System

Abstract

An object of a system according to an embodiment is to quickly and appropriately select and guide an optimal hospital when a physical condition is poor.SOLUTION: A system includes a user information collection part, a hospital database reference part, a hospital selection part, and a guide part. The user information collection unit collects necessary information from the user using the generated AI. The hospital database reference unit refers to the hospital database based on the user information collected by the user information collection unit. The hospital selection unit selects an optimal hospital based on the information referred to by the hospital database reference unit. The guidance unit guides the user with information on the hospital selected by the hospital selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of selecting the most suitable hospital when you are feeling unwell is complicated, making it difficult to quickly find an appropriate medical institution.

[0005] The system according to the embodiment aims to quickly and appropriately select and guide the most suitable hospital when a person is feeling unwell. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, a hospital database reference unit, a hospital selection unit, and a guidance unit. The user information collection unit collects necessary information from the user using a generation AI. The hospital database reference unit references the hospital database based on the user information collected by the user information collection unit. The hospital selection unit selects the most suitable hospital based on the information referenced by the hospital database reference unit. The guidance unit provides the user with information about the hospital selected by the hospital selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately select and guide the user to the most suitable hospital when the user feels unwell. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The hospital selection system according to an embodiment of the present invention is a system in which a generative AI interactively selects and guides the user to the most suitable hospital when a person who does not usually go to the hospital becomes ill and needs to go to the hospital. This allows even people who do not usually go to the hospital to receive appropriate medical care when they are ill.

[0029] A hospital selection system according to an embodiment includes a user information collection unit, a hospital database reference unit, a hospital selection unit, and a guidance unit. The user information collection unit uses a generation AI to collect necessary information from a user. For example, the generation AI asks the user questions such as, "How is your current health condition?", "What symptoms do you have?", "How far from your home is preferable?", "What means of transportation will you use?", "What medical examination will be performed?", "What is the maximum cost?", and "How long of a waiting time are you willing to accept?", and collects the user's answers. The hospital database reference unit references a hospital database based on the user information collected by the user information collection unit. For example, the hospital database includes information such as the location, medical examination contents, costs, waiting times, and transportation methods of each hospital, and the generation AI searches for the optimal hospital based on this information. The hospital selection unit selects the optimal hospital based on the information referenced by the hospital database reference unit. For example, the generation AI selects a hospital that is close to the user's home, has easy access to transportation, provides appropriate medical examination contents, is within budget, and has a short waiting time. The guidance unit provides the user with information about the hospital selected by the hospital selection unit. For example, the generation AI may provide guidance in the form of, "The best hospital is XX Hospital. The address is XX, Chome, XX-cho, XX-shi. The medical examination will be XX, and the cost will be XX yen. The waiting time will be approximately XX minutes. As for transportation, it is XX minutes on foot from XX Station." In this way, the hospital selection system according to the embodiment allows even people who do not usually go to the hospital to receive appropriate medical care when they feel unwell.

[0030] The user information collection unit collects the user's past health data or lifestyle information, enabling more accurate hospital selection. The user information collection unit, for example, collects the user's past health data, and the generation AI selects a hospital based on that data. For example, it references past medical examination history and medication prescription history. The user information collection unit also collects the user's lifestyle information, and the generation AI selects a hospital based on that information. For example, it takes into account the user's eating habits and exercise habits. The user information collection unit also collects the user's past health data and lifestyle information, and the generation AI selects a hospital based on that data. For example, it takes into account the user's sleeping patterns and stress level. This allows for a more appropriate hospital to be selected by taking into account the user's past health data and lifestyle information.

[0031] The user information collection unit can generate additional questions in real time based on the user's answers and collect detailed information. In the user information collection unit, for example, the generation AI generates additional questions in real time based on the user's answers. For example, it asks a question such as, "What specific symptoms do you have?" In addition, the user information collection unit can generate additional questions in real time based on the user's answers and collect detailed information. For example, it asks a question such as, "Have you noticed any changes in your health recently?" In addition, the user information collection unit can generate additional questions in real time based on the user's answers. For example, it asks a question such as, "Have you had similar symptoms in the past?" This allows detailed information to be collected based on the user's answers, making it possible to select a more appropriate hospital.

[0032] The user information collection unit can provide health advice or preventive measures based on the information input by the user. For example, the user information collection unit causes the generation AI to provide health advice based on the information input by the user. For example, the unit may provide advice such as "Make sure to drink plenty of water." The user information collection unit also causes the generation AI to provide preventive measures based on the information input by the user. For example, the unit may suggest preventive measures such as "Wash your hands thoroughly." The user information collection unit also causes the generation AI to provide health advice and preventive measures based on the information input by the user. For example, the unit may suggest advice such as "Make sure to get enough sleep." In this way, the generation AI can provide health advice and preventive measures to the user, thereby assisting them in managing their health.

[0033] The user information collection unit can link the information input by the user with other health-related apps to support comprehensive health management. The user information collection unit, for example, links the information input by the user with other health-related apps to support comprehensive health management. For example, it links with a fitness app to share exercise data. The user information collection unit also links the information input by the user with other health-related apps to support comprehensive health management. For example, it links with a diet management app to share diet data. The user information collection unit also links the information input by the user with other health-related apps to support comprehensive health management. For example, it links with a sleep management app to share sleep data. This makes it possible to link the user's health information with other apps to support comprehensive health management.

[0034] The hospital database reference unit adds the specialties or ratings of doctors at each hospital to the hospital database, allowing for more appropriate hospital selection. For example, the hospital database reference unit adds the specialties of doctors at each hospital to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a cardiologist. The hospital database reference unit also adds the ratings of doctors at each hospital to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with doctors who are highly rated by patients. The hospital database reference unit also adds the specialties and ratings of doctors at each hospital to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with many specialists. This allows for a more appropriate hospital to be selected by taking into account the specialties and ratings of doctors at each hospital.

