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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, the process of quickly searching for an appropriate hospital and deciding on the destination when an emergency patient is transported relies on telephone confirmation, which has the problem of being time-consuming.

[0005] The system according to the embodiment aims to quickly search for an appropriate hospital when an emergency transport is required and to suggest a hospital where the patient should be taken. [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 search results obtained by the search unit. [Effects 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 explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An emergency transport support system according to an embodiment of the present invention is a system that instantly searches for available hospital rooms and specialized hospitals to determine the optimal destination for emergency transport. This emergency transport support system uses a generative AI to enable instantaneous searches. For example, a paramedic inputs patient information. The generative AI then analyzes the information and searches for available hospital rooms and specialized hospitals. Based on the search results, the system suggests the optimal destination. This system significantly improves the efficiency of emergency transport and enables prompt treatment of patients. For example, a paramedic inputs information such as the patient's symptoms, urgency, and required specialized hospitals. This information is input into the generative AI. The generative AI then analyzes the input information. Based on the patient information, the generative AI searches for available hospital rooms and specialized hospitals. For example, for a patient with a heart attack, the generative AI searches for hospitals specializing in cardiac care. Based on the search results, the generative AI suggests the optimal destination. For example, the system suggests a hospital with available hospital rooms and specialized hospitals. This suggestion is instantly provided to the paramedic. This system significantly improves the efficiency of emergency transport. This eliminates the need for emergency personnel to check over the phone and allows them to instantly determine the optimal transport location. This also enables patients to receive treatment more quickly, which is expected to improve survival rates. 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 suggestion unit. The reception unit accepts patient information. The patient information includes, but is not limited to, medical history, symptoms, and personal information. The reception unit inputs information such as, for example, 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 may be performed using, for example, data mining or statistical analysis, but is not limited to, the example. The search unit uses a generation AI to search for hospitals based on the information analyzed by the analysis unit. The search is performed based on, for example, a search algorithm or filtering conditions, but is not limited to, the example. The suggestion unit proposes an optimal hospital destination based on the results of the search by the search unit. The suggestion is performed based on, for example, a proposal algorithm or evaluation criteria, but is not limited to, the example. This enables the emergency transport support system according to the embodiment to 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. The suggestion unit suggests the optimal destination based on the search results and provides it to paramedics. This significantly improves the efficiency of emergency transport and enables faster treatment of patients.

[0030] The reception unit accepts patient information. Patient information includes, but is not limited to, medical history, symptoms, and personal information. The reception unit inputs information such as the patient's symptoms, urgency, and required specialization. Specifically, the reception unit provides an interface designed to enable emergency medical personnel and medical staff to quickly and accurately input information. This interface can be used with tablets, smartphones, or dedicated input devices, and also includes features such as voice input and barcode scanning. For example, emergency medical personnel can check the patient's symptoms on-site and record them using voice input. Furthermore, by scanning the patient's medical ID card, past medical history, allergy information, and other information can be automatically acquired. 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 can be performed using methods such as, but not limited to, data mining and statistical analysis. Specifically, the generation AI identifies the optimal treatment method and required 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 of similar cases based on past data and propose the optimal treatment method. Furthermore, the generation AI considers hospital availability and doctor schedule information, which are updated in real time, to identify the optimal destination. This allows the analysis unit to quickly and accurately analyze patient 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 these examples. Specifically, the generation AI searches for the most suitable hospital based on information such as the patient's symptoms and urgency, required specialties, and hospital availability. 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. It can also identify the hospital best suited to the patient's symptoms based on hospital specialties, facilities, and doctor schedule information. Furthermore, the generation AI considers hospital availability, which is updated in real time, to identify the optimal destination. This allows the search unit to 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 proposes an optimal destination based on the search results obtained by the search unit. The proposal may be based on, for example, a proposal algorithm or evaluation criteria, but is not limited to such examples. Specifically, the suggestion unit uses a generation AI to identify an optimal destination based on the search results and provide the destination to the emergency medical technician. For example, the generation AI may comprehensively evaluate information such as the patient's symptoms, urgency, and hospital availability, and propose the optimal destination. The suggestion unit also provides an interface for displaying the proposal content in an easy-to-understand manner to emergency medical technicians. For example, the proposal may display information such as the name, address, contact information, and travel route of the optimal destination hospital on a tablet or smartphone screen, and may alert the emergency medical technician with voice guidance or vibration notifications. Furthermore, the suggestion unit can update the proposal content in real time and make proposals based on the latest information. This allows the suggestion unit to provide emergency medical technicians with quick and accurate destination suggestions, thereby supporting rapid treatment of patients.

[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, but is not limited to, periodic updates and real-time updates, for example. The update unit, for example, sets an update schedule for the database and updates the data periodically. The update unit also has a function to update 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, by managing the update frequency of the database, the latest information can be provided. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit inputs information from the hospital into the generation AI and causes the generation AI to update the database.

[0035] The emergency transport support system includes a providing unit that manages information provided by the hospital. The providing unit manages the information provided by the hospital. Information provided includes, for example, data format and timing of provision, but is not limited to these 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 information provision from the hospital. For example, the providing unit sets a schedule for information provision from the hospital and receives information periodically. This allows accurate information to be provided by managing the information provided by the hospital. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs information from the hospital into a generating AI and causes the generating AI to manage the information provision.

[0036] The emergency transport support system includes a protection unit that protects the privacy of patients. The protection unit protects the privacy of patients. Examples of privacy protection include, but are not limited to, data encryption and access control. For example, the protection unit encrypts patient information to prevent third parties from accessing it. The protection unit also performs access control to allow only specific users to access the patient information. For example, the protection unit encrypts patient information and stores it in a database. The protection unit also creates an access control list to allow only specific users to access the information. This protects patient privacy and allows the system to be used with peace of mind. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit may input patient information into a generation AI and have the generation AI perform privacy protection processing.

[0037] The emergency transport support system includes a priority unit that performs prioritization. The priority unit performs prioritization. Prioritization includes, but is not limited to, examples of urgency and importance. The priority unit determines the priority of information based on, for example, the patient's symptoms and urgency. The priority unit also has a function for determining the priority of information based on importance. For example, if the patient's urgency is high, the priority unit displays only the minimum necessary input items. Furthermore, if the patient's health condition is stable, the priority unit displays detailed input items. This enables efficient processing by performing prioritization. Some or all of the above-described processing in the priority unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority unit may input patient information into a generation AI and have the generation AI perform the prioritization process.

[0038] The search unit can search for hospitals based on the patient's symptoms or urgency. The search unit searches for hospitals based on, for example, the patient's symptoms and urgency. Symptoms include, for example, but are not limited to, the degree of pain and fever. Urgency includes, for example, but is not limited to, the degree of life-threatening condition and the rate at which symptoms progress. For example, in the case of a patient with a heart attack, the search unit searches for hospitals specializing in cardiac care. Furthermore, if the urgency is high, the search unit can also prioritize searching for the nearest hospital. This allows for the rapid search of an appropriate hospital by searching for hospitals based on the patient's symptoms and urgency. Some or all of the above-described 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 into the generation AI and cause the generation AI to search for hospitals.

[0039] The suggestion unit can suggest a delivery destination taking into consideration the distance to the hospital or traffic conditions. The suggestion unit suggests a delivery destination taking into consideration, for example, the distance to the hospital and traffic conditions. Examples of distance include, but are not limited to, straight-line distance and travel time. Examples of traffic conditions include, but are not limited to, traffic congestion and the operation status of public transportation. For example, the suggestion unit preferentially suggests the hospital closest to the patient's current location. The suggestion unit can also suggest the hospital that can be reached most quickly by taking into consideration traffic conditions. This enables rapid transport by proposing the optimal delivery destination taking into consideration the distance to the hospital and traffic conditions. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without using, AI. For example, the suggestion unit may input patient information into the generation AI and cause the generation AI to suggest a delivery 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. Medical history includes, but is not limited to, past medical records and prescription history. For example, the reception unit automatically displays relevant input fields based on the patient's past medical history. The reception unit can also automatically select the necessary specialty from the patient's past treatment history. Furthermore, the reception unit can refer to the patient's past medical data and customize the input fields. In this way, the optimal information input format can be provided by analyzing the patient's past medical history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's medical history data into a generation AI and have the generation AI provide the optimal information input format.

