system

Abstract

We provide the system. [Solution] A reservation management system that monitors patient appointment status in real time and adjusts the timing of visits based on the progress of treatment, A diagnostic support system that analyzes patient-entered medical information to determine the urgency of a situation, A waiting time management system that predicts waiting times based on the progress of consultations within the hospital and provides notifications including options for waiting outside, A follow-up method that provides follow-up information after consultation and automates the next appointment, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The length of waiting time for medical treatment in the medical field has become a significant burden on patients and medical staff. Especially in an aging society, efficient medical services are required, and it is necessary to eliminate patient dissatisfaction and excessive workload of medical staff caused by waiting. Also, it is important to appropriately judge the urgency of patients and promptly provide necessary medical support.

Means for Solving the Problems

[0005] This invention provides a reservation management system that monitors patients' appointment status in real time and adjusts the timing of their visits according to the progress of their treatment. It also includes a diagnostic support system that determines the urgency of a patient's condition based on information gathered from their medical interview. Furthermore, a waiting time management system predicts waiting times based on the progress of the examination and provides notifications, including options for waiting outside the clinic. In addition, it includes a follow-up system that provides necessary follow-up information after the consultation and automates the scheduling of the next appointment. In this way, the system aims to improve the efficiency of medical care delivery and reduce the burden on patients.

[0006] A "reservation management system" is a mechanism that monitors patients' appointment status in real time and optimizes the timing of their visits according to the progress of their treatment.

[0007] A "diagnostic support tool" is a system that analyzes patient interview information, determines the urgency of the situation, and then proposes appropriate medical actions.

[0008] A "waiting time management system" is a mechanism that predicts waiting times based on the progress of consultations within the hospital and notifies patients of their expected waiting time and external waiting options based on that prediction.

[0009] A "follow-up system" is a mechanism that supports patients' medical management by providing necessary information after a medical consultation and automatically scheduling the next appointment.

[0010] "Real-time" refers to immediately grasping the current situation and processing and transmitting necessary information immediately. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides an advanced information system for improving the efficiency of medical treatment processes in hospitals. In this embodiment, the system primarily operates around three components: a server, a terminal, and a user, each playing its own role.

[0033] First, the user makes a reservation via their device. The server receives this reservation, monitors the reservation status in real time using a reservation management system, and stores it in a database. For example, if a patient requests an internal medicine appointment for a specific date and time, the server checks the information and updates the schedule accordingly.

[0034] As the appointment date approaches, the server sends a reminder notification to the user's terminal, prompting the user to confirm. The user receives the notification and can reconfirm the appointment if necessary.

[0035] In the online consultation conducted before visiting the clinic, users input their symptoms from their device and send them to the server. The server's diagnostic support system analyzes this data to determine the urgency of the situation. For example, if it is determined that the situation is not urgent, the device will be notified of what to do at home and, in some cases, a video call with a doctor may be recommended.

[0036] When a user arrives at the hospital, a waiting time management system is activated. The server retrieves the progress of consultations within the hospital from a database and estimates the waiting time. It then notifies the user's terminal of the estimated waiting time and provides options for leaving the hospital if a long wait is necessary. At this time, information such as nearby cafes may also be provided.

[0037] After the consultation, a follow-up system is activated. The server sends the consultation results and follow-up information to the terminal and automatically schedules the next appointment. The user can then review this and plan their next visit. This series of operations can reduce the burden on patients and significantly improve the operational efficiency of healthcare facilities.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user requests an appointment using a terminal. The user enters the desired medical department and date / time, and sends this information to the server via the terminal.

[0041] Step 2:

[0042] The server records the received reservation request in its database and checks the availability of the reservation using the reservation management system. If the reservation is confirmed, the server notifies the terminal of this information.

[0043] Step 3:

[0044] As the appointment date approaches, the server automatically generates a reminder notification. The server sends the reminder to the user's device, notifying them to reconfirm their appointment.

[0045] Step 4:

[0046] Users complete an online medical questionnaire. They input their symptoms via their device and send the questionnaire data to a server. The input is then analyzed by diagnostic support tools.

