is not limited to, academic papers and medical guidelines. For example, the analysis unit refers to the latest medical literature related to the patient's symptoms and reflects it in the analysis result. The analysis unit can also optimize the analysis algorithm by referring to literature related to the patient's past treatment history. Furthermore, the analysis unit can automatically search for related literature based on the patient's symptoms and reflect it in the analysis result. Thereby, the accuracy of the analysis can be improved by referring to the patient's related literature. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the patient's symptom data to the generation AI and cause the generation AI to execute the reference of related literature.

[0095] The search unit can estimate the patient's emotion and adjust the search criteria based on the estimated patient's emotion. The search unit can, for example, estimate the patient's emotion and adjust the search criteria based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the search unit applies quick and concise search criteria when the patient is anxious. The search unit can also apply detailed search criteria when the patient is relaxed. Furthermore, the search unit can also preferentially search for the most important information when the patient is in a hurry. This enables appropriate search by adjusting the search criteria according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The search unit can improve the accuracy of the search by considering the mutual relationships of the hospitals at the time of the search. The search unit, for example, improves the accuracy of the search by considering the mutual relationships of the hospitals at the time of the search. The mutual relationships include, for example, partnership relationships and introduction records, but are not limited to such examples. For example, the search unit searches for an optimal delivery destination based on cooperation information between hospitals. In addition, the search unit can optimize the search results by considering the specialty fields and facility information of the hospitals. Furthermore, the search unit can analyze the mutual relationships of the hospitals and propose the most suitable hospital. As a result, the accuracy of the search can be improved by considering the mutual relationships of the hospitals. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the mutual relationship data of the hospitals to the generation AI and cause the generation AI to execute the improvement of the search accuracy.

[0097] The search unit can perform a search while considering the specialty and equipment information of the hospital. The search unit performs a search while considering the specialty and equipment information of the hospital, for example, while performing a search. The specialty includes, for example, internal medicine, surgery, obstetrics and gynecology, but is not limited to such examples. The equipment information includes, for example, the number of MRI devices and operating rooms, but is not limited to such examples. For example, the search unit searches for a hospital with a specialty according to the patient's symptoms. The search unit can also search for an optimal delivery destination based on the equipment information of the hospital. Furthermore, the search unit can combine the specialty and equipment information of the hospital to propose an optimal hospital. In this way, an appropriate hospital can be searched for by considering the specialty and equipment information of the hospital. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the specialty data of the hospital to the generation AI and cause the generation AI to execute the search.

[0098] The search unit can estimate the patient's emotion and adjust the display order of the search results based on the estimated patient's emotion. The search unit can estimate the patient's emotion, for example, and adjust the display order of the search results based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, when the patient feels anxious, the search unit can display the most important information with priority. In addition, when the patient is relaxed, the search unit can also display detailed information with priority. Furthermore, when the patient is in a hurry, the search unit can display information with high urgency with priority. In this way, important information can be displayed with priority by adjusting the display order of the search results according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0099] The search unit can perform a search while taking into consideration the geographical distribution of hospitals. For example, the search unit performs a search while taking into consideration the geographical distribution of hospitals. The geographical distribution includes, for example, urban areas and rural areas, but is not limited to such examples. For example, the search unit preferentially searches nearby hospitals based on the patient's current location. The search unit can also refer to the geographical distribution of hospitals and suggest the optimal delivery destination. Furthermore, the search unit can search for the optimal hospital based on the patient's location information. In this way, an appropriate hospital can be searched for by taking into consideration the geographical distribution of hospitals. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the geographical distribution data of hospitals to the generation AI and cause the generation AI to execute the search.

[0100] The search unit can improve the accuracy of the search by referring to the related literature of the hospital at the time of the search. The search unit can improve the accuracy of the search by referring to the related literature of the hospital at the time of the search, for example. The related literature includes, but is not limited to, academic papers and medical guidelines. For example, the search unit can refer to the latest medical literature related to the specialty of the hospital and reflect it in the search result. The search unit can also optimize the search algorithm by referring to the literature related to the equipment information of the hospital. Furthermore, the search unit can automatically search for related literature based on the specialty of the hospital and reflect it in the search result. Thereby, the accuracy of the search can be improved by referring to the related literature of the hospital. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the related literature data of the hospital to the generation AI and cause the generation AI to execute the improvement of the search accuracy.