[0041] The reception unit can dynamically change input items based on the patient's current health condition and urgency when inputting information. For example, the reception unit dynamically changes input items based on the patient's current health condition and urgency when inputting information. Examples of health conditions include, but are not limited to, vital signs and self-reporting. Examples of urgency include, but are not limited to, the degree of life-threatening illness and the rate of progression of symptoms. For example, the reception unit displays only the minimum necessary input items when the patient's health condition is high. Furthermore, the reception unit can display detailed input items when the patient's health condition is stable. Furthermore, the reception unit can dynamically change input items based on the patient's symptoms. This enables appropriate information input by dynamically changing the input items based on the patient's health condition and urgency. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the patient's health condition data into the generation AI and cause the generation AI to dynamically change the input items.

[0042] The reception unit can prioritize input of highly relevant information in consideration of the patient's geographical location information when inputting information. For example, the reception unit prioritizes input of highly relevant information in consideration of the patient's geographical location information when inputting information. Geographical location information includes, but is not limited to, GPS data and address information. For example, the reception unit prioritizes input of nearby hospital information based on the patient's current location. The reception unit can also prioritize input of information of related medical institutions by referring to the patient's geographical location information. Furthermore, the reception unit can suggest the optimal delivery destination based on the patient's location information. In this way, highly relevant information can be prioritized by taking the patient's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, 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 prioritize input of highly relevant information.

[0043] The reception unit can analyze the patient's social media activity and input relevant information when inputting information. For example, the reception unit can analyze the patient's social media activity and input relevant information when inputting information. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, the reception unit can analyze the patient's social media posts and input information related to their health condition. The reception unit can also estimate the patient's past medical history from the patient's social media activity and customize the input fields. Furthermore, the reception unit can automatically select the necessary specialty based on the patient's social media data. This allows relevant information to be input by analyzing the patient's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the patient's social media data into a generation AI and have the generation AI input relevant information.

[0044] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. Medical data includes, but is not limited to, medical records and test results. 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. In this way, the analysis algorithm can be optimized by referring to the patient's past medical data. Some 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 patient's medical data into the generation AI and cause the generation AI to optimize the analysis algorithm.

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

[0046] The analysis unit can adjust the analysis results by taking into account the patient's geographic location information during analysis. For example, the analysis unit adjusts the analysis results by taking into account the patient's geographic location information during analysis. Geographic location information includes, but is not limited to, GPS data and address information. For example, the analysis unit provides optimal analysis results based on the patient's current location. The analysis unit can also refer to the patient's geographic location information and reflect information about related medical institutions in the analysis results. Furthermore, the analysis unit can suggest the optimal delivery location based on the patient's location information. This allows appropriate analysis results to be provided by taking into account the patient's geographic location information. Some or all of the above-described processing by 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 patient's location information data into the generation AI and cause the generation AI to adjust the analysis results.

[0047] The analysis unit can improve the accuracy of the analysis by referring to literature related to the patient during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the patient during analysis. Examples of relevant literature include, but are not limited to, academic papers and medical guidelines. For example, the analysis unit can refer to the latest medical literature related to the patient's symptoms and reflect the results of the analysis. 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 relevant literature based on the patient's symptoms and reflect the results of the analysis. By doing so, the accuracy of the analysis can be improved by referring to literature related to the patient. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the patient's symptom data into the generation AI and cause the generation AI to refer to relevant literature.

[0048] The search unit can improve search accuracy by taking into account the interrelationships between hospitals during a search. The search unit, for example, improves search accuracy by taking into account the interrelationships between hospitals during a search. Interrelationships include, but are not limited to, partnerships and referral records. For example, the search unit searches for the optimal delivery destination based on collaboration information between hospitals. The search unit can also optimize search results by taking into account hospital specialties and facility information. Furthermore, the search unit can analyze the interrelationships between hospitals and suggest the most suitable hospital. In this way, by taking into account the interrelationships between hospitals, search accuracy can be improved. Some or all of the above-described 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 hospital interrelationship data into the generation AI and cause the generation AI to improve search accuracy.

[0049] The search unit may perform a search while taking into account the hospital's specialty and equipment information. For example, the search unit may perform a search while taking into account the hospital's specialty and equipment information. Specialties include, but are not limited to, internal medicine, surgery, obstetrics and gynecology, for example. Equipment information includes, but is not limited to, the number of MRI machines and operating rooms, for example. For example, the search unit may search for hospitals with a specialty based on the patient's symptoms. The search unit may also search for the optimal delivery destination based on the hospital's equipment information. Furthermore, the search unit may combine the hospital's specialty and equipment information to suggest the optimal hospital. This allows for a search for an appropriate hospital by taking into account the hospital's specialty and equipment information. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without AI. For example, the search unit may input hospital specialty data into the generation AI and cause the generation AI to perform a search.

[0050] The search unit may perform a search while taking into account the geographical distribution of hospitals. For example, the search unit may perform a search while taking into account the geographical distribution of hospitals. Geographical distribution includes, but is not limited to, urban areas and rural areas. For example, the search unit may preferentially search for nearby hospitals based on the patient's current location. The search unit may also refer to the geographical distribution of hospitals to suggest the optimal delivery destination. Furthermore, the search unit may search for the optimal hospital based on the patient's location information. This allows for a search for an appropriate hospital by taking into account the geographical distribution of hospitals. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input hospital geographical distribution data into the generation AI and cause the generation AI to perform a search.

[0051] The search unit can improve search accuracy by referring to related literature on the hospital during a search. The search unit can improve search accuracy by, for example, referring to related literature on the hospital during a search. 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 hospital's specialty and reflect it in the search results. The search unit can also optimize the search algorithm by referring to literature related to the hospital's equipment information. Furthermore, the search unit can automatically search for related literature based on the hospital's specialty and reflect it in the search results. By doing so, by referring to related literature on the hospital, search accuracy can be improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can input related literature data on the hospital into the generation AI and cause the generation AI to improve search accuracy.

[0052] The suggestion unit can adjust the level of detail of the proposal based on the importance of the hospital when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the hospital when making the proposal. The importance includes, but is not limited to, for example, the size and specialization of the hospital. For example, the suggestion unit preferentially suggests hospitals with high importance. The suggestion unit can also adjust the level of detail of the proposal depending on the importance of the hospital. Furthermore, the suggestion unit can make detailed suggestions for hospitals with high importance. This enables appropriate suggestions to be made by adjusting the level of detail of the proposal based on the importance of the hospital. Some or all of the above-described 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 hospital importance data 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 suggestion algorithms depending on the hospital category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the hospital category when making a suggestion. Categories include, but are not limited to, general hospitals and specialized hospitals. For example, the suggestion unit applies a rapid suggestion algorithm to hospitals with high urgency. The suggestion unit can also apply a detailed suggestion algorithm to hospitals specializing in specialized fields. Furthermore, the suggestion unit can apply a standard suggestion algorithm to general hospitals. This enables appropriate suggestions by applying different suggestion algorithms depending on the hospital category. Some 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 hospital category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0054] The suggestion unit may determine the priority of the suggestions by taking into account the distance to the hospital and traffic conditions when making the suggestions. For example, the suggestion unit may determine the priority of the suggestions by taking into account the distance to the hospital and traffic conditions when making the suggestions. Examples of distance include, but are not limited to, straight-line distance and travel time. Examples of traffic conditions include, but are not limited to, traffic congestion and the operation status of public transportation. For example, the suggestion unit may preferentially suggest the hospital closest to the patient's current location. The suggestion unit may also consider traffic conditions to suggest the hospital that can be reached most quickly. Furthermore, the suggestion unit may comprehensively determine the distance and traffic conditions to suggest the most appropriate hospital. This enables appropriate suggestions by taking into account the distance to the hospital and traffic conditions. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the distance to the hospital and traffic conditions into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the hospitals when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the hospitals when making suggestions. Examples of relevance include, but are not limited to, matching medical specialties and past referral records. For example, the suggestion unit prioritizes suggesting hospitals that are most relevant to the patient's symptoms. The suggestion unit can also recommend highly relevant hospitals based on the hospital's specialty. Furthermore, the suggestion unit can prioritize highly relevant hospitals according to the patient's urgency. This allows for appropriate suggestions by adjusting the order of suggestions based on the relevance of the hospitals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input hospital relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0056] The update unit can optimize the update algorithm by referring to past update data during an update. For example, the update unit optimizes the update algorithm by referring to past update data during an update. Update data includes, but is not limited to, past medical records and patient feedback. 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 optimize the update algorithm by referring to past update data. In this way, the update algorithm can be optimized by referring to the past update data. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data to a generation AI and cause the generation AI to optimize the update algorithm.