[0047] Step 5:

[0048] The server determines the urgency of the situation based on the medical questionnaire information. The server sends the determination result to the terminal and displays it to the user. It also provides guidance on how to handle the situation at home and how to schedule a video call with a doctor, as needed.

[0049] Step 6:

[0050] After the patient arrives at the hospital, the server monitors the progress of consultations within the hospital in real time and estimates the waiting time. The server uses a waiting time management system to notify the terminal of the predicted waiting time and the option of leaving the hospital.

[0051] Step 7:

[0052] Once the consultation is complete, the server processes the results using a follow-up system. The server automatically schedules the next appointment and sends that information to the terminal.

[0053] Step 8:

[0054] The device notifies the user of their next appointment information and detailed medical results, allowing them to review the follow-up plan. After receiving the notification, the user can prepare for their next appointment.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In modern healthcare settings, patient appointment management, optimization of waiting times for consultations, and post-consultation follow-up are often not carried out efficiently. This impairs patient convenience and reduces the operational efficiency of healthcare institutions. To solve these problems, it is necessary to automate these processes and provide more advanced information management tools.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes: appointment management means for monitoring patient appointment status in real time and adjusting the timing of patient visits based on information on the progress of medical care provision; diagnostic support means for analyzing patient-entered medical information and determining the urgency of the situation; and waiting time management means for predicting waiting times based on the progress of consultations within the medical institution and providing notifications, including options for waiting outside. This improves patient convenience and optimizes the operational efficiency of medical institutions.

[0060] A "reservation management system" is a function that receives users' reservation information, monitors it in real time, and adjusts the timing of their visits based on information about the progress of medical care provision.

[0061] A "diagnostic support tool" is a function that analyzes the medical information entered by the patient and determines the urgency of the symptoms based on that data.

[0062] A "waiting time management system" is a function that estimates waiting times based on the progress of consultations within a medical institution and provides notifications, including external waiting options, as needed.

[0063] "Follow-up services" refer to functions that provide follow-up information after medical treatment, automate the scheduling of the next appointment, and facilitate the patient's next visit.

[0064] A "reminder mechanism" is a function that sends a medical appointment reminder notification to the user's device and asks the user to confirm it.

[0065] "Communication methods" refers to functions that allow for visual communication with healthcare professionals, such as video calls, based on the patient's medical history data.

[0066] This invention is an advanced information system aimed at improving the efficiency of medical treatment processes in hospitals, and it primarily functions through three parties: a server, a terminal, and a user.

[0067] First, the user makes a hospital appointment using a device. This device is a computer such as a PC or smartphone, and requires an internet connection to access the hospital's appointment system. The appointment information entered by the user is sent to a server via the internet. The server receives this information and updates the schedule in real time using dedicated appointment management software. Specifically, this involves data processing such as storing appointment information in a database, calculating available time slots, and updating appointment status.

[0068] As the appointment date approaches, the server sends a reminder notification to the user's device. This notification is sent via email or app notification, prompting the user to confirm the appointment date, time, and location. Such a reminder system can utilize notification tools developed using Python, JavaScript (registered trademark), or other languages.

[0069] Users can also complete an online questionnaire before coming to the clinic. This is done via a dedicated questionnaire application that runs on the user's device. The user enters their symptoms and sends them to the server, which analyzes them using diagnostic support software to determine the urgency. If a serious case is detected, a video call system (e.g., Zoom or WebRTC) is activated using the communication method to immediately suggest a video call with a doctor.

[0070] When a user arrives at the hospital, the server retrieves consultation progress data from a database and analyzes it using waiting time management software. Based on this data, it predicts waiting times and notifies the user's terminal with information including options for leaving the hospital. The notification function can also be linked with a map application for user convenience.

[0071] After the consultation, the server uses a follow-up system to send the consultation results and next appointment information to the user. This automatically schedules the next appointment, and the user can review it and adjust the plan accordingly.