[0101] The suggestion unit can estimate the patient's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the patient. The suggestion unit, for example, estimates the patient's emotion and adjusts the way of expressing the suggestion based on the estimated emotion of the patient. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the suggestion unit makes a simple and intuitive suggestion when the patient feels anxious. In addition, the suggestion unit can also make a detailed suggestion when the patient is relaxed. Furthermore, the suggestion unit can also make a quick and concise suggestion when the patient is in a hurry. This enables appropriate suggestions by adjusting the way of expressing the suggestion according to the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute the estimation of emotion.

[0102] The suggestion unit can adjust the level of detail of the proposal based on the importance of the hospital at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the hospital at the time of suggestion. The importance includes, for example, the size and specialty of the hospital, but is not limited to such examples. For example, the suggestion unit preferentially suggests hospitals with high importance. In addition, the suggestion unit can adjust the level of detail of the proposal according to the importance of the hospital. Furthermore, the suggestion unit can make a detailed proposal for a hospital with high importance. As a result, an appropriate proposal can be made by adjusting the level of detail of the proposal based on the importance of the hospital. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the hospital to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0103] The suggestion unit can apply different proposal algorithms depending on the category of the hospital at the time of suggestion. For example, the suggestion unit applies different proposal algorithms depending on the category of the hospital at the time of suggestion. The categories include, for example, general hospitals and specialized hospitals, but are not limited to such examples. For example, the suggestion unit applies a quick proposal algorithm to a hospital with a high degree of urgency. In addition, the suggestion unit can also apply a detailed proposal algorithm to a hospital specialized in a specialized field. Furthermore, the suggestion unit can also apply a standard proposal algorithm to a general hospital. As a result, appropriate proposals can be made by applying different proposal algorithms depending on the category of the hospital. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input hospital category data to the generation AI and cause the generation AI to execute the application of the proposal algorithm.

[0104] The suggestion unit can estimate the patient's emotion and adjust the length of the suggestion based on the estimated patient's emotion. The suggestion unit, for example, estimates the patient's emotion and adjusts the length of the suggestion based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the suggestion unit makes a short and to the point suggestion when the patient is anxious. In addition, the suggestion unit can also make a longer suggestion including detailed explanations when the patient is relaxed. Furthermore, the suggestion unit can also make a quick and concise suggestion when the patient is in a hurry. This allows appropriate suggestions to be made by adjusting the length of the suggestion according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The suggestion unit can determine the priority of the proposal in consideration of the distance of the hospital and the traffic conditions at the time of proposal. The suggestion unit, for example, determines the priority of the proposal in consideration of the distance of the hospital and the traffic conditions at the time of proposal. The distance includes, for example, a straight-line distance and a travel time, but is not limited to such examples. The traffic conditions include, for example, traffic congestion and the operation conditions of public transportation, but is not limited to such examples. For example, the suggestion unit preferentially suggests a hospital that is closest to the patient's current location. In addition, the suggestion unit can also consider the traffic conditions and propose a hospital that can be reached most quickly. Furthermore, the suggestion unit can comprehensively judge the distance and the traffic conditions and propose an optimal hospital. As a result, appropriate proposals can be made by considering the distance of the hospital and the traffic conditions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the distance of the hospital and the traffic conditions data to the generation AI and cause the generation AI to execute the priority decision of the proposal.

[0106] The suggestion unit can adjust the order of the suggestions based on the relevance of the hospitals at the time of suggestion. The suggestion unit, for example, adjusts the order of the suggestions based on the relevance of the hospitals at the time of suggestion. The relevance includes, for example, a match of medical departments and a past referral record, but is not limited to such examples. For example, the suggestion unit preferentially suggests a hospital that is most related to the patient's symptoms. The suggestion unit can also suggest a highly related hospital based on the hospital's specialty. Furthermore, the suggestion unit can also preferentially suggest a highly related hospital according to the urgency of the patient. This makes it possible to make an appropriate suggestion by adjusting the order of the suggestions based on the relevance of the hospitals. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input hospital relevance data to the generation AI and cause the generation AI to execute the adjustment of the order of the suggestions.