[0057] The update unit can weight the update data taking into account the geographical location information of the hospital during updating. For example, the update unit weights the update data taking into account the geographical location information of the hospital during updating. Geographical location information includes, but is not limited to, GPS data and address information. For example, the update unit prioritizes updating data of hospitals 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 refer to the patient's location information and select optimal update data. This allows appropriate data to be updated preferentially by taking into account the geographical location information of the hospital. Some or all of the above-described processing in the update unit may be performed using, for example, 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 weight the update data.

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

[0059] The providing unit can select the optimal information provision method by taking into consideration the geographical location information of the hospital when providing information. For example, the providing unit selects the optimal information provision method by taking into consideration the geographical location information of the hospital when providing information. Geographical location information includes, but is not limited to, GPS data and address information. For example, the providing unit may preferentially provide information about hospitals close to the patient's current location. The providing unit can also select the optimal information provision method based on the geographical location information of the hospital. Furthermore, the providing unit can also select the optimal information provision method by referring to the patient's location information. This enables appropriate information provision by taking into consideration the geographical location information of the hospital. Some or all of the above-described processing by the providing unit may be performed using, for example, 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 information provision method.

[0060] The protection unit can optimize the protection algorithm by referring to past protection data during privacy protection. For example, the protection unit optimizes the protection algorithm by referring to past protection data during privacy protection. Protection data includes, for example, past protection records and feedback of protection results, but is not limited to these 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 optimize the protection algorithm by referring to past protection data. In this way, the protection algorithm can be optimized by referring to the past protection data. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input past protection data to a generation AI and cause the generation AI to optimize the protection algorithm.

[0061] The protection unit may weight the protection data taking into account the geographic location information of the hospital during privacy protection. For example, the protection unit may weight the protection data taking into account the geographic location information of the hospital during privacy protection. Examples of geographic location information include, but are not limited to, GPS data and address information. For example, the protection unit may prioritize protection of data from hospitals close to the patient's current location. The protection unit may also weight the protection data based on the geographic location information of the hospital. Furthermore, the protection unit may refer to the patient's location information and select optimal protection data. This allows appropriate data to be prioritized for protection by taking into account the geographic location information of the hospital. Some or all of the above-described processing in the protection unit may be performed using, or without, AI. For example, the protection unit may input geographic location information data of the hospital to the generation AI and cause the generation AI to weight the protection data.

[0062] The priority unit can optimize the priority algorithm by referring to past priority data when prioritizing. For example, the priority unit can optimize the priority algorithm by referring to past priority data when prioritizing. Priority data includes, for example, past priority records and feedback of priority results, but is not limited to these 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 optimize the priority algorithm by referring to past priority data. In this way, the priority algorithm can be optimized by referring to past priority data. Some or all of the above-mentioned processing in the priority unit may be performed, for example, using AI or without using AI. For example, the priority unit can input past priority data to a generation AI and cause the generation AI to optimize the priority algorithm.

[0063] The priority unit may weight the priority data taking into account the geographic location information of the hospital when prioritizing. For example, the priority unit may weight the priority data taking into account the geographic location information of the hospital when prioritizing. Geographic location information includes, but is not limited to, GPS data and address information. For example, the priority unit may preferentially process data from hospitals close to the patient's current location. The priority unit may also weight the priority data based on the geographic location information of the hospital. Furthermore, the priority unit may refer to the patient's location information and select optimal priority data. This allows appropriate data to be preferentially processed by taking into account the geographic location information of the hospital. Some or all of the above-described processing in the priority unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority unit may input geographic location information data of hospitals to the generation AI and cause the generation AI to weight 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 can 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 system if an abnormality is detected. The monitoring unit can also transmit the patient's vital sign data to an analysis unit, which can then reevaluate the optimal transport location based on this data. Furthermore, the monitoring unit can record fluctuations in the patient's vital signs and use this information 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 also be equipped with a notification unit that automatically notifies the patient's family and emergency contacts. For example, when the patient's transport destination has been determined, the notification unit sends a notification to the 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 allows the patient's family and emergency contacts to receive prompt and accurate information, giving them peace of mind.

[0067] The emergency transport support system can further include a cloud management unit that manages patients' past medical data on the cloud. The cloud management unit, for example, stores patients' medical records and test results in the cloud, making them accessible as needed. The cloud management unit also facilitates data sharing between medical institutions, enabling rapid information provision. Furthermore, the cloud management unit can regularly back up data to ensure data security. This allows for efficient management of patients' medical data and rapid provision of necessary information.

[0068] The emergency transport support system can further include a multilingual support unit that provides information according to the patient's language and culture. The multilingual support unit, for example, 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 cultural background into consideration and provides information in a format that is easy for patients to understand. Furthermore, the multilingual support unit can support medical staff in smoothly communicating with patients who speak different languages. This eliminates communication barriers due to language and cultural differences and enables the provision of appropriate medical services.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit accepts patient information. This information includes medical history, symptoms, personal information, etc. The reception unit provides an interface for entering information such as the patient's symptoms, urgency, and required specialty. Step 2: The analysis unit uses the generative 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 destination based on the search results. The proposal is based on the proposed algorithm and evaluation criteria. The suggestion unit proposes the optimal destination and provides it to the emergency responders.

[0071] (Example 2) An emergency transport support system according to an embodiment of the present invention is a system that instantly searches for available hospital rooms and specialized hospitals to determine the optimal destination for emergency transport. This emergency transport support system uses a generative AI to enable instantaneous searches. For example, a paramedic inputs patient information. The generative AI then analyzes the information and searches for available hospital rooms and specialized hospitals. Based on the search results, the system suggests the optimal destination. This system significantly improves the efficiency of emergency transport and enables prompt treatment of patients. For example, a paramedic inputs information such as the patient's symptoms, urgency, and required specialized hospitals. This information is input into the generative AI. The generative AI then analyzes the input information. Based on the patient information, the generative AI searches for available hospital rooms and specialized hospitals. For example, for a patient with a heart attack, the generative AI searches for hospitals specializing in cardiac care. Based on the search results, the generative AI suggests the optimal destination. For example, the system suggests a hospital with available hospital rooms and specialized hospitals. This suggestion is instantly provided to the paramedic. This system significantly improves the efficiency of emergency transport. This eliminates the need for emergency personnel to check over the phone and allows them to instantly determine the optimal transport location. This also enables patients to receive treatment more quickly, which is expected to improve survival rates. 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 suggestion unit. The reception unit accepts patient information. The patient information includes, but is not limited to, medical history, symptoms, and personal information. The reception unit inputs information such as, for example, 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 may be performed using, for example, data mining or statistical analysis, but is not limited to, the example. The search unit uses a generation AI to search for hospitals based on the information analyzed by the analysis unit. The search is performed based on, for example, a search algorithm or filtering conditions, but is not limited to, the example. The suggestion unit proposes an optimal hospital destination based on the results of the search by the search unit. The suggestion is performed based on, for example, a proposal algorithm or evaluation criteria, but is not limited to, the example. This enables the emergency transport support system according to the embodiment to 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. The suggestion unit suggests the optimal destination based on the search results and provides it to paramedics. This significantly improves the efficiency of emergency transport and enables faster treatment of patients.