[0035] The hospital database reference unit reflects the hospital's congestion status or reservation status in real time, thereby reducing waiting times. The hospital database reference unit, for example, reflects the hospital's congestion status in the hospital database in real time, and the generation AI selects a hospital based on that information. For example, it selects a hospital that is not congested. The hospital database reference unit also reflects the hospital's reservation status in the hospital database in real time, and the generation AI selects a hospital based on that information. For example, it selects a hospital where it is easy to make an appointment. The hospital database reference unit also reflects the hospital's congestion status and reservation status in the hospital database in real time, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a short waiting time. In this way, waiting times can be reduced by reflecting the hospital's congestion status and reservation status in real time.

[0036] The hospital database reference unit can add information about hospital facilities or the latest medical technology to the database and add it to the selection criteria. For example, the hospital database reference unit adds hospital facility information to the hospital database, and the generating AI selects a hospital based on that information. For example, it selects a hospital with the latest MRI equipment. The hospital database reference unit also adds information about the latest medical technology to the hospital database, and the generating AI selects a hospital based on that information. For example, it selects a hospital with the latest surgical techniques. The hospital database reference unit also adds information about hospital facilities and the latest medical technology to the hospital database, and the generating AI selects a hospital based on that information. For example, it selects a hospital that offers the latest treatment methods. This makes it possible to select a more appropriate hospital by taking into account information about hospital facilities and the latest medical technology.

[0037] The hospital database reference unit can link the hospital database with other medical institutions to provide comprehensive medical services. For example, the hospital database reference unit links the hospital database with a clinic, and the generation AI selects a hospital based on that information. For example, after an initial consultation at a clinic, the generation AI can refer the patient to an appropriate hospital. The hospital database reference unit also links the hospital database with a pharmacy, and the generation AI selects a hospital based on that information. For example, the generation AI can select a hospital based on medication prescription information at a pharmacy. The hospital database reference unit also links the hospital database with other medical institutions, and the generation AI can select a hospital based on that information. For example, information from multiple medical institutions can be integrated to provide comprehensive medical services. This allows the hospital database to be linked with other medical institutions to provide comprehensive medical services.

[0038] The hospital database reference unit can add user reviews or feedback to the hospital database to use as a reference for selection. For example, the hospital database reference unit adds user reviews to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a high review rating. The hospital database reference unit also adds user feedback to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital based on the feedback content. The hospital database reference unit also adds user reviews and feedback to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a large number of positive reviews. This makes it possible to select a more appropriate hospital by taking user reviews and feedback into consideration.

[0039] The hospital selection unit can select the most appropriate hospital by taking into account the user's past medical history or allergy information. The hospital selection unit, for example, takes into account the user's past medical history, and the generating AI selects the most appropriate hospital. For example, it selects a hospital where the user has received treatment in the past. The hospital selection unit also takes into account the user's allergy information, and the generating AI selects the most appropriate hospital. For example, it selects a hospital that can accommodate allergies. The hospital selection unit also takes into account the user's past medical history and allergy information, and the generating AI selects the most appropriate hospital. For example, it selects a medical institution that can accommodate a specific allergy. This allows a more appropriate hospital to be selected by taking into account the user's past medical history and allergy information.

[0040] The hospital selection unit can weight multiple selection criteria and select a hospital based on the user's priorities. For example, the generation AI weights multiple selection criteria and selects a hospital based on the user's priorities. For example, it adjusts the weighting of distance, cost, and waiting time. The hospital selection unit also weights multiple selection criteria and selects a hospital based on the user's priorities. For example, it sets a high importance for the contents of the examination. The hospital selection unit also weights multiple selection criteria and selects a hospital based on the user's priorities. For example, it places emphasis on convenient transportation. In this way, by weighting multiple selection criteria, it is possible to select the optimal hospital based on the user's priorities.

[0041] The hospital selection unit can present multiple alternatives to the selected hospital, allowing the user to choose. For example, the hospital selection unit presents multiple alternatives to the hospital selected by the generation AI, allowing the user to choose. For example, it presents three candidate hospitals. The hospital selection unit also presents multiple alternatives to the hospital selected by the generation AI, allowing the user to choose. For example, it presents hospitals with different distances, costs, and waiting times. The hospital selection unit also presents multiple alternatives to the hospital selected by the generation AI, allowing the user to choose. For example, it presents hospitals with different examination contents or transportation methods. In this way, by presenting multiple alternatives to the selected hospital, the user can select the optimal hospital.

[0042] The hospital selection unit can add a function to incorporate the opinions of the user's family or friends when selecting a hospital. For example, the hospital selection unit adds a function to incorporate the opinions of the user's family and friends when selecting a hospital. For example, hospitals recommended by family and friends are taken into consideration. The hospital selection unit also adds a function to incorporate the opinions of the user's family and friends, and the generation AI selects a hospital based on that information. For example, it refers to reviews from family and friends. The hospital selection unit also adds a function to incorporate the opinions of the user's family and friends when selecting a hospital. For example, it prioritizes the selection of hospitals that family and friends have used in the past. In this way, by incorporating the opinions of the user's family and friends, a more appropriate hospital can be selected.