[0072] As a concrete example, the following is an example of a prompt sentence to be input to the generating AI model: "Please describe the procedure for updating the appointment status in the medical appointment system and sending a notification to the patient." By using this prompt, the effectiveness of the invention can be maximized, and the entire medical process can be carried out smoothly.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user enters their hospital appointment information using a terminal. They enter the necessary information (e.g., department, date and time, patient information) into the input form on the terminal and press the submit button to send the information to the server.

[0076] Step 2:

[0077] The server analyzes the reservation information received from the terminal. It accesses the database and checks availability by comparing it with the current reservation status. It adjusts reservations as needed and updates the database with new schedules. At this time, new reservation information is created in the database and the availability is updated.

[0078] Step 3:

[0079] Once the reservation is complete, the server sends confirmation information to the user's terminal. The user receives this confirmation notification and can reconfirm the reservation details. The notification includes reservation details (e.g., date and time, department, doctor's name).

[0080] Step 4:

[0081] As the appointment date approaches, the server sends a reminder notification to the user's device. The reminder is automatically generated at the specified date and time and sent to the user via email or app notification. Here, the notification information based on the schedule is output.

[0082] Step 5:

[0083] Before coming to the clinic, users complete an online medical questionnaire via their device. They enter their symptoms and health status into the questionnaire form and submit it to the server. Based on the input data, the server analyzes the symptoms using diagnostic support tools and determines the urgency of the situation. The analysis results are output directly and influence the next step in treatment.

[0084] Step 6:

[0085] Upon arrival at the hospital, the server estimates the waiting time based on the progress of the consultation. The server retrieves consultation progress data from the database and calculates the waiting time using a waiting time management system. The results are notified to the terminal, and suggestions for how to spend time outside the hospital (e.g., information on nearby facilities) are made as needed.

[0086] Step 7:

[0087] After the consultation, the server sends follow-up information to the user's terminal. It automatically generates and notifies the user of information including the consultation results, next appointment scheduling, and a self-follow-up guide. This allows the user to plan their next visit.

[0088] Through these steps, users can have a smoother consultation experience, and healthcare institutions can achieve more efficient operations.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In urban hospitals, the complexity of appointment management, which ensures patients receive appropriate medical care, is leading to decreased efficiency. Furthermore, patients are unable to effectively utilize their waiting time at hospitals, resulting in a lack of smooth medical delivery. These problems not only impair patient convenience but also reduce the operational efficiency of healthcare institutions. Solutions are needed to improve this situation.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes a reservation management means that monitors the patient's reservation status in real time and adjusts the timing of their visit based on the progress of their treatment; a diagnostic support means that analyzes the medical questionnaire information entered by the patient and determines the urgency of the situation; and a health care navigation means that centrally manages the hospital's reservation status and recommends the most suitable medical service to the patient. This enables the provision of efficient medical services to patients and allows for rapid responses both inside and outside the hospital.

[0094] "Patient appointment status" refers to information that shows the status of appointments made at a hospital based on the patient's preferred date and time for treatment.

[0095] "Real-time monitoring" is the process of instantly monitoring constantly changing information and always being aware of the latest status.

[0096] "Medical treatment progress information" refers to information that indicates the stage of treatment within the hospital.

[0097] "Adjusting the timing of hospital visits" refers to the management and settings required to ensure that patients visit the hospital at the appropriate time.

[0098] "Diagnostic support tools" are functions that analyze medical information collected from patients and assist in the diagnostic process.

[0099] "Assessing the severity of the situation" is the process of evaluating the severity and urgency of a patient's symptoms.

[0100] "Centralized management" refers to the comprehensive management and centralized processing of multiple data and pieces of information.

[0101] "Health Care Navigation" is a system that provides information and recommendations to help patients access the most appropriate medical services.

[0102] "Waiting time prediction" refers to predicting the waiting time before a consultation begins and providing this information to the patient.

[0103] "Off-site waiting options" refer to the option of providing patients with waiting locations or services available outside the hospital.

[0104] "Follow-up information" refers to information used to monitor the progress of treatment after a medical examination.

[0105] "Automated next appointment scheduling" refers to the system automatically scheduling the next appointment after the current consultation.