[0073] The reception unit accepts patient information. Patient information includes, but is not limited to, medical history, symptoms, and personal information. The reception unit inputs information such as the patient's symptoms, urgency, and required specialization. Specifically, the reception unit provides an interface designed to enable emergency medical personnel and medical staff to quickly and accurately input information. This interface can be used with tablets, smartphones, or dedicated input devices, and also includes features such as voice input and barcode scanning. For example, emergency medical personnel can check the patient's symptoms on-site and record them using voice input. Furthermore, by scanning the patient's medical ID card, past medical history, allergy information, and other information can be automatically acquired. 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 can be performed using methods such as, but not limited to, data mining and statistical analysis. Specifically, the generation AI identifies the optimal treatment method and required 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 of similar cases based on past data and propose the optimal treatment method. Furthermore, the generation AI considers hospital availability and doctor schedule information, which are updated in real time, to identify the optimal destination. This allows the analysis unit to quickly and accurately analyze patient 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 these examples. Specifically, the generation AI searches for the most suitable hospital based on information such as the patient's symptoms and urgency, required specialties, and hospital availability. 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. It can also identify the hospital best suited to the patient's symptoms based on hospital specialties, facilities, and doctor schedule information. Furthermore, the generation AI considers hospital availability, which is updated in real time, to identify the optimal destination. This allows the search unit to 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 proposes an optimal destination based on the search results obtained by the search unit. The proposal may be based on, for example, a proposal algorithm or evaluation criteria, but is not limited to such examples. Specifically, the suggestion unit uses a generation AI to identify an optimal destination based on the search results and provide the destination to the emergency medical technician. For example, the generation AI may comprehensively evaluate information such as the patient's symptoms, urgency, and hospital availability, and propose the optimal destination. The suggestion unit also provides an interface for displaying the proposal content in an easy-to-understand manner to emergency medical technicians. For example, the proposal may display information such as the name, address, contact information, and travel route of the optimal destination hospital on a tablet or smartphone screen, and may alert the emergency medical technician with voice guidance or vibration notifications. Furthermore, the suggestion unit can update the proposal content in real time and make proposals based on the latest information. This allows the suggestion unit to provide emergency medical technicians with quick and accurate destination suggestions, thereby supporting rapid treatment of patients.

[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, but is not limited to, periodic updates and real-time updates, for example. The update unit, for example, sets an update schedule for the database and updates the data periodically. The update unit also has a function to update 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, by managing the update frequency of the database, the latest information can be provided. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit inputs information from the hospital into the generation AI and causes the generation AI to update the database.

[0078] The emergency transport support system includes a providing unit that manages information provided by the hospital. The providing unit manages the information provided by the hospital. Information provided includes, for example, data format and timing of provision, but is not limited to these 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 information provision from the hospital. For example, the providing unit sets a schedule for information provision from the hospital and receives information periodically. This allows accurate information to be provided by managing the information provided by the hospital. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs information from the hospital into a generating AI and causes the generating AI to manage the information provision.

[0079] The emergency transport support system includes a protection unit that protects the privacy of patients. The protection unit protects the privacy of patients. Examples of privacy protection include, but are not limited to, data encryption and access control. For example, the protection unit encrypts patient information to prevent third parties from accessing it. The protection unit also performs access control to allow only specific users to access the patient information. For example, the protection unit encrypts patient information and stores it in a database. The protection unit also creates an access control list to allow only specific users to access the information. This protects patient privacy and allows the system to be used with peace of mind. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit may input patient information into a generation AI and have the generation AI perform privacy protection processing.

[0080] The emergency transport support system includes a priority unit that performs prioritization. The priority unit performs prioritization. Prioritization includes, but is not limited to, examples of urgency and importance. The priority unit determines the priority of information based on, for example, the patient's symptoms and urgency. The priority unit also has a function for determining the priority of information based on importance. For example, if the patient's urgency is high, the priority unit displays only the minimum necessary input items. Furthermore, if the patient's health condition is stable, the priority unit displays detailed input items. This enables efficient processing by performing prioritization. Some or all of the above-described processing in the priority unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority unit may input patient information into a generation AI and have the generation AI perform the prioritization process.

[0081] The search unit can search for hospitals based on the patient's symptoms or urgency. The search unit searches for hospitals based on, for example, the patient's symptoms and urgency. Symptoms include, for example, but are not limited to, the degree of pain and fever. Urgency includes, for example, but is not limited to, the degree of life-threatening condition and the rate at which symptoms progress. For example, in the case of a patient with a heart attack, the search unit searches for hospitals specializing in cardiac care. Furthermore, if the urgency is high, the search unit can also prioritize searching for the nearest hospital. This allows for the rapid search of an appropriate hospital by searching for hospitals based on the patient's symptoms and urgency. Some or all of the above-described 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 into the generation AI and cause the generation AI to search for hospitals.

[0082] The suggestion unit can suggest a delivery destination taking into consideration the distance to the hospital or traffic conditions. The suggestion unit suggests a delivery destination taking into consideration, for example, the distance to the hospital and traffic conditions. Examples of distance include, but are not limited to, straight-line distance and travel time. Examples of traffic conditions include, but are not limited to, traffic congestion and the operation status of public transportation. For example, the suggestion unit preferentially suggests the hospital closest to the patient's current location. The suggestion unit can also suggest the hospital that can be reached most quickly by taking into consideration traffic conditions. This enables rapid transport by proposing the optimal delivery destination taking into consideration the distance to the hospital and traffic conditions. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without using, AI. For example, the suggestion unit may input patient information into the generation AI and cause the generation AI to suggest a delivery destination.

[0083] The reception unit can estimate the patient's emotions and adjust the information input method based on the estimated patient's emotions. The reception unit, for example, estimates the patient's emotions and adjusts the information input method based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, if the patient is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the patient is in a hurry, the reception unit can prioritize voice input to enable quick information input. This allows for more appropriate information input by adjusting the information input method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without 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. Medical history includes, but is not limited to, past medical records and prescription history. For example, the reception unit automatically displays relevant input fields based on the patient's past medical history. The reception unit can also automatically select the necessary specialty from the patient's past treatment history. Furthermore, the reception unit can refer to the patient's past medical data and customize the input fields. In this way, the optimal information input format can be provided by analyzing the patient's past medical history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the patient's medical history data into a generation AI and have the generation AI provide the optimal information input format.

[0085] The reception unit can dynamically change input items based on the patient's current health condition and urgency when inputting information. For example, the reception unit dynamically changes input items based on the patient's current health condition and urgency when inputting information. Examples of health conditions include, but are not limited to, vital signs and self-reporting. Examples of urgency include, but are not limited to, the degree of life-threatening illness and the rate of progression of symptoms. For example, the reception unit displays only the minimum necessary input items when the patient's health condition is high. Furthermore, the reception unit can display detailed input items when the patient's health condition is stable. Furthermore, the reception unit can dynamically change input items based on the patient's symptoms. This enables appropriate information input by dynamically changing the input items based on the patient's health condition and urgency. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the patient's health condition data into the generation AI and cause the generation AI to dynamically change the input items.

[0086] The reception unit can estimate the patient's emotions and determine the priority of information to be input based on the estimated patient's emotions. The reception unit, for example, estimates the patient's emotions and determines the priority of information to be input based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the reception unit can prioritize inputting the most important information. Also, if the patient is relaxed, the reception unit can prioritize inputting detailed information. Furthermore, if the patient is in a hurry, the reception unit can prioritize inputting information with high urgency. In this way, by determining the priority of information to be input based on the patient's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input facial expression data of the patient into the generation AI and have the generation AI perform emotion estimation.