[0043] The guidance unit can add surrounding traffic information or parking information to the hospital guidance, making access easier. For example, the guidance unit adds surrounding traffic information to the hospital guidance, and the generating AI provides guidance based on that information. For example, it provides information on the nearest station or bus stop. The guidance unit also adds parking information to the hospital guidance, and the generating AI provides guidance based on that information. For example, it provides information on the locations and fees of parking lots around the hospital. The guidance unit also adds surrounding traffic information and parking information to the hospital guidance, and the generating AI provides guidance based on that information. For example, it provides options for transportation. In this way, adding surrounding traffic information and parking information makes it easier for users to access the hospital.

[0044] The guidance unit can include the consultation flow or a list of necessary items in the hospital guidance to support the user's preparations. For example, the guidance unit adds the consultation flow to the hospital guidance, and the generation AI provides guidance based on that information. For example, it explains the steps from reception to the consultation. The guidance unit also adds a list of necessary items to the hospital guidance, and the generation AI provides guidance based on that information. For example, it encourages the user to bring their health insurance card or patient card. The guidance unit also adds the consultation flow and a list of necessary items to the hospital guidance, and the generation AI provides guidance based on that information. For example, it supports preparations before the consultation. This can support the user's preparations by including the consultation flow and a list of necessary items.

[0045] The guidance unit provides hospital information in multiple languages, making it possible to accommodate foreign users as well. For example, the guidance unit provides hospital information in multiple languages, and the generation AI provides guidance based on that information. For example, guidance is provided in English, Chinese, Spanish, etc. The guidance unit also provides hospital information in multiple languages ​​to accommodate foreign users as well. For example, guidance is provided in major languages. The guidance unit also provides hospital information in multiple languages, and the generation AI provides guidance based on that information. For example, guidance is provided in a way that is easy for foreign users to understand. In this way, guidance can be provided in multiple languages, making it possible to accommodate foreign users as well.

[0046] The guidance unit adds information about nearby restaurants or accommodations to the hospital guidance, making it possible to accommodate users from far away. For example, the guidance unit adds information about nearby restaurants to the hospital guidance, and the generation AI provides guidance based on that information. For example, it provides information about restaurants and cafes near the hospital. The guidance unit also adds information about accommodations to the hospital guidance, and the generation AI provides guidance based on that information. For example, it provides information about hotels and accommodations around the hospital. The guidance unit also adds information about nearby restaurants and accommodations to the hospital guidance, and the generation AI provides guidance based on that information. For example, it provides guidance that is easy for users from far away to use. In this way, by adding information about nearby restaurants and accommodations, it is possible to accommodate users from far away.

[0047] The system can optimally allocate medical resources to each region and provide equal medical services to the entire population. For example, the system optimally allocates medical resources to each region and provides equal medical services to the entire population. For example, it optimizes the placement of medical institutions. The system also builds a system that optimally allocates medical resources to each region and provides equal medical services to the entire population. For example, it adjusts the supply and demand of medical resources. The system also optimally allocates medical resources to each region and provides equal medical services to the entire population. For example, it monitors the utilization status of medical institutions in real time. This makes it possible to optimally allocate medical resources to each region and provide equal medical services to the entire population.

[0048] The system can anonymously collect health data of users and monitor the health status of a local area. For example, the system anonymously collects health data of users and monitors the health status of a local area. For example, the system analyzes local health trends based on the health data. The system also anonymously collects health data of users and monitors the health status of a local area. For example, it monitors the epidemic status of infectious diseases. The system also anonymously collects health data of users and monitors the health status of a local area. For example, it suggests preventive measures based on the health data. In this way, the system can monitor the health status of a local area by anonymously collecting health data of users.

[0049] The system can link with other public services to support comprehensive emergency response. For example, the system links with emergency services to support comprehensive emergency response. For example, it can quickly arrange for an ambulance. The system also links with fire services to support comprehensive emergency response. For example, it can provide evacuation instructions in the event of a fire. The system also links with other public services to support comprehensive emergency response. For example, it can provide evacuation shelter information in the event of a disaster. In this way, by linking with other public services, it can support comprehensive emergency response.

[0050] The system can cooperate with schools or companies to support group health management. For example, the system cooperates with schools to support group health management. For example, health checkup data is shared within the school. The system also cooperates with companies to support group health management. For example, health management programs are provided within the company. The system also cooperates with schools or companies to support group health management. For example, preventive measures for group infection are proposed. In this way, by cooperating with schools or companies, group health management can be supported.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The user information collection unit collects the user's past health data or lifestyle information, enabling more accurate hospital selection. For example, the unit references the user's past medical examination history and prescription history. It also considers the user's dietary and exercise habits. It also considers the user's sleep patterns and stress levels. This allows for a more appropriate hospital selection by taking into account the user's past health data and lifestyle information.

[0053] The user information collection unit can generate additional questions in real time based on the user's answers and collect more detailed information. For example, based on the user's answers, it may ask questions such as "What specific symptoms do you have?" or "Have you had any recent changes in your physical condition?" or "Have you had similar symptoms in the past?" This allows it to collect more detailed information based on the user's answers and select a more appropriate hospital.

[0054] The user information collection unit can provide health advice or preventative measures based on the information entered by the user. For example, the generation AI can give advice such as "Make sure you drink plenty of water." It can also suggest preventative measures such as "Wash your hands thoroughly." It can also give advice such as "Make sure you get enough sleep." This allows the system to provide health advice and preventative measures to users, thereby supporting their health management.

[0055] The user information collection unit can link the information entered by the user with other health-related apps to support comprehensive health management. For example, it can link with a fitness app to share exercise data, or with a diet management app to share diet data, or with a sleep management app to share sleep data. In this way, by linking the user's health information with other apps, comprehensive health management is possible.

[0056] The hospital database reference unit adds the specialties and ratings of doctors at each hospital to the hospital database, allowing for more appropriate hospital selection. For example, it selects hospitals with cardiologists. It also selects hospitals with doctors who are highly rated by patients. It also selects hospitals with many specialists. This allows for more appropriate hospital selection by taking into account the specialties and ratings of doctors at each hospital.