[0106] To implement this invention, a system is constructed in which a server, a terminal, and a user work together. The server requires dedicated software to monitor patient appointment status in real time and track the progress of medical treatment. This software is developed using programming languages ​​such as Python or Java (registered trademark) and works in conjunction with a database to manage appointment information and the progress of medical treatment.

[0107] The terminal is a computer device such as a smartphone or tablet, and it provides an interface for patients to make appointments and check their appointment schedules. Furthermore, the terminal receives reminder notifications sent from the server and functions as a tool for patients to reconfirm their appointment details themselves as needed.

[0108] The user is a patient who uses a terminal to access the hospital's appointment system and enter the necessary information. The server uses this information to provide diagnostic support and determine the urgency of the situation. Depending on the diagnosis, a video call can be arranged if necessary.

[0109] As a concrete example, in a hospital system in a certain region, when a patient makes an appointment via smartphone, the server updates the appointment status in real time and automatically sends a reminder as the appointment date approaches. Upon receiving this reminder, the patient can check the appointment date and time and make adjustments as needed.

[0110] An example of a prompt message generated using an AI model is: "Please provide the information needed to make a medical appointment. For example, I would like to know if I can go to a local hospital and what time slots are available for appointments."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users make appointments using a terminal. The input includes the user's desired appointment date and time, and the medical department. The terminal sends this data to the server. The server consults a database to check available time slots and doctor schedules, and generates a response prompting the user to confirm or re-select the appointment. The output is either the confirmed appointment information or a list of time slots requiring selection.

[0114] Step 2:

[0115] The server generates a reminder notification as the appointment date approaches, based on the reservation information. The input is the reservation date and time retrieved from the database. The server uses this information to create a reminder notification based on the specified date and time and sends it to the device. The output is the reminder notification displayed on the device.

[0116] Step 3:

[0117] The user completes an online medical questionnaire from their device. The input consists of the user's medical information and a description of their symptoms. The device sends this information to a server, which analyzes the data through a diagnostic support system to determine the urgency of the situation. Based on the diagnosis, a video call with a doctor may be recommended in some cases. The output includes an assessment of the urgency of the situation and recommended actions.

[0118] Step 4:

[0119] When a user arrives at the hospital, the server estimates the waiting time and sends a notification to the terminal. The input is data on the progress of consultations within the hospital. The server analyzes this information and performs calculations to estimate the waiting time. The output is an estimate of the user's acceptable waiting time and guidance on external waiting options if necessary.

[0120] Step 5:

[0121] After the consultation, the server automatically generates follow-up information and schedules the next appointment. Inputs include the final diagnosis and the patient's schedule. Based on this, the server suggests the next appointment and sends a follow-up notification to the terminal. Outputs include detailed information about the next appointment and follow-up recommendations.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention is an information system designed to enable patients to receive medical care without stress during the treatment process in a medical setting. This system can not only consistently manage the user (patient) from appointment scheduling to post-treatment care, but can also recognize the user's emotions and customize the service content accordingly.

[0124] First, the user makes an appointment using their device. The server receives this appointment information and monitors its progress in real time. As the appointment date approaches, the server automatically generates a reminder and sends a notification to the device. This allows the user to reconfirm their appointment and prepare for their visit with ample time.

[0125] Before coming to the clinic, users complete an online questionnaire via their device and send information about their symptoms to the server. The server analyzes this data, determines the urgency of the situation, and adjusts the priority of treatment accordingly. It also uses an emotion engine to recognize emotions from the user's input data and provides psychological support as needed. In this case, content promoting relaxation may be displayed on the device.

[0126] On the day of the appointment, when the user arrives at the hospital, the server monitors the progress of the consultation and calculates the waiting time. Here too, the emotion engine is used, and based on the user's emotional state, appropriate entertainment and relaxation options are suggested to the device while they wait.

[0127] Once the consultation is complete, the server sends the consultation results to the terminal as follow-up information and automatically schedules the next appointment. Based on the results of the emotion engine, a follow-up method tailored to the user's physical and mental state is recommended. This makes it easier for users to manage their own health. In this way, this system enhances patient satisfaction throughout the entire medical service and improves the operational efficiency of medical institutions.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses a terminal to enter the desired medical department and date / time for their appointment and sends it to the server. The server stores the received appointment information in a database and monitors the appointment status in real time.