[0087] The reception unit can prioritize input of highly relevant information in consideration of the patient's geographical location information when inputting information. For example, the reception unit prioritizes input of highly relevant information in consideration of the patient's geographical location information when inputting information. Geographical location information includes, but is not limited to, GPS data and address information. For example, the reception unit prioritizes input of nearby hospital information based on the patient's current location. The reception unit can also prioritize input of information of related medical institutions by referring to the patient's geographical location information. Furthermore, the reception unit can suggest the optimal delivery destination based on the patient's location information. In this way, highly relevant information can be prioritized by taking the patient's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, 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 prioritize input of highly relevant information.

[0088] The reception unit can analyze the patient's social media activity and input relevant information when inputting information. For example, the reception unit can analyze the patient's social media activity and input relevant information when inputting information. Social media activity includes, but is not limited to, the content of posts and the number of likes. For example, the reception unit can analyze the patient's social media posts and input information related to their health condition. The reception unit can also estimate the patient's past medical history from the patient's social media activity and customize the input fields. Furthermore, the reception unit can automatically select the necessary specialty based on the patient's social media data. This allows relevant information to be input by analyzing the patient's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the patient's social media data into a generation AI and have the generation AI input relevant information.

[0089] The analysis unit can estimate the patient's emotions and adjust the analysis method based on the estimated patient's emotions. The analysis unit can, for example, estimate the patient's emotions and adjust the analysis method based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the analysis unit can apply a quick and concise analysis method if the patient is anxious. The analysis unit can also apply a detailed analysis method if the patient is relaxed. Furthermore, the analysis unit can prioritize analysis of the most important information if the patient is in a hurry. This enables more appropriate analysis by adjusting the analysis method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI. For example, the analysis unit can input the patient's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] The analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the patient's past medical data during analysis. Medical data includes, but is not limited to, medical records and test results. 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. In this way, the analysis algorithm can be optimized by referring to the patient's past medical data. Some 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 patient's medical data into the generation AI and cause the generation AI to optimize the analysis algorithm.

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

[0092] The analysis unit can estimate the patient's emotions and determine the analysis priority based on the estimated patient's emotions. The analysis unit can, for example, estimate the patient's emotions and determine the analysis priority based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the analysis unit can prioritize analyzing the most important information. Also, if the patient is relaxed, the analysis unit can prioritize analyzing detailed information. Furthermore, if the patient is in a hurry, the analysis unit can prioritize analyzing information with high urgency. This allows important information to be analyzed preferentially by determining the analysis priority based on the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the patient's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The analysis unit can adjust the analysis results by taking into account the patient's geographic location information during analysis. For example, the analysis unit adjusts the analysis results by taking into account the patient's geographic location information during analysis. Geographic location information includes, but is not limited to, GPS data and address information. For example, the analysis unit provides optimal analysis results based on the patient's current location. The analysis unit can also refer to the patient's geographic location information and reflect information about related medical institutions in the analysis results. Furthermore, the analysis unit can suggest the optimal delivery location based on the patient's location information. This allows appropriate analysis results to be provided by taking into account the patient's geographic location information. Some or all of the above-described processing by 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 patient's location information data into the generation AI and cause the generation AI to adjust the analysis results.

[0094] The analysis unit can improve the accuracy of the analysis by referring to literature related to the patient during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the patient during analysis. Examples of relevant literature include, but are not limited to, academic papers and medical guidelines. For example, the analysis unit can refer to the latest medical literature related to the patient's symptoms and reflect the results of the analysis. 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 relevant literature based on the patient's symptoms and reflect the results of the analysis. By doing so, the accuracy of the analysis can be improved by referring to literature related to the patient. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the patient's symptom data into the generation AI and cause the generation AI to refer to relevant literature.

[0095] The search unit can estimate the patient's emotions and adjust search criteria based on the estimated patient's emotions. The search unit can, for example, estimate the patient's emotions and adjust search criteria based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the search unit can apply 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 prioritize searching for the most important information when the patient is in a hurry. This enables appropriate search by adjusting search criteria according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the search unit can be performed using AI, for example, or without AI. For example, the search unit can input facial expression data of the patient into the generation AI and have the generation AI perform emotion estimation.

[0096] The search unit can improve search accuracy by taking into account the interrelationships between hospitals during a search. The search unit, for example, improves search accuracy by taking into account the interrelationships between hospitals during a search. Interrelationships include, but are not limited to, partnerships and referral records. For example, the search unit searches for the optimal delivery destination based on collaboration information between hospitals. The search unit can also optimize search results by taking into account hospital specialties and facility information. Furthermore, the search unit can analyze the interrelationships between hospitals and suggest the most suitable hospital. In this way, by taking into account the interrelationships between hospitals, search accuracy can be improved. Some or all of the above-described 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 hospital interrelationship data into the generation AI and cause the generation AI to improve search accuracy.

[0097] The search unit may perform a search while taking into account the hospital's specialty and equipment information. For example, the search unit may perform a search while taking into account the hospital's specialty and equipment information. Specialties include, but are not limited to, internal medicine, surgery, obstetrics and gynecology, for example. Equipment information includes, but is not limited to, the number of MRI machines and operating rooms, for example. For example, the search unit may search for hospitals with a specialty based on the patient's symptoms. The search unit may also search for the optimal delivery destination based on the hospital's equipment information. Furthermore, the search unit may combine the hospital's specialty and equipment information to suggest the optimal hospital. This allows for a search for an appropriate hospital by taking into account the hospital's specialty and equipment information. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without AI. For example, the search unit may input hospital specialty data into the generation AI and cause the generation AI to perform a search.

[0098] The search unit can estimate the patient's emotion and adjust the display order of search results based on the estimated patient's emotion. The search unit, for example, estimates the patient's emotion and adjusts the display order of search results based on the estimated patient's emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the search unit can prioritize displaying the most important information. Furthermore, if the patient is relaxed, the search unit can prioritize displaying detailed information. Furthermore, if the patient is in a hurry, the search unit can prioritize displaying information with high urgency. This allows important information to be prioritized by adjusting the display order of search results according to the patient's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0099] The search unit may perform a search while taking into account the geographical distribution of hospitals. For example, the search unit may perform a search while taking into account the geographical distribution of hospitals. Geographical distribution includes, but is not limited to, urban areas and rural areas. For example, the search unit may preferentially search for nearby hospitals based on the patient's current location. The search unit may also refer to the geographical distribution of hospitals to suggest the optimal delivery destination. Furthermore, the search unit may search for the optimal hospital based on the patient's location information. This allows for a search for an appropriate hospital by taking into account the geographical distribution of hospitals. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input hospital geographical distribution data into the generation AI and cause the generation AI to perform a search.

[0100] The search unit can improve search accuracy by referring to related literature on the hospital during a search. The search unit can improve search accuracy by, for example, referring to related literature on the hospital during a search. 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 hospital's specialty and reflect it in the search results. The search unit can also optimize the search algorithm by referring to literature related to the hospital's equipment information. Furthermore, the search unit can automatically search for related literature based on the hospital's specialty and reflect it in the search results. By doing so, by referring to related literature on the hospital, search accuracy can be improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or without AI. For example, the search unit can input related literature data on the hospital into the generation AI and cause the generation AI to improve search accuracy.