[0107] The update unit can estimate the patient's emotion and adjust the update frequency of the database based on the estimated emotion of the patient. The update unit, for example, estimates the patient's emotion and adjusts the update frequency of the database based on the estimated emotion of the patient. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the update unit increases the update frequency of the database when the patient feels anxious. In addition, the update unit can also maintain the normal update frequency when the patient is relaxed. Furthermore, the update unit can also preferentially increase the update frequency of important data when the patient is in a hurry. In this way, appropriate information can be provided by adjusting the update frequency of the database according to the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the update unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the update unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0108] The update unit can optimize the update algorithm by referring to past update data at the time of updating. The update unit optimizes the update algorithm by referring to past update data, for example, at the time of updating. The update data includes, for example, past medical records and patient feedback, but is not limited to such examples. For example, the update unit analyzes past update data and selects an optimal update algorithm. The update unit can also adjust the update algorithm based on the update history. Furthermore, the update unit can also optimize the update algorithm by referring to past update data. As a result, the update algorithm can be optimized by referring to the past update data. A part or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit inputs past update data to the generation AI and causes the generation AI to execute optimization of the update algorithm.

[0109] The update unit can estimate the patient's emotion and determine the priority of updates based on the estimated emotion of the patient. The update unit, for example, estimates the patient's emotion and determines the priority of updates based on the estimated emotion of the patient. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the update unit prioritizes the update of important data when the patient feels anxious. The update unit can also maintain the normal update order when the patient is relaxed. Furthermore, the update unit can also prioritize the update of data with high urgency when the patient is in a hurry. In this way, important data can be updated preferentially by determining the priority of updates according to the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the update unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the update unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0110] The update unit can weight the update data in consideration of the geographical location information of the hospital at the time of update. The update unit, for example, weights the update data in consideration of the geographical location information of the hospital at the time of update. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the update unit preferentially updates data of a hospital close to the patient's current location. The update unit can also weight the update data based on the geographical location information of the hospital. Furthermore, the update unit can also select optimal update data by referring to the patient's location information. In this way, appropriate data can be preferentially updated by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input geographical location information data of the hospital to the generation AI and cause the generation AI to execute weighting of the update data.

[0111] The providing unit can estimate the patient's emotion and adjust the method of providing information based on the estimated emotion of the patient. The providing unit, for example, estimates the patient's emotion and adjusts the method of providing information based on the estimated emotion of the patient. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, when the patient feels anxious, the providing unit provides simple and intuitive information. In addition, when the patient is relaxed, the providing unit can also provide detailed information. Furthermore, when the patient is in a hurry, the providing unit can also provide quick and concise information. This makes it possible to provide appropriate information by adjusting the method of providing information according to the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0112] The providing unit can select the optimal information providing method by referring to the hospital's past providing history when providing information. For example, the providing unit selects the optimal information providing method by referring to the hospital's past providing history when providing information. The providing history includes, for example, past providing data and feedback of the providing result, but is not limited to such examples. For example, the providing unit selects the optimal information providing method based on the hospital's past providing history. In addition, the providing unit can also adjust the information providing method by referring to the providing history. Furthermore, the providing unit can analyze the past providing history and select the optimal information providing method. As a result, the optimal information providing method can be selected by referring to the hospital's past providing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past providing history data to the generating AI and cause the generating AI to select the optimal information providing method.

[0113] The providing unit can estimate the patient's emotion and determine the priority of information provision based on the estimated patient's emotion. The providing unit, for example, estimates the patient's emotion and determines the priority of information provision based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, when the patient feels anxious, the providing unit provides important information preferentially. In addition, when the patient is relaxed, the providing unit can also provide detailed information preferentially. Furthermore, when the patient is in a hurry, the providing unit can also provide information with a high degree of urgency preferentially. In this way, by determining the priority of information provision according to the patient's emotion, important information can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0114] The providing unit can select the optimal providing method in consideration of the geographical location information of the hospital when providing information. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the hospital when providing information. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the providing unit preferentially provides information of a hospital close to the patient's current location. In addition, the providing unit can also select the optimal information providing method based on the geographical location information of the hospital. Furthermore, the providing unit can also select the optimal information providing method by referring to the patient's location information. As a result, appropriate information can be provided by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the hospital to the generating AI and cause the generating AI to select the optimal providing method.