[0057] The hospital database reference unit reflects the congestion status or reservation status of hospitals in real time, thereby shortening waiting times. For example, it selects a hospital that is not crowded. It also selects a hospital where it is easy to make an appointment. It also selects a hospital with a short waiting time. In this way, by reflecting the congestion status and reservation status of hospitals in real time, it is possible to shorten waiting times.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The user information collection unit uses the generation AI to collect necessary information from the user. For example, the generation AI asks the user questions such as "How is your current health condition?", "What symptoms do you have?", "How far from your home would be good?", "What means of transportation will you use?", "What kind of consultation will you receive?", "What is the maximum cost?", and "How long can you tolerate waiting?", and collects the user's answers. Step 2: The hospital database reference unit references the hospital database based on the user information collected by the user information collection unit. For example, the hospital database contains information such as the location of each hospital, medical treatment details, costs, waiting times, and transportation options, and the generation AI searches for the most suitable hospital based on this information. Step 3: The hospital selection unit selects the most suitable hospital based on the information referenced by the hospital database reference unit. For example, the generation AI selects a hospital that is close to the patient's home, has easy access to transportation, provides appropriate medical care, is within budget, and has a short waiting time. Step 4: The guidance unit provides the user with information about the hospital selected by the hospital selection unit. For example, the generation AI might provide guidance in the form of, "The best hospital is XX Hospital. The address is XX City, XX Town, XX Chome, No. XX. The medical examination will be XX and the cost will be XX yen. The waiting time will be approximately XX minutes. As for transportation, it is XX minutes on foot from XX Station."

[0060] (Example 2) The hospital selection system according to an embodiment of the present invention is a system in which a generative AI interactively selects and guides the user to the most suitable hospital when a person who does not usually go to the hospital becomes ill and needs to go to the hospital. This allows even people who do not usually go to the hospital to receive appropriate medical care when they are ill.

[0061] A hospital selection system according to an embodiment includes a user information collection unit, a hospital database reference unit, a hospital selection unit, and a guidance unit. The user information collection unit uses a generation AI to collect necessary information from a user. For example, the generation AI asks the user questions such as, "How is your current health condition?", "What symptoms do you have?", "How far from your home is preferable?", "What means of transportation will you use?", "What medical examination will be performed?", "What is the maximum cost?", and "How long of a waiting time are you willing to accept?", and collects the user's answers. The hospital database reference unit references a hospital database based on the user information collected by the user information collection unit. For example, the hospital database includes information such as the location, medical examination contents, costs, waiting times, and transportation methods of each hospital, and the generation AI searches for the optimal hospital based on this information. The hospital selection unit selects the optimal hospital based on the information referenced by the hospital database reference unit. For example, the generation AI selects a hospital that is close to the user's home, has easy access to transportation, provides appropriate medical examination contents, is within budget, and has a short waiting time. The guidance unit provides the user with information about the hospital selected by the hospital selection unit. For example, the generation AI may provide guidance in the form of, "The best hospital is XX Hospital. The address is XX, Chome, XX-cho, XX-shi. The medical examination will be XX, and the cost will be XX yen. The waiting time will be approximately XX minutes. As for transportation, it is XX minutes on foot from XX Station." In this way, the hospital selection system according to the embodiment allows even people who do not usually go to the hospital to receive appropriate medical care when they feel unwell.

[0062] The user information collection unit can infer emotions from the user's tone of voice or manner of speaking and engage in dialogue to reduce stress or anxiety. In the user information collection unit, for example, the generation AI analyzes the user's tone of voice and manner of speaking to infer emotions. For example, if the user is nervous, the generation AI engages in dialogue to help the user relax. The user information collection unit can also infer emotions from the user's tone of voice and manner of speaking and engage in dialogue to reduce stress or anxiety. For example, if the user is feeling anxious, the generation AI speaks words that give a sense of security. In the user information collection unit, the generation AI analyzes the user's tone of voice and manner of speaking to infer emotions. For example, if the user is impatient, the generation AI engages in dialogue to help the user calm down. This reduces the user's stress and anxiety and allows information to be provided in a relaxed state.

[0063] The user information collection unit collects the user's past health data or lifestyle information, enabling more accurate hospital selection. The user information collection unit, for example, collects the user's past health data, and the generation AI selects a hospital based on that data. For example, it references past medical examination history and medication prescription history. The user information collection unit also collects the user's lifestyle information, and the generation AI selects a hospital based on that information. For example, it takes into account the user's eating habits and exercise habits. The user information collection unit also collects the user's past health data and lifestyle information, and the generation AI selects a hospital based on that data. For example, it takes into account the user's sleeping patterns and stress level. This allows for a more appropriate hospital to be selected by taking into account the user's past health data and lifestyle information.

[0064] The user information collection unit can generate additional questions in real time based on the user's answers and collect detailed information. In the user information collection unit, for example, the generation AI generates additional questions in real time based on the user's answers. For example, it asks a question such as, "What specific symptoms do you have?" In addition, the user information collection unit can generate additional questions in real time based on the user's answers and collect detailed information. For example, it asks a question such as, "Have you noticed any changes in your health recently?" In addition, the user information collection unit can generate additional questions in real time based on the user's answers. For example, it asks a question such as, "Have you had similar symptoms in the past?" This allows detailed information to be collected based on the user's answers, making it possible to select a more appropriate hospital.