[0101] The suggestion unit can estimate the patient's emotion and adjust the way the suggestion is expressed based on the estimated patient's emotion. The suggestion unit can, for example, estimate the patient's emotion and adjust the way the suggestion is expressed based on the estimated patient's emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the suggestion unit can make a simple and intuitive suggestion. If the patient is relaxed, the suggestion unit can also make a detailed suggestion. Furthermore, if the patient is in a hurry, the suggestion unit can also make a quick and concise suggestion. This enables appropriate suggestions by adjusting the way the suggestion is expressed based on the patient's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0102] The suggestion unit can adjust the level of detail of the proposal based on the importance of the hospital when making the proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the hospital when making the proposal. The importance includes, but is not limited to, for example, the size and specialization of the hospital. For example, the suggestion unit preferentially suggests hospitals with high importance. The suggestion unit can also adjust the level of detail of the proposal depending on the importance of the hospital. Furthermore, the suggestion unit can make detailed suggestions for hospitals with high importance. This enables appropriate suggestions to be made by adjusting the level of detail of the proposal based on the importance of the hospital. Some or all of the above-described 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 hospital importance data 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 suggestion algorithms depending on the hospital category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the hospital category when making a suggestion. Categories include, but are not limited to, general hospitals and specialized hospitals. For example, the suggestion unit applies a rapid suggestion algorithm to hospitals with high urgency. The suggestion unit can also apply a detailed suggestion algorithm to hospitals specializing in specialized fields. Furthermore, the suggestion unit can apply a standard suggestion algorithm to general hospitals. This enables appropriate suggestions by applying different suggestion algorithms depending on the hospital category. Some 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 hospital category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0104] The suggestion unit can estimate the patient's emotion and adjust the length of the suggestion based on the estimated emotion. The suggestion unit, for example, estimates the patient's emotion and adjusts the length of the suggestion based on the estimated emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the suggestion unit can make a short, concise suggestion. If the patient is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. Furthermore, if the patient is in a hurry, the suggestion unit can make a quick, concise suggestion. This enables appropriate suggestions by adjusting the length of the suggestion based on the patient's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0105] The suggestion unit may determine the priority of the suggestions by taking into account the distance to the hospital and traffic conditions when making the suggestions. For example, the suggestion unit may determine the priority of the suggestions by taking into account the distance to the hospital and traffic conditions when making the suggestions. Examples of distance include, but are not limited to, straight-line distance and travel time. Examples of traffic conditions include, but are not limited to, traffic congestion and the operation status of public transportation. For example, the suggestion unit may preferentially suggest the hospital closest to the patient's current location. The suggestion unit may also consider traffic conditions to suggest the hospital that can be reached most quickly. Furthermore, the suggestion unit may comprehensively determine the distance and traffic conditions to suggest the most appropriate hospital. This enables appropriate suggestions by taking into account the distance to the hospital and traffic conditions. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the distance to the hospital and traffic conditions into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0106] The suggestion unit can adjust the order of suggestions based on the relevance of the hospitals when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the hospitals when making suggestions. Examples of relevance include, but are not limited to, matching medical specialties and past referral records. For example, the suggestion unit prioritizes suggesting hospitals that are most relevant to the patient's symptoms. The suggestion unit can also recommend highly relevant hospitals based on the hospital's specialty. Furthermore, the suggestion unit can prioritize highly relevant hospitals according to the patient's urgency. This allows for appropriate suggestions by adjusting the order of suggestions based on the relevance of the hospitals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input hospital relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

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

[0108] The update unit can optimize the update algorithm by referring to past update data during an update. For example, the update unit optimizes the update algorithm by referring to past update data during an update. Update data includes, but is not limited to, past medical records and patient feedback. 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 optimize the update algorithm by referring to past update data. In this way, the update algorithm can be optimized by referring to the past update data. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data to a generation AI and cause the generation AI to optimize the update algorithm.

[0109] The update unit can estimate the patient's emotions and determine update priorities based on the estimated patient's emotions. The update unit, for example, estimates the patient's emotions and determines update priorities based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the update unit prioritizes updating important data. The update unit can also maintain the normal update order if the patient is relaxed. Furthermore, if the patient is in a hurry, the update unit can prioritize updating data with high urgency. This allows important data to be updated preferentially by determining update priorities based on the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate emotions.

[0110] The update unit can weight the update data taking into account the geographical location information of the hospital during updating. For example, the update unit weights the update data taking into account the geographical location information of the hospital during updating. Geographical location information includes, but is not limited to, GPS data and address information. For example, the update unit prioritizes updating data of hospitals 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 refer to the patient's location information and select optimal update data. This allows appropriate data to be updated preferentially by taking into account the geographical location information of the hospital. Some or all of the above-described processing in the update unit may be performed using, for example, 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 weight the update data.

[0111] The providing unit can estimate the patient's emotions and adjust the method of providing information based on the estimated patient's emotions. The providing unit, for example, estimates the patient's emotions and adjusts the method of providing information based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the providing unit can provide simple and intuitive information. If the patient is relaxed, the providing unit can also provide detailed information. Furthermore, if the patient is in a hurry, the providing unit can also provide quick and concise information. This enables appropriate information provision by adjusting the method of providing information according to the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the patient's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

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

[0113] The providing unit can estimate the patient's emotions and determine the priority of information provision based on the estimated patient's emotions. The providing unit, for example, estimates the patient's emotions and determines the priority of information provision based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the providing unit can prioritize providing important information. Also, if the patient is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the patient is in a hurry, the providing unit can prioritize providing information with high urgency. In this way, by determining the priority of information provision according to the patient's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input facial expression data of the patient into the generation AI and cause the generation AI to estimate the emotion.

[0114] The providing unit can select the optimal information provision method by taking into consideration the geographical location information of the hospital when providing information. For example, the providing unit selects the optimal information provision method by taking into consideration the geographical location information of the hospital when providing information. Geographical location information includes, but is not limited to, GPS data and address information. For example, the providing unit may preferentially provide information about hospitals close to the patient's current location. The providing unit can also select the optimal information provision method based on the geographical location information of the hospital. Furthermore, the providing unit can also select the optimal information provision method by referring to the patient's location information. This enables appropriate information provision by taking into consideration the geographical location information of the hospital. Some or all of the above-described processing by the providing unit may be performed using, for example, 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 information provision method.

[0115] The protection unit can estimate the patient's emotions and adjust the privacy protection method based on the estimated patient's emotions. The protection unit, for example, estimates the patient's emotions and adjusts the privacy protection method based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the protection unit applies strict privacy protection when the patient is anxious. The protection unit can also apply normal privacy protection when the patient is relaxed. Furthermore, the protection unit can prioritize privacy protection for 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 emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the protection unit may be performed using AI, or without AI. For example, the protection unit can input facial expression data of the patient into the generation AI and have the generation AI perform emotion estimation.

[0116] The protection unit can optimize the protection algorithm by referring to past protection data during privacy protection. For example, the protection unit optimizes the protection algorithm by referring to past protection data during privacy protection. Protection data includes, for example, past protection records and feedback of protection results, but is not limited to these 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 optimize the protection algorithm by referring to past protection data. In this way, the protection algorithm can be optimized by referring to the past protection data. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input past protection data to a generation AI and cause the generation AI to optimize the protection algorithm.

[0117] The protection unit can estimate the patient's emotions and determine the priority of privacy protection based on the estimated patient's emotions. The protection unit, for example, estimates the patient's emotions and determines the priority of privacy protection based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the protection unit prioritizes privacy protection of important data. The protection unit can also apply normal privacy protection if the patient is relaxed. Furthermore, if the patient is in a hurry, the protection unit can prioritize privacy protection of data with high urgency. Thus, by determining the priority of privacy protection based on the patient's emotions, important data can be prioritized for protection. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or without AI. For example, the protection unit can input facial expression data of the patient into the generation AI and have the generation AI estimate the emotion.

[0118] The protection unit may weight the protection data taking into account the geographic location information of the hospital during privacy protection. For example, the protection unit may weight the protection data taking into account the geographic location information of the hospital during privacy protection. Examples of geographic location information include, but are not limited to, GPS data and address information. For example, the protection unit may prioritize protection of data from hospitals close to the patient's current location. The protection unit may also weight the protection data based on the geographic location information of the hospital. Furthermore, the protection unit may refer to the patient's location information and select optimal protection data. This allows appropriate data to be prioritized for protection by taking into account the geographic location information of the hospital. Some or all of the above-described processing in the protection unit may be performed using, or without, AI. For example, the protection unit may input geographic location information data of the hospital to the generation AI and cause the generation AI to weight the protection data.