[0115] The protection unit can estimate the patient's emotion and adjust the privacy protection method based on the estimated patient's emotion. The protection unit, for example, estimates the patient's emotion and adjusts the privacy protection method based on the estimated patient's emotion. The emotion estimation includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the protection unit applies strict privacy protection when the patient feels anxious. The protection unit can also apply normal privacy protection when the patient is relaxed. Furthermore, the protection unit can also prioritize privacy protection of important data when the patient is in a hurry. This enables appropriate privacy protection by adjusting the privacy protection method according to the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0116] The protection unit can optimize the protection algorithm by referring to past protection data during privacy protection. The protection unit optimizes the protection algorithm by referring to past protection data during privacy protection, for example. The protection data includes, for example, past protection records and feedback of protection results, but is not limited to such examples. For example, the protection unit analyzes past protection data and selects an optimal protection algorithm. The protection unit can also adjust the protection algorithm based on the protection history. Furthermore, the protection unit can also optimize the protection algorithm by referring to past protection data. As a result, the protection algorithm can be optimized by referring to the past protection data. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input past protection data to the generation AI and cause the generation AI to execute optimization of the protection algorithm.

[0117] The protection unit can estimate the patient's emotion and determine the priority of privacy protection based on the estimated patient's emotion. The protection unit, for example, estimates the patient's emotion and determines the priority of privacy protection based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the protection unit prioritizes privacy protection of important data when the patient feels anxious. The protection unit can also apply normal privacy protection when the patient is relaxed. Furthermore, the protection unit can also prioritize privacy protection of data with high urgency when the patient is in a hurry. In this way, important data can be protected preferentially by determining the priority of privacy protection according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input facial expression data of a patient to the generation AI and have the generation AI perform emotion estimation.

[0118] The protection unit can weight the protection data in consideration of the geographical location information of the hospital during privacy protection. The protection unit, for example, weights the protection data in consideration of the geographical location information of the hospital during privacy protection. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the protection unit preferentially protects data of a hospital close to the patient's current location. The protection unit can also weight the protection data based on the geographical location information of the hospital. Furthermore, the protection unit can also refer to the patient's location information and select optimal protection data. As a result, appropriate data can be preferentially protected by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the geographical location information data of the hospital to the generation AI and cause the generation AI to execute weighting of the protection data.

[0119] The priority unit can estimate the patient's emotion and adjust the prioritization method based on the estimated patient's emotion. The priority unit can, for example, estimate the patient's emotion and adjust the prioritization method based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the priority unit prioritizes important information when the patient feels anxious. The priority unit can also apply normal prioritization when the patient is relaxed. Furthermore, the priority unit can also prioritize information with high urgency when the patient is in a hurry. This allows appropriate prioritization by adjusting the prioritization method according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the priority unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the priority unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0120] The priority unit can optimize the priority algorithm by referring to past priority data when prioritizing. The priority unit optimizes the priority algorithm by referring to past priority data when prioritizing, for example. The priority data includes, for example, past priority records and feedback of priority results, but is not limited to such examples. For example, the priority unit analyzes past priority data and selects an optimal priority algorithm. The priority unit can also adjust the priority algorithm based on the priority history. Furthermore, the priority unit can also optimize the priority algorithm by referring to past priority data. In this way, the priority algorithm can be optimized by referring to the past priority data. A part or all of the above-mentioned processing in the priority unit may be performed, for example, using AI, or may be performed without using AI. For example, the priority unit can input past priority data to the generation AI and cause the generation AI to execute optimization of the priority algorithm.