[0065] The user information collection unit can provide health advice or preventive measures based on the information input by the user. For example, the user information collection unit causes the generation AI to provide health advice based on the information input by the user. For example, the unit may provide advice such as "Make sure to drink plenty of water." The user information collection unit also causes the generation AI to provide preventive measures based on the information input by the user. For example, the unit may suggest preventive measures such as "Wash your hands thoroughly." The user information collection unit also causes the generation AI to provide health advice and preventive measures based on the information input by the user. For example, the unit may suggest advice such as "Make sure to get enough sleep." In this way, the generation AI can provide health advice and preventive measures to the user, thereby assisting them in managing their health.

[0066] The user information collection unit can link the information input by the user with other health-related apps to support comprehensive health management. The user information collection unit, for example, links the information input by the user with other health-related apps to support comprehensive health management. For example, it links with a fitness app to share exercise data. The user information collection unit also links the information input by the user with other health-related apps to support comprehensive health management. For example, it links with a diet management app to share diet data. The user information collection unit also links the information input by the user with other health-related apps to support comprehensive health management. For example, it links with a sleep management app to share sleep data. This makes it possible to link the user's health information with other apps to support comprehensive health management.

[0067] The user information collection unit uses the emotion estimation function to analyze the emotion of the user when entering input in real time, and can conduct a dialogue that elicits positive emotions. The user information collection unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering input in real time. For example, if the user is feeling anxious, the generation AI conducts a dialogue that gives a sense of security. The user information collection unit also uses the emotion estimation function to analyze the emotion of the user when entering input in real time, and conducts a dialogue that elicits positive emotions. For example, if the user is nervous, the generation AI conducts a dialogue that relaxes the user. The user information collection unit also uses the emotion estimation function to analyze the emotion of the user when entering input in real time. For example, if the user is impatient, the generation AI conducts a dialogue that calms the user. In this way, by analyzing the user's emotions in real time and eliciting positive emotions, the user's stress can be reduced and information can be provided in a relaxed state.

[0068] The hospital database reference unit adds the specialties or ratings of doctors at each hospital to the hospital database, allowing for more appropriate hospital selection. For example, the hospital database reference unit adds the specialties of doctors at each hospital to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a cardiologist. The hospital database reference unit also adds the ratings of doctors at each hospital to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with doctors who are highly rated by patients. The hospital database reference unit also adds the specialties and ratings of doctors at each hospital to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with many specialists. This allows for a more appropriate hospital to be selected by taking into account the specialties and ratings of doctors at each hospital.

[0069] The hospital database reference unit reflects the hospital's congestion status or reservation status in real time, thereby reducing waiting times. The hospital database reference unit, for example, reflects the hospital's congestion status in the hospital database in real time, and the generation AI selects a hospital based on that information. For example, it selects a hospital that is not congested. The hospital database reference unit also reflects the hospital's reservation status in the hospital database in real time, and the generation AI selects a hospital based on that information. For example, it selects a hospital where it is easy to make an appointment. The hospital database reference unit also reflects the hospital's congestion status and reservation status in the hospital database in real time, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a short waiting time. In this way, waiting times can be reduced by reflecting the hospital's congestion status and reservation status in real time.

[0070] The hospital database reference unit can add information about hospital facilities or the latest medical technology to the database and add it to the selection criteria. For example, the hospital database reference unit adds hospital facility information to the hospital database, and the generating AI selects a hospital based on that information. For example, it selects a hospital with the latest MRI equipment. The hospital database reference unit also adds information about the latest medical technology to the hospital database, and the generating AI selects a hospital based on that information. For example, it selects a hospital with the latest surgical techniques. The hospital database reference unit also adds information about hospital facilities and the latest medical technology to the hospital database, and the generating AI selects a hospital based on that information. For example, it selects a hospital that offers the latest treatment methods. This makes it possible to select a more appropriate hospital by taking into account information about hospital facilities and the latest medical technology.

[0071] The hospital database reference unit can link the hospital database with other medical institutions to provide comprehensive medical services. For example, the hospital database reference unit links the hospital database with a clinic, and the generation AI selects a hospital based on that information. For example, after an initial consultation at a clinic, the generation AI can refer the patient to an appropriate hospital. The hospital database reference unit also links the hospital database with a pharmacy, and the generation AI selects a hospital based on that information. For example, the generation AI can select a hospital based on medication prescription information at a pharmacy. The hospital database reference unit also links the hospital database with other medical institutions, and the generation AI can select a hospital based on that information. For example, information from multiple medical institutions can be integrated to provide comprehensive medical services. This allows the hospital database to be linked with other medical institutions to provide comprehensive medical services.

[0072] The hospital database reference unit can add user reviews or feedback to the hospital database to use as a reference for selection. For example, the hospital database reference unit adds user reviews to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a high review rating. The hospital database reference unit also adds user feedback to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital based on the feedback content. The hospital database reference unit also adds user reviews and feedback to the hospital database, and the generation AI selects a hospital based on that information. For example, it selects a hospital with a large number of positive reviews. This makes it possible to select a more appropriate hospital by taking user reviews and feedback into consideration.

[0073] The hospital database reference unit can use the emotion estimation function to perform emotion analysis of hospital reviews or feedback and preferentially display hospitals with positive ratings. The hospital database reference unit, for example, uses the emotion estimation function to perform emotion analysis of hospital reviews and feedback. For example, hospitals with many reviews containing positive emotions are preferentially displayed. The hospital database reference unit also performs emotion analysis of hospital reviews and feedback and preferentially display hospitals with positive ratings. For example, it selects hospitals with high emotion scores. The hospital database reference unit also uses the emotion estimation function to perform emotion analysis of hospital reviews and feedback and preferentially display hospitals with positive ratings. For example, it selects hospitals based on the user's emotional response. In this way, by performing emotion analysis, hospitals with positive ratings are preferentially displayed, making it possible to select the optimal hospital for the user.