[0131] Step 2:

[0132] The server automatically generates a reminder the day before the reservation date. The server sends this reminder to the user's device, and the user confirms the reservation upon receiving it.

[0133] Step 3:

[0134] Users use their devices to answer online questionnaires and send information about their symptoms and current health status to the server. The server analyzes this information and determines the urgency of the situation.

[0135] Step 4:

[0136] Based on the diagnostic results, the server uses an emotion engine to recognize the user's emotions. For example, if it determines that the user is feeling stressed, it will display relaxation-promoting content on the device.

[0137] Step 5:

[0138] When a user arrives at the hospital, the server monitors the progress of consultations within the hospital. The server predicts the waiting time and notifies the user of this information. Based on the user's emotional response, it suggests appropriate ways to spend the waiting time.

[0139] Step 6:

[0140] After the consultation is complete, the server collects the consultation results and automatically schedules the next appointment. In addition, the server generates follow-up information and notifies the user via their device.

[0141] Step 7:

[0142] Based on the analysis results of the emotion engine, the device suggests appropriate follow-up actions to the user. The user can then review this information and take the necessary actions to manage their own health.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In the medical treatment process at healthcare facilities, there are problems that make it difficult for patients to receive treatment smoothly and comfortably. Specifically, there is a lack of support that takes into account the patient's psychological state at each stage, from making an appointment to the consultation and post-consultation follow-up. In addition, the lack of information provided regarding the progress of treatment and waiting times is a factor that causes anxiety and dissatisfaction among patients.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes a reservation management means for monitoring the patient's appointment status in real time and adjusting the timing of their visit; a diagnostic support means for analyzing the patient's entered medical information and determining the urgency of the situation; and an emotion analysis means for analyzing the patient's emotional state and providing psychological support. This enables medical support tailored to the patient's psychological state and improves patient satisfaction throughout the entire medical process.

[0148] A "reservation management system" is a system or program that has the function of monitoring a patient's reservation status in real time and adjusting the optimal timing of their visit based on information on the progress of their medical treatment.

[0149] A "diagnostic support tool" is a system or program that analyzes patient-provided medical information and determines the urgency of symptoms based on that data, thereby prioritizing appropriate medical treatment.

[0150] A "waiting time management system" is a system or program that predicts waiting times based on the progress of medical examinations within a hospital and provides appropriate notifications to patients.

[0151] A "follow-up method" refers to a system or program that provides follow-up information to patients after a medical consultation and has the function of automating the scheduling of the next appointment.

[0152] An "emotional analysis tool" is a system or program that analyzes patient input data, understands the patient's emotional state based on the analysis results, and provides psychological support.

[0153] A "reminder mechanism" is a system or program that has the function of sending a notification to the user's terminal in advance regarding a scheduled medical appointment and requesting confirmation of the appointment details.

[0154] A "customized information provision method" refers to a system or program that has the function of providing personalized medical and health information to patients, taking into account medical interview data and the patient's emotional state.

[0155] This invention is an information system that consistently manages the patient's treatment process in a medical institution and provides support that takes into account the patient's psychological state. This system provides various functions related to appointment management, diagnostic support, waiting time management, follow-up, and emotion analysis.

[0156] Users access the medical institution's reservation system using a device (e.g., smartphone or computer). By entering their desired appointment date and symptoms into the reservation form and submitting it, the reservation information is transferred to the server. The server is hosted on the cloud and stores the reservation information in a database (e.g., MySQL®). Using programming languages ​​such as Python or Java, the server monitors the reservation status in real time and adjusts the timing of appointments based on the reservations.

[0157] Before visiting the clinic, users complete an online questionnaire and send the entered data to the server via their device. The server analyzes the questionnaire information using machine learning libraries (e.g., scikit-learn and TENSORFLOW®) to assess the urgency of the consultation. Simultaneously, it uses an emotion engine to recognize emotions from the user's input data and provides psychological support as needed. Relaxation-promoting content may also be displayed on the device.