[0121] The priority unit can estimate the patient's emotion and determine the priority of the prioritization based on the estimated patient's emotion. The priority unit can, for example, estimate the patient's emotion and determine the priority of the prioritization based on the estimated patient's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the priority unit preferentially processes important information when the patient feels anxious. The priority unit can also apply normal prioritization when the patient is relaxed. Furthermore, the priority unit can also preferentially process information with high urgency when the patient is in a hurry. In this way, important information can be preferentially processed by determining the priority of the prioritization according to the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the priority unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the priority unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0122] The priority unit can weight the priority data in consideration of the geographical location information of the hospital when prioritizing. For example, the priority unit weights the priority data in consideration of the geographical location information of the hospital when prioritizing. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. For example, the priority unit preferentially processes data of a hospital close to the patient's current location. The priority unit can also weight the priority data based on the geographical location information of the hospital. Furthermore, the priority unit can also refer to the patient's location information and select optimal priority data. In this way, appropriate data can be preferentially processed by considering the geographical location information of the hospital. A part or all of the above-mentioned processing in the priority unit may be performed, for example, using AI, or may be performed without using AI. For example, the priority unit can input geographical location information data of the hospital to the generation AI and cause the generation AI to execute weighting of the priority data.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The emergency transport support system may further include a monitoring unit that monitors the patient's vital signs in real time. The monitoring unit continuously monitors vital signs, such as heart rate, blood pressure, and oxygen saturation, and immediately notifies the patient if an abnormality is detected. The monitoring unit may also transmit vital sign data of the patient to an analysis unit, which may then reevaluate the optimal destination based on the data. The monitoring unit may also record changes in the patient's vital signs and use the data for subsequent medical treatment. This allows the patient's condition to be understood in real time, enabling prompt and appropriate response.

[0125] The emergency transport support system can further include a notification unit that automatically notifies the patient's family and emergency contacts. For example, when the patient's transport destination has been decided, the notification unit sends a notification to the patient's family and emergency contacts by SMS or email. The notification unit can also share the patient's condition and the progress of the transport in real time. Furthermore, the notification unit can receive replies from the family and emergency contacts and provide information to emergency personnel as necessary. This makes it possible to provide the patient's family and emergency contacts with quick and accurate information, providing a sense of security.

[0126] The emergency transport support system may further include a cloud management unit that manages the patient's past medical data on the cloud. The cloud management unit, for example, stores the patient's medical records and test results in the cloud and allows them to be accessed as needed. The cloud management unit also facilitates data sharing between medical institutions and enables rapid provision of information. Furthermore, the cloud management unit can also regularly back up data to ensure data safety. This allows the patient's medical data to be managed efficiently and necessary information to be provided quickly.

[0127] The emergency transport support system can further include a multilingual support unit that provides information according to the patient's language and culture. For example, the multilingual support unit translates information according to the patient's language and provides it in the appropriate language. The multilingual support unit can also provide information that takes into consideration the cultural background and provides information in a form that is easy for the patient to understand. Furthermore, the multilingual support unit can support medical staff to communicate smoothly with patients who speak different languages. This makes it possible to remove communication barriers due to differences in language and culture and provide appropriate medical services.

[0128] The emergency transport support system may further include an entertainment unit that estimates the patient's emotions and provides music and audio guidance during transport based on the estimated emotions. For example, if the patient feels anxious, the entertainment unit may play relaxing music to provide a sense of security. If the patient is relaxed, the entertainment unit may also provide an interesting audio guidance. Furthermore, the entertainment unit may dynamically change the content of the music and audio guidance depending on the patient's emotions. This makes it possible to provide entertainment that takes into consideration the patient's emotions and reduce stress during transport.

[0129] The emergency transport support system may further include an environment adjustment unit that estimates the patient's emotions and adjusts the lighting and temperature during transport based on the estimated emotions. For example, when the patient feels anxious, the environment adjustment unit provides soft lighting and a comfortable temperature to create an environment in which the patient can relax. Furthermore, when the patient is relaxed, the environment adjustment unit can also maintain appropriate brightness and temperature. Furthermore, the environment adjustment unit can dynamically change the lighting and temperature settings according to the patient's emotions. This provides an environment that takes into consideration the patient's emotions, improving comfort during transport.

[0130] The emergency transport support system may further include a communication unit that estimates the patient's emotions and adjusts the communication method during transport based on the estimated emotions. For example, if the patient feels anxious, the communication unit speaks to the patient in a gentle tone to reassure the patient. If the patient is relaxed, the communication unit may provide a detailed explanation to deepen the patient's understanding. Furthermore, the communication unit may dynamically change the content and method of communication depending on the patient's emotions. This allows communication that takes into consideration the patient's emotions to be provided, increasing the patient's sense of security during transport.