[0119] The priority unit can estimate the patient's emotions and adjust the prioritization method based on the estimated patient's emotions. The priority unit, for example, estimates the patient's emotions and adjusts the prioritization method based on the estimated patient's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the priority unit prioritizes important information. The priority unit can also apply normal prioritization when the patient is relaxed. Furthermore, if the patient is in a hurry, the priority unit can prioritize information with high urgency. This enables appropriate prioritization by adjusting the prioritization method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the priority unit may be performed using AI, or without AI. For example, the priority unit can input the patient's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0120] The priority unit can optimize the priority algorithm by referring to past priority data when prioritizing. For example, the priority unit can optimize the priority algorithm by referring to past priority data when prioritizing. Priority data includes, for example, past priority records and feedback of priority results, but is not limited to these 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 optimize the priority algorithm by referring to past priority data. In this way, the priority algorithm can be optimized by referring to past priority data. Some or all of the above-mentioned processing in the priority unit may be performed, for example, using AI or without using AI. For example, the priority unit can input past priority data to a generation AI and cause the generation AI to optimize the priority algorithm.

[0121] The priority unit can estimate the patient's emotions and determine the prioritization priority based on the estimated patient's emotions. The priority unit can, for example, estimate the patient's emotions and determine the prioritization priority based on the estimated patient's emotions. Emotion estimation can include, but is not limited to, facial expression recognition and voice analysis. For example, if the patient is feeling anxious, the priority unit can prioritize important information. The priority unit can also apply normal prioritization when the patient is relaxed. Furthermore, if the patient is in a hurry, the priority unit can prioritize information with high urgency. This allows important information to be prioritized by determining the prioritization priority based on the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the priority unit can be performed using, for example, AI, or without AI. For example, the priority unit can input the patient's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0122] The priority unit may weight the priority data taking into account the geographic location information of the hospital when prioritizing. For example, the priority unit may weight the priority data taking into account the geographic location information of the hospital when prioritizing. Geographic location information includes, but is not limited to, GPS data and address information. For example, the priority unit may preferentially process data from hospitals close to the patient's current location. The priority unit may also weight the priority data based on the geographic location information of the hospital. Furthermore, the priority unit may refer to the patient's location information and select optimal priority data. This allows appropriate data to be preferentially processed by taking into account the geographic location information of the hospital. Some or all of the above-described processing in the priority unit may be performed using, for example, AI, or may be performed without using AI. For example, the priority unit may input geographic location information data of hospitals to the generation AI and cause the generation AI to weight 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 can 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 system if an abnormality is detected. The monitoring unit can also transmit the patient's vital sign data to an analysis unit, which can then reevaluate the optimal transport location based on this data. Furthermore, the monitoring unit can record fluctuations in the patient's vital signs and use this information 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 also be equipped with a notification unit that automatically notifies the patient's family and emergency contacts. For example, when the patient's transport destination has been determined, the notification unit sends a notification to the 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 allows the patient's family and emergency contacts to receive prompt and accurate information, giving them peace of mind.

[0126] The emergency transport support system can further include a cloud management unit that manages patients' past medical data on the cloud. The cloud management unit, for example, stores patients' medical records and test results in the cloud, making them accessible as needed. The cloud management unit also facilitates data sharing between medical institutions, enabling rapid information provision. Furthermore, the cloud management unit can regularly back up data to ensure data security. This allows for efficient management of patients' medical data and rapid provision of necessary information.

[0127] The emergency transport support system can further include a multilingual support unit that provides information according to the patient's language and culture. The multilingual support unit, for example, 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 cultural background into consideration and provides information in a format that is easy for patients to understand. Furthermore, the multilingual support unit can support medical staff in smoothly communicating with patients who speak different languages. This eliminates communication barriers due to language and cultural differences and enables the provision of appropriate medical services.

[0128] The emergency transport support system can 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 is feeling anxious, the entertainment unit can play relaxing music to give the patient a sense of security. Alternatively, if the patient is relaxed, the entertainment unit can provide an interesting audio guidance. Furthermore, the entertainment unit can dynamically change the content of the music and audio guidance according to the patient's emotions. This allows for the provision of entertainment that takes into consideration the patient's emotions, reducing stress during transport.

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

[0130] The emergency transport support system can 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 them. If the patient is relaxed, the communication unit can also provide detailed explanations to deepen the patient's understanding. Furthermore, the communication unit can dynamically change the content and method of communication depending on the patient's emotions. This allows for communication that takes into consideration the patient's emotions and increases 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 may, for example, monitor the patient's heart rate and respiratory rate to evaluate the stress level. The stress monitoring unit may also analyze the patient's emotional 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 understood in real time and appropriate measures to be taken.

[0132] The emergency transport support system can 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 relieve pain. Furthermore, if the patient appears relaxed, the medical treatment adjustment unit can also proceed with treatment while providing detailed explanations. Furthermore, the medical treatment adjustment unit can dynamically change the content and order of treatment depending on the patient's emotions. This allows for medical treatment that takes the patient's emotions into consideration, increasing a sense of security during transport.

[0133] The emergency transport support system can further include an information providing unit that estimates the patient's emotions and adjusts the information provided during transport based on the estimated emotions. For example, if the patient feels anxious, the information providing unit provides simple and intuitive information to give the patient a sense of security. Alternatively, if 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 and method of information provided depending on the patient's emotions. This allows the information provided to take the patient's emotions into consideration, increasing the patient's sense of security during transport.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The reception unit accepts patient information. This information includes medical history, symptoms, personal information, etc. The reception unit provides an interface for entering information such as the patient's symptoms, urgency, and required specialty. Step 2: The analysis unit uses the generative 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 destination based on the search results. The proposal is based on the proposed algorithm and evaluation criteria. The suggestion unit proposes the optimal destination and provides it to the emergency responders.

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

[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 engine).<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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these 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 processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[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 a generated 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 suggests the optimal delivery destination based on the search results 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 provision of information by the hospital side by the specific processing unit 290 of the data processing device 12. The protection unit protects patient privacy by the specific processing unit 290 of the data processing device 12. The priority unit assigns priorities 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 modifications 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0147] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] For example, the reception unit can receive patient information using the microphone 238 of the smart glasses 214. The analysis unit analyzes the patient information using a generated 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 suggests the optimal delivery destination based on the search results 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 provision of information 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 assigns priorities 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 modifications 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[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 the optimal delivery destination based on the search results 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 provision of information 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 assigns priorities 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 example described above, and various modifications 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, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[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 audio according to instructions from the processor 46.

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

[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 carried out in a secure state.

[0179] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[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 a generated 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 suggests the optimal delivery destination based on the search results 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 provision of information by the hospital side by the specific processing unit 290 of the data processing device 12. The protection unit protects the privacy of patients by the specific processing unit 290 of the data processing device 12. The priority unit assigns priorities 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 modifications are possible.