[0074] The hospital selection unit can select a hospital that will reduce stress by taking into account the user's emotional state. For example, the generation AI in the hospital selection unit analyzes the user's emotional state and selects a hospital that will reduce stress. For example, it selects a hospital with a relaxing environment. The hospital selection unit also considers the user's emotional state by taking into account the user's emotional state by taking into account the generation AI and selects a hospital that will reduce stress. For example, it selects a hospital with a short waiting time. The hospital selection unit also analyzes the user's emotional state by taking into account the user's emotional state by taking into account the generation AI and selects a hospital that will reduce stress. For example, it selects a hospital with friendly staff. In this way, by taking the user's emotional state into account, stress can be reduced and a more appropriate hospital can be selected.

[0075] The hospital selection unit can select the most appropriate hospital by taking into account the user's past medical history or allergy information. The hospital selection unit, for example, takes into account the user's past medical history, and the generating AI selects the most appropriate hospital. For example, it selects a hospital where the user has received treatment in the past. The hospital selection unit also takes into account the user's allergy information, and the generating AI selects the most appropriate hospital. For example, it selects a hospital that can accommodate allergies. The hospital selection unit also takes into account the user's past medical history and allergy information, and the generating AI selects the most appropriate hospital. For example, it selects a medical institution that can accommodate a specific allergy. This allows a more appropriate hospital to be selected by taking into account the user's past medical history and allergy information.

[0076] The hospital selection unit can weight multiple selection criteria and select a hospital based on the user's priorities. For example, the generation AI weights multiple selection criteria and selects a hospital based on the user's priorities. For example, it adjusts the weighting of distance, cost, and waiting time. The hospital selection unit also weights multiple selection criteria and selects a hospital based on the user's priorities. For example, it sets a high importance for the contents of the examination. The hospital selection unit also weights multiple selection criteria and selects a hospital based on the user's priorities. For example, it places emphasis on convenient transportation. In this way, by weighting multiple selection criteria, it is possible to select the optimal hospital based on the user's priorities.

[0077] The hospital selection unit can present multiple alternatives to the selected hospital, allowing the user to choose. For example, the hospital selection unit presents multiple alternatives to the hospital selected by the generation AI, allowing the user to choose. For example, it presents three candidate hospitals. The hospital selection unit also presents multiple alternatives to the hospital selected by the generation AI, allowing the user to choose. For example, it presents hospitals with different distances, costs, and waiting times. The hospital selection unit also presents multiple alternatives to the hospital selected by the generation AI, allowing the user to choose. For example, it presents hospitals with different examination contents or transportation methods. In this way, by presenting multiple alternatives to the selected hospital, the user can select the optimal hospital.

[0078] The hospital selection unit can add a function to incorporate the opinions of the user's family or friends when selecting a hospital. For example, the hospital selection unit adds a function to incorporate the opinions of the user's family and friends when selecting a hospital. For example, hospitals recommended by family and friends are taken into consideration. The hospital selection unit also adds a function to incorporate the opinions of the user's family and friends, and the generation AI selects a hospital based on that information. For example, it refers to reviews from family and friends. The hospital selection unit also adds a function to incorporate the opinions of the user's family and friends when selecting a hospital. For example, it prioritizes the selection of hospitals that family and friends have used in the past. In this way, by incorporating the opinions of the user's family and friends, a more appropriate hospital can be selected.

[0079] The hospital selection unit can use the emotion estimation function to select a hospital where the user can feel most comfortable. The hospital selection unit, for example, uses the emotion estimation function to select a hospital where the user can feel most comfortable. For example, the hospital is selected based on the user's emotion score. The hospital selection unit also uses the emotion estimation function to select a hospital where the user can feel most comfortable. For example, the hospital is selected by analyzing the user's emotional state. The hospital selection unit also uses the emotion estimation function to select a hospital where the user can feel most comfortable. For example, the hospital is selected based on the user's emotional response. In this way, the emotion estimation function can be used to select a hospital where the user can feel most comfortable.

[0080] The guidance unit can provide guidance that gives a sense of security by taking into account the emotional state of the user. For example, the generation AI of the guidance unit analyzes the emotional state of the user and provides guidance that gives a sense of security. For example, it provides hospital guidance using gentle language. The guidance unit also considers the emotional state of the user and provides guidance that gives a sense of security by taking into account the emotional state of the user. For example, it provides guidance that helps the user to relax. The guidance unit also analyzes the emotional state of the user by taking into account the emotional state of the user. For example, it strives to provide a friendly response. This makes it possible to provide guidance that gives a sense of security by taking into account the emotional state of the user.

[0081] The guidance unit can add surrounding traffic information or parking information to the hospital guidance, making access easier. For example, the guidance unit adds surrounding traffic information to the hospital guidance, and the generating AI provides guidance based on that information. For example, it provides information on the nearest station or bus stop. The guidance unit also adds parking information to the hospital guidance, and the generating AI provides guidance based on that information. For example, it provides information on the locations and fees of parking lots around the hospital. The guidance unit also adds surrounding traffic information and parking information to the hospital guidance, and the generating AI provides guidance based on that information. For example, it provides options for transportation. In this way, adding surrounding traffic information and parking information makes it easier for users to access the hospital.

[0082] The guidance unit can include the consultation flow or a list of necessary items in the hospital guidance to support the user's preparations. For example, the guidance unit adds the consultation flow to the hospital guidance, and the generation AI provides guidance based on that information. For example, it explains the steps from reception to the consultation. The guidance unit also adds a list of necessary items to the hospital guidance, and the generation AI provides guidance based on that information. For example, it encourages the user to bring their health insurance card or patient card. The guidance unit also adds the consultation flow and a list of necessary items to the hospital guidance, and the generation AI provides guidance based on that information. For example, it supports preparations before the consultation. This can support the user's preparations by including the consultation flow and a list of necessary items.