[0131] The emergency transport support system may further include a stress monitoring unit that estimates the patient's emotions and monitors the stress level during transport based on the estimated emotions. The stress monitoring unit, for example, monitors the patient's heart rate and respiratory rate to evaluate the stress level. The stress monitoring unit may also analyze the patient's emotion data and identify the cause of stress. Furthermore, if the stress level is high, the stress monitoring unit may send instructions to other elements to provide a relaxing environment. This allows the patient's stress level to be grasped in real time and appropriate measures to be taken.

[0132] The emergency transport support system may further include a medical treatment adjustment unit that estimates the patient's emotions and adjusts medical treatment during transport based on the estimated emotions. For example, if the patient feels anxious, the medical treatment adjustment unit prioritizes treatment to reduce pain. Also, if the patient is relaxed, the medical treatment adjustment unit may proceed with treatment while providing detailed explanations. Furthermore, the medical treatment adjustment unit may dynamically change the content and order of treatment according to the patient's emotions. This allows for medical treatment that takes into consideration the patient's emotions, increasing the sense of security during transport.

[0133] The emergency transport support system may further include an information providing unit that estimates the patient's emotions and adjusts the provision of information during transport based on the estimated emotions. For example, when the patient feels anxious, the information providing unit provides simple and intuitive information to give the patient a sense of security. Also, when the patient is relaxed, the information providing unit can provide detailed information to deepen the patient's understanding. Furthermore, the information providing unit can dynamically change the content of information and the method of providing information according to the patient's emotions. This allows the provision of information that takes into consideration the patient's emotions, increasing the sense of security during transport.

[0134] The process flow of the second embodiment will be briefly described below.

[0135] Step 1: The reception unit receives patient information. The patient information includes medical history, symptoms, personal information, etc. The reception unit provides an interface for inputting information such as the patient's symptoms, urgency, and required specialty. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining and statistical analysis. The analysis unit analyzes the patient information and identifies the required specialty and available hospital rooms. Step 3: The search unit uses the generation AI to search for hospitals based on the information analyzed by the analysis unit. The search is performed based on the search algorithm and filtering conditions. Step 4: The suggestion unit proposes the optimal delivery destination based on the results searched by the search unit. The proposal is made based on the proposal algorithm and evaluation criteria. The suggestion unit proposes the optimal delivery destination and provides it to the ambulance crew.

[0136] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0137] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0138] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] For example, the reception unit can receive patient information using the reception device 38 of the smart device 14. The analysis unit analyzes the patient information using the generation AI by the specific processing unit 290 of the data processing device 12. The search unit searches for a hospital based on the information analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes an optimal delivery destination based on the search result by the specific processing unit 290 of the data processing device 12. The update unit manages the update frequency of the database 24 by the specific processing unit 290 of the data processing device 12. The provision unit manages the information provision by the hospital side by the specific processing unit 290 of the data processing device 12. The protection unit protects the privacy of the patient by the specific processing unit 290 of the data processing device 12. The priority unit prioritizes by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0143] 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.

[0144] 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 the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0146] 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 performed in a secure state.

[0147] Fig. 4 shows an example of 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.

[0148] 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.

[0149] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0151] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0153] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] For example, the reception unit can accept the patient's information using the microphone 238 of the smart glasses 214. The analysis unit analyzes the patient's information using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The search unit searches for a hospital based on the information analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal delivery destination based on the search result by the specific processing unit 290 of the data processing device 12. The update unit manages the update frequency of the database 24 by the specific processing unit 290 of the data processing device 12. The provision unit manages the information provision by the hospital side by the specific processing unit 290 of the data processing device 12. The protection unit protects the privacy of the patient by the specific processing unit 290 of the data processing device 12. The priority unit prioritizes by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0159] 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.

[0160] 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 the voice according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0162] 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 performed in a secure state.

[0163] 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.

[0164] 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.

[0165] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0167] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] 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 voice indicating a user input for 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.