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

[0190] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

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

[0193] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[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 USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0198] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

[0201] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0202] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

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

[0205] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[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 proposal unit that proposes a delivery destination based on the search results of the search unit; Equipped with A system characterized by: (Appendix 2) Equipped with an update unit that manages the frequency of database updates 2. The system of claim 1. (Appendix 3) Equipped with a provision department 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 Search for hospitals based on patient symptoms or urgency 2. The system of claim 1. (Appendix 7) The proposal unit Propose a delivery location taking into account the distance to the hospital or traffic conditions 2. The system of claim 1. (Appendix 8) The reception unit Inferring patient emotions and adjusting how information is entered based on the patient's emotions 2. The system of claim 1. (Appendix 9) The reception unit Analyze the patient's past medical history and provide the optimal information entry format 2. The system of claim 1. (Appendix 10) The reception unit 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 Estimate the patient's emotions and prioritize the information to be entered based on the estimated patient emotions. 2. The system of claim 1. (Appendix 12) The reception unit When entering information, the patient's geographic location is taken into account to prioritize the most relevant information. 2. The system of claim 1. (Appendix 13) The reception unit When entering information, analyze the patient's social media activity and enter relevant information 2. The system of claim 1. (Appendix 14) The analysis unit Estimate the patient's emotions and adjust the analysis method based on the estimated patient emotions 2. The system of claim 1. (Appendix 15) The analysis unit 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 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 Estimate the patient's emotions and determine the priority of analysis based on the estimated patient emotions 2. The system of claim 1. (Appendix 18) The analysis unit During analysis, adjust the analysis results by taking into account the patient's geographic location information 2. The system of claim 1. (Appendix 19) The analysis unit 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 Estimate patient sentiment and adjust search criteria based on the estimated patient sentiment 2. The system of claim 1. (Appendix 21) The search unit Improve search accuracy by taking into account the relationships between hospitals when searching 2. The system of claim 1. (Appendix 22) The search unit When searching, consider the hospital's specialty and facility information. 2. The system of claim 1. (Appendix 23) The search unit Inferring patient sentiment and adjusting the order of search results based on the estimated sentiment 2. The system of claim 1. (Appendix 24) The search unit When searching, consider the geographical distribution of hospitals. 2. The system of claim 1. (Appendix 25) The search unit When searching, refer to the hospital's relevant literature to improve search accuracy. 2. The system of claim 1. (Appendix 26) The proposal unit Inferring patient emotions and adjusting the presentation of suggestions based on the inferred emotions 2. The system of claim 1. (Appendix 27) The proposal 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 proposal unit When making suggestions, different suggestion algorithms are applied depending on the hospital category. 2. The system of claim 1. (Appendix 29) The proposal unit Estimate the patient's emotions and adjust the length of the suggestions based on the estimated patient emotions. 2. The system of claim 1. (Appendix 30) The proposal unit When making proposals, the priority of proposals is determined taking into account the distance to hospitals and traffic conditions. 2. The system of claim 1. (Appendix 31) The proposal unit During suggestions, adjust the order of suggestions based on hospital relevance 2. The system of claim 1. (Appendix 32) The update unit Estimate the patient's emotions and adjust the frequency of database updates based on the estimated patient emotions. 2. The system of claim 1. (Appendix 33) The update unit When updating, the update algorithm is optimized by referencing past update data. 2. The system of claim 1. (Appendix 34) The update unit Estimate patient emotions and prioritize updates based on the estimated patient emotions 2. The system of claim 1. (Appendix 35) The update unit 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 Estimate the patient's feelings and adjust the way information is provided based on the estimated patient feelings 2. The system of claim 1. (Appendix 37) The providing unit When providing information, the hospital's past provision history is referenced to select the most appropriate method of provision. 2. The system of claim 1. (Appendix 38) The providing unit Estimate the patient's feelings and prioritize the information provided based on the estimated patient feelings. 2. The system of claim 1. (Appendix 39) The providing unit 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 Estimating patient emotions and adjusting privacy protection methods based on the estimated 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 Estimate the patient's feelings and determine the priority of privacy protection based on the estimated patient's feelings 2. The system of claim 1. (Appendix 43) The protective part is When protecting privacy, weighting of protected data is performed taking into account the geographic location information of hospitals. 2. The system of claim 1. (Appendix 44) The priority portion is Estimate patient sentiment and adjust prioritization methods based on estimated patient sentiment 2. The system of claim 1. (Appendix 45) The priority portion is When prioritizing, reference past priority data to optimize the priority algorithm 2. The system of claim 1. (Appendix 46) The priority portion is Estimate the patient's emotions and determine 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. An emotion estimation means for estimating the emotional state of a patient using an emotion identification model by a processor based on facial expression information or voice information of the patient obtained from a camera or microphone provided on a smart device, smart glasses, headset terminal, or robot used by the patient; and a reception unit for receiving patient information by controlling a reception device of the smart device, smart glasses, headset terminal, or robot according to the estimated emotional state so that if the processor determines that the patient is feeling anxious, a simple and intuitive input interface is displayed on the display unit of the smart device, smart glasses, headset terminal, or robot to minimize input steps, if the processor determines that the patient is relaxed, detailed input options are displayed, or if the processor determines that the patient is in a hurry, voice input is prioritized. An analysis unit in which the processor uses a generation AI to analyze information including the patient's symptoms, medical history, and urgency received by the reception unit using natural language processing technology, and identifies the optimal treatment method or required specialty field; A search unit in which the processor uses a generation AI and a geographic information system to search for hospitals based on the information identified by the analysis unit and the availability of hospitals updated in real time obtained from a database, taking into account the distance from the patient's current location and traffic conditions; a suggestion unit that, based on the search results obtained by the search unit, suggests information including the name, address, contact information, and travel route of the optimal hospital to be taken to the emergency medical personnel via a display or speaker of the smart device, smart glasses, headset terminal, or robot; Equipped with A system characterized by:

2. The present invention further comprises an update unit that manages the update frequency of the database so that, based on the emotional state of the patient estimated by the emotion estimation means, the processor increases the update frequency of the database when it determines that the patient is feeling anxious, maintains the normal update frequency when it determines that the patient is relaxed, or prioritizes increasing the update frequency of important data when it determines that the patient is in a hurry.

2. The system of claim 1.

3. The method further comprises a providing unit that manages information provision on the hospital side so that, based on the emotional state of the patient estimated by the emotion estimation means, the processor provides simple and intuitive information when it determines that the patient is feeling anxious, provides detailed information when it determines that the patient is relaxed, or provides quick and concise information when it determines that the patient is in a hurry.

2. The system of claim 1.

4. The method further comprises a protection unit that protects the patient's privacy by applying strict privacy protection when the processor determines that the patient is feeling anxious based on the emotional state of the patient estimated by the emotion estimation means, applying normal privacy protection when the processor determines that the patient is relaxed, or prioritizing privacy protection of important data when the processor determines that the patient is in a hurry.

2. The system of claim 1.

5. The method further comprises a priority section that prioritizes information based on the emotional state of the patient estimated by the emotion estimation means, such that if the processor determines that the patient is feeling anxious, important information is given priority, if the processor determines that the patient is relaxed, normal prioritization is applied, or if the processor determines that the patient is in a hurry, information of high urgency is given priority.

2. The system of claim 1.

6. The search unit Based on the emotional state of the patient estimated by the emotion estimation means, the processor applies quick and concise search criteria when it determines that the patient is anxious, applies detailed search criteria when it determines that the patient is relaxed, or adjusts the search criteria to prioritize searching for the most important information when it determines that the patient is in a hurry, thereby searching for hospitals based on the patient's symptoms or urgency.

2. The system of claim 1.

7. The proposal unit Based on the emotional state of the patient estimated by the emotion estimation means, the processor adjusts the way the proposal is expressed to make a simple and intuitive proposal when it is determined that the patient is feeling anxious, to make a detailed proposal when it is determined that the patient is relaxed, or to make a quick and concise proposal when it is determined that the patient is in a hurry, and suggests a delivery destination taking into account the distance to a hospital or traffic conditions.

2. The system of claim 1.

8. The reception unit Based on the emotional state of the patient estimated by the emotion estimation means, the processor determines the priority of information to be input, such that the most important information is input preferentially when it is determined that the patient is feeling anxious, detailed information is input preferentially when it is determined that the patient is relaxed, or information with a high degree of urgency is input preferentially when it is determined that the patient is in a hurry, and estimates the patient's emotion, and adjusts the method of inputting information based on the estimated emotion of the patient.

2. The system of claim 1.

9. The reception unit The processor analyzes the patient's past medical history and provides the optimal information input format to the display unit of the smart device, smart glasses, headset terminal, or robot based on the analysis results.

2. The system of claim 1.

10. The reception unit When information is entered, the processor dynamically changes the input items based on the patient's current health condition and urgency.

2. The system of claim 1.

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