[0083] The guidance unit provides hospital information in multiple languages, making it possible to accommodate foreign users as well. For example, the guidance unit provides hospital information in multiple languages, and the generation AI provides guidance based on that information. For example, guidance is provided in English, Chinese, Spanish, etc. The guidance unit also provides hospital information in multiple languages ​​to accommodate foreign users as well. For example, guidance is provided in major languages. The guidance unit also provides hospital information in multiple languages, and the generation AI provides guidance based on that information. For example, guidance is provided in a way that is easy for foreign users to understand. In this way, guidance can be provided in multiple languages, making it possible to accommodate foreign users as well.

[0084] The guidance unit adds information about nearby restaurants or accommodations to the hospital guidance, making it possible to accommodate users from far away. For example, the guidance unit adds information about nearby restaurants to the hospital guidance, and the generation AI provides guidance based on that information. For example, it provides information about restaurants and cafes near the hospital. The guidance unit also adds information about accommodations to the hospital guidance, and the generation AI provides guidance based on that information. For example, it provides information about hotels and accommodations around the hospital. The guidance unit also adds information about nearby restaurants and accommodations to the hospital guidance, and the generation AI provides guidance based on that information. For example, it provides guidance that is easy for users from far away to use. In this way, by adding information about nearby restaurants and accommodations, it is possible to accommodate users from far away.

[0085] The guidance unit can use the emotion estimation function to provide guidance in a format that is most easily understandable to the user. The guidance unit, for example, uses the emotion estimation function to provide guidance in a format that is most easily understandable to the user. For example, the guidance unit analyzes the user's emotional state and provides guidance using appropriate language. The guidance unit also uses the emotion estimation function to provide guidance in a format that is most easily understandable to the user. For example, the guidance content is adjusted based on the user's emotional response. The guidance unit also uses the emotion estimation function to provide guidance in a format that is most easily understandable to the user. For example, the guidance method is selected based on the user's emotion score. In this way, by using the emotion estimation function, guidance can be provided in a format that is most easily understandable to the user.

[0086] The system takes into account the user's emotional state and can select the optimal hospital for the entire population individually. For example, the system uses a generating AI to analyze the user's emotional state and select the optimal hospital for the entire population individually. For example, it selects a hospital based on an emotional score. The system also takes into account the user's emotional state and uses a generating AI to select the optimal hospital for the entire population individually. For example, it selects a hospital that gives a sense of security. The system also uses a generating AI to analyze the user's emotional state and select the optimal hospital for the entire population individually. For example, it selects a hospital that reduces stress. In this way, by taking into account the user's emotional state, it is possible to select the optimal hospital for the entire population individually.

[0087] The system can optimally allocate medical resources to each region and provide equal medical services to the entire population. For example, the system optimally allocates medical resources to each region and provides equal medical services to the entire population. For example, it optimizes the placement of medical institutions. The system also builds a system that optimally allocates medical resources to each region and provides equal medical services to the entire population. For example, it adjusts the supply and demand of medical resources. The system also optimally allocates medical resources to each region and provides equal medical services to the entire population. For example, it monitors the utilization status of medical institutions in real time. This makes it possible to optimally allocate medical resources to each region and provide equal medical services to the entire population.

[0088] The system can anonymously collect health data of users and monitor the health status of a local area. For example, the system anonymously collects health data of users and monitors the health status of a local area. For example, the system analyzes local health trends based on the health data. The system also anonymously collects health data of users and monitors the health status of a local area. For example, it monitors the epidemic status of infectious diseases. The system also anonymously collects health data of users and monitors the health status of a local area. For example, it suggests preventive measures based on the health data. In this way, the system can monitor the health status of a local area by anonymously collecting health data of users.

[0089] The system can link with other public services to support comprehensive emergency response. For example, the system links with emergency services to support comprehensive emergency response. For example, it can quickly arrange for an ambulance. The system also links with fire services to support comprehensive emergency response. For example, it can provide evacuation instructions in the event of a fire. The system also links with other public services to support comprehensive emergency response. For example, it can provide evacuation shelter information in the event of a disaster. In this way, by linking with other public services, it can support comprehensive emergency response.

[0090] The system can cooperate with schools or companies to support group health management. For example, the system cooperates with schools to support group health management. For example, health checkup data is shared within the school. The system also cooperates with companies to support group health management. For example, health management programs are provided within the company. The system also cooperates with schools or companies to support group health management. For example, preventive measures for group infection are proposed. In this way, by cooperating with schools or companies, group health management can be supported.

[0091] The system can use the emotion estimation function to monitor the emotional state of the entire population and improve the quality of medical services. For example, the system can use the emotion estimation function to monitor the emotional state of the entire population and improve the quality of medical services. For example, the system can adjust medical services based on the emotion data. The system can also monitor the emotional state of the entire population and improve the quality of medical services using the emotion estimation function. For example, the system can improve the response of medical staff based on the emotion scores. The system can also monitor the emotional state of the entire population and improve the quality of medical services using the emotion estimation function. For example, the system can improve the environment of medical facilities based on the emotion data. In this way, the system can use the emotion estimation function to monitor the emotional state of the entire population and improve the quality of medical services.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The user information collection unit can infer emotions from the user's tone of voice or speaking style and engage in dialogue to reduce stress or anxiety. For example, the generation AI analyzes the user's tone of voice and speaking style to infer emotions. If the user is nervous, the generation AI engages in dialogue to help them relax. If the user is feeling anxious, the generation AI speaks words to give them a sense of security. Furthermore, if the user is feeling impatient, the generation AI engages in dialogue to calm them down. This reduces the user's stress and anxiety and allows information to be provided in a relaxed state.