[0158] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation and notifies the user of the estimated waiting time on their device. During the waiting time, entertainment and relaxation options are suggested, taking into account the patient's emotional state. After the consultation, the results are provided as follow-up information, and the next appointment is automatically scheduled.

[0159] For example, if the emotion engine detects an anxious state when a user makes a medical appointment, the server will display a message on the device such as, "Music to encourage deep breathing is available. Listen and relax."

[0160] An example of a prompt message is: "User is feeling anxious about the upcoming doctor's appointment. Suggest relaxing activities or content to help alleviate stress."

[0161] In this way, the present invention aims to improve the overall efficiency of medical care within healthcare institutions while simultaneously enhancing patient satisfaction.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users access the reservation system using their terminal and enter their desired appointment date and symptoms. The entered information (date, time, symptoms, etc.) is sent to the server via the reservation form. The server stores the received reservation information in a database, allowing for real-time monitoring of the reservation progress.

[0165] Step 2:

[0166] As the appointment date approaches, the server generates a reminder based on the appointment information. This involves checking the configured schedule record and using a reminder template to send the reminder as an email or push notification. The server then sends this reminder information to the user's device to prompt confirmation.

[0167] Step 3:

[0168] Before coming to the clinic, users complete an online questionnaire via their device. They enter their symptoms, medical history, lifestyle, etc., into the questionnaire form and submit it. The server receives this data, which is then analyzed using machine learning algorithms to determine the urgency of the situation. This allows for the calculation of treatment priority.

[0169] Step 4:

[0170] The server inputs the received questionnaire data into an emotion engine and uses natural language processing technology to recognize the user's emotions. Based on the results of the emotion analysis, if it is determined that psychological support is needed, the server selects content and support messages that promote relaxation and presents them to the device.

[0171] Step 5:

[0172] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation in real time. It retrieves consultation progress data and calculates the waiting time. Based on the calculated waiting time, the server sends a notification to the user's device, providing the total waiting time and progress information.

[0173] Step 6:

[0174] While waiting, the server generates prompts from an AI model based on the user's emotion analysis results, suggesting entertainment and relaxation options to the device. Specifically, it selects and delivers content such as videos and music that the user can relax with.

[0175] Step 7:

[0176] After the consultation is complete, the server summarizes the results and generates follow-up information. This information, including scheduling the next appointment, is automatically sent to the user's device. The follow-up content is personalized based on the results of the sentiment analysis and provided as health management advice.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] In current long-term care services, appointment management and waiting time adjustments for users are inadequate, often causing anxiety and stress for users. Furthermore, insufficient follow-up care results in inadequate health management for users. Additionally, a lack of responses to emotional changes negatively impacts the quality of service. These issues need to be resolved.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes reservation management means that monitors the user's reservation status in real time and adjusts the timing of visits based on care progress information; diagnostic support means that analyzes the medical questionnaire information entered by the user and determines the urgency; and emotional support means that evaluates the user's psychological state using emotion analysis technology and provides relaxation content. This makes it possible to reduce user stress and improve the overall quality of the service.

[0182] A "reservation management system" is a function that monitors the user's reservation status in real time and adjusts the timing of visits based on information about the progress of care.

[0183] A "diagnostic support tool" is an analytical function that determines the urgency of a patient's condition based on the medical information entered by the user.

[0184] The "waiting time management method" is a function that predicts the waiting time for home care visits and provides notifications, including options for waiting outside the home.

[0185] "Follow-up methods" refer to functions that provide follow-up information after care and automate the scheduling of the next appointment.

[0186] "Emotional support measures" refer to functions that use emotion analysis technology to evaluate the user's psychological state and provide relaxation content.

[0187] The system for realizing this invention is built using a cloud server, a user terminal (such as a smartphone), and a software platform equipped with emotion analysis technology. The cloud server receives reservation information and medical questionnaire data, and monitors and analyzes it in real time. The user terminal displays notifications and relaxation content based on the received information and is responsible for interacting with the user.