[0169] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0171] For example, the reception unit can receive patient information using the microphone 238 of the headset terminal 314. The analysis unit analyzes the patient information using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The search unit searches for a hospital based on the information analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal delivery destination based on the search result by the specific processing unit 290 of the data processing device 12. The update unit manages the update frequency of the database 24 by the specific processing unit 290 of the data processing device 12. The provision unit manages the information provision by the hospital side by the specific processing unit 290 of the data processing device 12. The protection unit protects the privacy of the patient by the specific processing unit 290 of the data processing device 12. The priority unit prioritizes by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 7, a 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.

[0174] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0175] 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.

[0176] 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 the voice according to instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0178] 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 performed in a secure state.

[0179] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0180] 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.

[0181] 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.

[0182] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0183] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0184] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0185] 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 a voice indicating a user input for 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.

[0186] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0187] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0188] For example, the reception unit can receive patient information using the microphone 238 of the robot 414. The analysis unit analyzes the patient information using the generation AI by the specific processing unit 290 of the data processing device 12. The search unit searches for a hospital based on the information analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes an optimal delivery destination based on the search result by the specific processing unit 290 of the data processing device 12. The update unit manages the update frequency of the database 24 by the specific processing unit 290 of the data processing device 12. The provision unit manages the information provision by the hospital side by the specific processing unit 290 of the data processing device 12. The protection unit protects the privacy of the patient by the specific processing unit 290 of the data processing device 12. The priority unit prioritizes by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0189] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0190] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0192] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0193] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0194] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0196] 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.

[0197] 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 Universal Serial Bus (USB) 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.

[0198] In addition, 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 upon request from the data processing device 12.

[0199] 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.