[0094] The user information collection unit collects the user's past health data or lifestyle information, enabling more accurate hospital selection. For example, the unit references the user's past medical examination history and prescription history. It also considers the user's dietary and exercise habits. It also considers the user's sleep patterns and stress levels. This allows for a more appropriate hospital selection by taking into account the user's past health data and lifestyle information.

[0095] The user information collection unit can generate additional questions in real time based on the user's answers and collect more detailed information. For example, based on the user's answers, it may ask questions such as "What specific symptoms do you have?" or "Have you had any recent changes in your physical condition?" or "Have you had similar symptoms in the past?" This allows it to collect more detailed information based on the user's answers and select a more appropriate hospital.

[0096] The user information collection unit can provide health advice or preventative measures based on the information entered by the user. For example, the generation AI can give advice such as "Make sure you drink plenty of water." It can also suggest preventative measures such as "Wash your hands thoroughly." It can also give advice such as "Make sure you get enough sleep." This allows the system to provide health advice and preventative measures to users, thereby supporting their health management.

[0097] The user information collection unit can link the information entered by the user with other health-related apps to support comprehensive health management. For example, it can link with a fitness app to share exercise data, or with a diet management app to share diet data, or with a sleep management app to share sleep data. In this way, by linking the user's health information with other apps, comprehensive health management is possible.

[0098] The user information collection unit uses the emotion estimation function to analyze the emotions the user is inputting in real time and can conduct dialogue that elicits positive emotions. For example, if the user is feeling anxious, the generation AI will conduct dialogue that gives a sense of security. If the user is nervous, the generation AI will conduct dialogue that relaxes the user. Furthermore, if the user is impatient, the generation AI will conduct dialogue that calms the user. In this way, by analyzing the user's emotions in real time and eliciting positive emotions, the system can reduce the user's stress and provide information in a relaxed state.

[0099] The hospital database reference unit adds the specialties and ratings of doctors at each hospital to the hospital database, allowing for more appropriate hospital selection. For example, it selects hospitals with cardiologists. It also selects hospitals with doctors who are highly rated by patients. It also selects hospitals with many specialists. This allows for more appropriate hospital selection by taking into account the specialties and ratings of doctors at each hospital.

[0100] The hospital database reference unit reflects the congestion status or reservation status of hospitals in real time, thereby shortening waiting times. For example, it selects a hospital that is not crowded. It also selects a hospital where it is easy to make an appointment. It also selects a hospital with a short waiting time. In this way, by reflecting the congestion status and reservation status of hospitals in real time, it is possible to shorten waiting times.

[0101] The hospital database reference unit uses the emotion estimation function to perform emotion analysis of hospital reviews or feedback and prioritize displaying hospitals with positive evaluations. For example, it prioritizes displaying hospitals with reviews with many positive emotions. It also selects hospitals with high emotion scores. It also selects hospitals based on the user's emotional response. In this way, emotion analysis allows it to prioritize displaying hospitals with positive evaluations and select the most suitable hospital for the user.

[0102] The hospital selection unit can select a hospital that takes into account the user's emotional state and reduces stress. For example, the generation AI analyzes the user's emotional state and selects a hospital with a relaxing environment. It also selects a hospital with short waiting times and friendly staff. In this way, by taking the user's emotional state into consideration, stress can be reduced and a more appropriate hospital can be selected.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The user information collection unit uses the generation AI to collect necessary information from the user. For example, the generation AI asks the user questions such as "How is your current health condition?", "What symptoms do you have?", "How far from your home would be good?", "What means of transportation will you use?", "What kind of consultation will you receive?", "What is the maximum cost?", and "How long can you tolerate waiting?", and collects the user's answers. Step 2: The hospital database reference unit references the hospital database based on the user information collected by the user information collection unit. For example, the hospital database contains information such as the location of each hospital, medical treatment details, costs, waiting times, and transportation options, and the generation AI searches for the most suitable hospital based on this information. Step 3: The hospital selection unit selects the most suitable hospital based on the information referenced by the hospital database reference unit. For example, the generation AI selects a hospital that is close to the patient's home, has easy access to transportation, provides appropriate medical care, is within budget, and has a short waiting time. Step 4: The guidance unit provides the user with information about the hospital selected by the hospital selection unit. For example, the generation AI might provide guidance in the form of, "The best hospital is XX Hospital. The address is XX City, XX Town, XX Chome, No. XX. The medical examination will be XX and the cost will be XX yen. The waiting time will be approximately XX minutes. As for transportation, it is XX minutes on foot from XX Station."

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information collection unit that uses a generation AI to collect necessary information from users; a hospital database reference unit that references a hospital database based on the user information collected by the user information collection unit; a hospital selection unit that selects an optimal hospital based on the information referenced by the hospital database reference unit; a guidance unit that provides the user with information about the hospital selected by the hospital selection unit. A system characterized by:

2. The hospital database reference unit Adding the specialties or ratings of doctors at each hospital to the hospital database for better hospital selection 2. The system of claim 1.

3. The hospital selection unit Select the most suitable hospital based on the user's past medical history or allergy information 2. The system of claim 1.

4. The guide unit is Adding local traffic and parking information to hospital guides to make access easier 2. The system of claim 1.

5. The user information collection unit Inferring emotions from the user's tone of voice or speaking style and engaging in dialogue to reduce stress or anxiety 2. The system of claim 1.

Images