[0188] The server first checks the reservation status from users in real time and adjusts the timing of care provision based on the progress information. During this process, it utilizes cloud services to process data and feeds the analysis results back to the user's terminal in real time. Next, the server analyzes the medical history data to determine the urgency level. Based on this determination, it dynamically adjusts priorities and modifies the care content as needed.

[0189] As a means of emotional support, the server uses facial recognition and natural language processing technologies to evaluate the user's emotional state. Specifically, it analyzes facial expressions using technologies such as Amazon Rekognition and determines emotions from text using Google Cloud Natural Language. Based on the analysis results, appropriate relaxation content is sent to the user's device, and the user can use it to reduce stress.

[0190] For example, if a user makes a home care appointment and there is questionnaire data indicating signs of stress, the device will display "To help alleviate your anxiety, we suggest some relaxing music," and play meditation music. An example of a specific prompt might be, "Based on the user's emotional analysis, please suggest content to reduce stress. The current emotional state is 'anxious'."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server receives reservation information from the user's terminal. The input includes the user's reservation date and time and care details. The server saves this information to a cloud database and prepares for real-time monitoring. The output is a reservation confirmation notification.

[0194] Step 2:

[0195] When a user completes an online medical questionnaire on their device, the data is sent to the server. The input consists of information about the user's health status and symptoms, and the server analyzes this data to determine the urgency of the situation. The output is the result of the urgency determination.

[0196] Step 3:

[0197] The server analyzes the user's emotions using facial recognition APIs and natural language processing tools. Input consists of facial expression data and questionnaire text provided by the terminal; the server analyzes this data and performs a psychological assessment. The output is the analyzed emotional data.

[0198] Step 4:

[0199] Based on the analyzed emotional data, the server provides appropriate relaxation content to the user's terminal. The input is emotional data, and the output is the selection and transmission of relaxation content. In this process, content such as music and videos are suggested.

[0200] Step 5:

[0201] The server sends a reminder notification to the user's terminal as the scheduled date and time for the home care visit approaches. The input is the reservation information, and the output is the reminder notification. This allows the user to prepare for the visit.

[0202] Step 6:

[0203] After care, the server sends follow-up information on the care results to the user's terminal and automates the scheduling of the next appointment. The input is care result data, and the output is follow-up information and a suggestion for the next appointment. This allows the user to continue managing their health.

[0204] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0205] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0206] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0210] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0211] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0212] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0213] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0214] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0215] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0216] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0217] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0218] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0219] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0220] This invention provides an advanced information system for improving the efficiency of medical treatment processes in hospitals. In this embodiment, the system primarily operates around three components: a server, a terminal, and a user, each playing its own role.

[0221] First, the user makes a reservation via their device. The server receives this reservation, monitors the reservation status in real time using a reservation management system, and stores it in a database. For example, if a patient requests an internal medicine appointment for a specific date and time, the server checks the information and updates the schedule accordingly.

[0222] As the appointment date approaches, the server sends a reminder notification to the user's terminal, prompting the user to confirm. The user receives the notification and can reconfirm the appointment if necessary.

[0223] In the online consultation conducted before visiting the clinic, users input their symptoms from their device and send them to the server. The server's diagnostic support system analyzes this data to determine the urgency of the situation. For example, if it is determined that the situation is not urgent, the device will be notified of what to do at home and, in some cases, a video call with a doctor may be recommended.

[0224] When a user arrives at the hospital, a waiting time management system is activated. The server retrieves the progress of consultations within the hospital from a database and estimates the waiting time. It then notifies the user's terminal of the estimated waiting time and provides options for leaving the hospital if a long wait is necessary. At this time, information such as nearby cafes may also be provided.

[0225] After the consultation, a follow-up system is activated. The server sends the consultation results and follow-up information to the terminal and automatically schedules the next appointment. The user can then review this and plan their next visit. This series of operations can reduce the burden on patients and significantly improve the operational efficiency of healthcare facilities.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user requests an appointment using a terminal. The user enters the desired medical department and date / time, and sends this information to the server via the terminal.

[0229] Step 2:

[0230] The server records the received reservation request in its database and checks the availability of the reservation using the reservation management system. If the reservation is confirmed, the server notifies the terminal of this information.