[0200] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0201] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0202] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0203] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0204] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0205] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0206] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0207] (Appendix 1) A reception unit that receives patient information; an analysis unit that analyzes the information received by the reception unit; a search unit that searches for hospitals based on the information analyzed by the analysis unit; a suggestion unit that suggests a destination based on the search results by the search unit; Equipped A system characterized by: (Appendix 2) Equipped with an update unit that manages the update frequency of the database 2. The system of claim 1, (Appendix 3) Equipped with a provision section that manages information provided by the hospital 2. The system of claim 1, (Appendix 4) Equipped with a protection section to protect patient privacy 2. The system of claim 1, (Appendix 5) Equipped with a priority section that assigns priorities 2. The system of claim 1, (Appendix 6) The search unit is Search for hospitals based on patient symptoms or urgency 2. The system of claim 1, (Appendix 7) The suggestion unit, Propose a delivery location based on the distance to the hospital or traffic conditions 2. The system of claim 1, (Appendix 8) The reception unit is Infer patient emotions and adjust information entry methods based on the inferred patient emotions 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the patient's past medical history and provide the optimal format for entering information 2. The system of claim 1, (Appendix 10) The reception unit is Dynamically change input fields based on the patient's current health status and urgency as information is entered 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the patient's emotions and prioritize the information to be entered based on the estimated patient's emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When entering information, the geographic location of the patient is taken into consideration to prioritize the most relevant information 2. The system of claim 1, (Appendix 13) The reception unit is Analyze the patient's social media activity and enter relevant information when entering information 2. The system of claim 1, (Appendix 14) The analysis unit is Infer the patient's emotions and adjust the analysis method based on the inferred patient emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the analysis algorithm is optimized by referencing the patient's past medical data. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis methods are applied depending on the patient's symptoms and urgency. 2. The system of claim 1, (Appendix 17) The analysis unit is Estimate the patient's emotions and prioritize analysis based on the estimated patient's emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the geographic location of the patient is taken into account to adjust the analysis results. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, refer to patient-related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 20) The search unit is Infer patient sentiment and adjust search criteria based on the inferred patient sentiment 2. The system of claim 1, (Appendix 21) The search unit is Improve search accuracy by taking into account the relationships between hospitals during searches 2. The system of claim 1, (Appendix 22) The search unit is When searching, consider the hospital's specialty and facility information. 2. The system of claim 1, (Appendix 23) The search unit is Infer patient sentiment and adjust the order of search results based on the inferred patient sentiment 2. The system of claim 1, (Appendix 24) The search unit is When searching, the geographical distribution of hospitals is taken into account. 2. The system of claim 1, (Appendix 25) The search unit is When searching, refer to the hospital's related literature to improve search accuracy 2. The system of claim 1, (Appendix 26) The suggestion unit, Inferring the patient's emotions and adjusting the way the suggestions are presented based on the inferred patient emotions 2. The system of claim 1, (Appendix 27) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the hospital 2. The system of claim 1, (Appendix 28) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the hospital category. 2. The system of claim 1, (Appendix 29) The suggestion unit, Estimating patient sentiment and adjusting the length of suggestions based on the estimated patient sentiment 2. The system of claim 1, (Appendix 30) The suggestion unit, When making proposals, the priority of proposals will be decided taking into account the distance to hospitals and traffic conditions. 2. The system of claim 1, (Appendix 31) The suggestion unit, During suggestions, adjust the order of suggestions based on hospital relevance 2. The system of claim 1, (Appendix 32) The update unit is Estimating patient emotions and adjusting the frequency of database updates based on the estimated patient emotions 2. The system of claim 1, (Appendix 33) The update unit is When updating, the update algorithm is optimized by referencing past update data. 2. The system of claim 1, (Appendix 34) The update unit is Estimate patient sentiment and prioritize updates based on the estimated patient sentiment 2. The system of claim 1, (Appendix 35) The update unit is When updating, the updated data is weighted taking into account the geographic location of the hospital. 2. The system of claim 1, (Appendix 36) The providing unit is Infer the patient's feelings and adjust the way information is provided based on the inferred feelings 2. The system of claim 1, (Appendix 37) The providing unit is When providing information, the hospital will refer to its past provision history to select the most appropriate method of providing the information. 2. The system of claim 1, (Appendix 38) The providing unit is Estimate the patient's feelings and determine the priority of providing information based on the estimated patient's feelings 2. The system of claim 1, (Appendix 39) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the geographic location of the hospital. 2. The system of claim 1, (Appendix 40) The protective part is Inferring patient emotions and adjusting privacy protection methods based on the inferred patient emotions 2. The system of claim 1, (Appendix 41) The protective part is When protecting privacy, the protection algorithm is optimized by referring to past protection data. 2. The system of claim 1, (Appendix 42) The protective part is Estimating a patient's emotions and determining the priority of privacy protection based on the estimated patient's emotions 2. The system of claim 1, (Appendix 43) The protective part is When protecting privacy, the hospital's geographic location information is taken into account to weight the protection data. 2. The system of claim 1, (Appendix 44) The priority portion is Infer patient sentiment and adjust prioritization methods based on inferred patient sentiment 2. The system of claim 1, (Appendix 45) The priority portion is During prioritization, look at past priority data to optimize the priority algorithm 2. The system of claim 1, (Appendix 46) The priority portion is Estimating patient emotions and determining priorities for prioritization based on the estimated patient emotions 2. The system of claim 1, (Appendix 47) The priority portion is During prioritization, hospital geographic location is taken into account to weight priority data 2. The system of claim 1, [Explanation of symbols]

[0208] 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 reception unit that receives patient information; an analysis unit that analyzes the information received by the reception unit; a search unit that searches for hospitals based on the information analyzed by the analysis unit; a suggestion unit that suggests a destination based on the search results by the search unit; Equipped A system characterized by:

2. Equipped with an update unit that manages the update frequency of the database 2. The system of claim 1 .

3. Equipped with a provision section that manages information provided by the hospital 2. The system of claim 1 .

4. Equipped with a protection section to protect patient privacy 2. The system of claim 1 .

5. Equipped with a priority section that assigns priorities 2. The system of claim 1 .

6. The search unit is Search for hospitals based on patient symptoms or urgency 2. The system of claim 1 .

7. The suggestion unit, Propose a delivery location based on the distance to the hospital or traffic conditions 2. The system of claim 1 .

8. The reception unit is Infer patient emotions and adjust information entry methods based on the inferred patient emotions 2. The system of claim 1 .

9. The reception unit is Analyze the patient's past medical history and provide the optimal format for entering information 2. The system of claim 1 .

10. The reception unit is Dynamically change input fields based on the patient's current health status and urgency as information is entered 2. The system of claim 1 .

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