[0231] Step 3:

[0232] As the appointment date approaches, the server automatically generates a reminder notification. The server sends the reminder to the user's device, notifying them to reconfirm their appointment.

[0233] Step 4:

[0234] Users complete an online medical questionnaire. They input their symptoms via their device and send the questionnaire data to a server. The input is then analyzed by diagnostic support tools.

[0235] Step 5:

[0236] The server determines the urgency of the situation based on the medical questionnaire information. The server sends the determination result to the terminal and displays it to the user. It also provides guidance on how to handle the situation at home and how to schedule a video call with a doctor, as needed.

[0237] Step 6:

[0238] After the patient arrives at the hospital, the server monitors the progress of consultations within the hospital in real time and estimates the waiting time. The server uses a waiting time management system to notify the terminal of the predicted waiting time and the option of leaving the hospital.

[0239] Step 7:

[0240] Once the consultation is complete, the server processes the results using a follow-up system. The server automatically schedules the next appointment and sends that information to the terminal.

[0241] Step 8:

[0242] The device notifies the user of their next appointment information and detailed medical results, allowing them to review the follow-up plan. After receiving the notification, the user can prepare for their next appointment.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In modern healthcare settings, patient appointment management, optimization of waiting times for consultations, and post-consultation follow-up are often not carried out efficiently. This impairs patient convenience and reduces the operational efficiency of healthcare institutions. To solve these problems, it is necessary to automate these processes and provide more advanced information management tools.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes: appointment management means for monitoring patient appointment status in real time and adjusting the timing of patient visits based on information on the progress of medical care provision; diagnostic support means for analyzing patient-entered medical information and determining the urgency of the situation; and waiting time management means for predicting waiting times based on the progress of consultations within the medical institution and providing notifications, including options for waiting outside. This improves patient convenience and optimizes the operational efficiency of medical institutions.

[0248] A "reservation management system" is a function that receives users' reservation information, monitors it in real time, and adjusts the timing of their visits based on information about the progress of medical care provision.

[0249] A "diagnostic support tool" is a function that analyzes the medical information entered by the patient and determines the urgency of the symptoms based on that data.

[0250] A "waiting time management system" is a function that estimates waiting times based on the progress of consultations within a medical institution and provides notifications, including external waiting options, as needed.

[0251] "Follow-up services" refer to functions that provide follow-up information after medical treatment, automate the scheduling of the next appointment, and facilitate the patient's next visit.

[0252] A "reminder mechanism" is a function that sends a medical appointment reminder notification to the user's device and asks the user to confirm it.

[0253] "Communication methods" refers to functions that allow for visual communication with healthcare professionals, such as video calls, based on the patient's medical history data.

[0254] This invention is an advanced information system aimed at improving the efficiency of medical treatment processes in hospitals, and it primarily functions through three parties: a server, a terminal, and a user.

[0255] First, the user makes a hospital appointment using a device. This device is a computer such as a PC or smartphone, and requires an internet connection to access the hospital's appointment system. The appointment information entered by the user is sent to a server via the internet. The server receives this information and updates the schedule in real time using dedicated appointment management software. Specifically, this involves data processing such as storing appointment information in a database, calculating available time slots, and updating appointment status.

[0256] As the appointment date approaches, the server sends a reminder notification to the user's device. This notification is sent via email or app notification, prompting the user to confirm the appointment date, time, and location. Such a reminder system can utilize notification tools developed using Python, JavaScript, or other languages.

[0257] Users can also complete an online questionnaire before coming to the clinic. This is done via a dedicated questionnaire application that runs on the user's device. The user enters their symptoms and sends them to the server, which analyzes them using diagnostic support software to determine the urgency. If a serious case is detected, a video call system (e.g., Zoom or WebRTC) is activated using the communication method to immediately suggest a video call with a doctor.

[0258] When a user arrives at the hospital, the server retrieves consultation progress data from a database and analyzes it using waiting time management software. Based on this data, it predicts waiting times and notifies the user's terminal with information including options for leaving the hospital. The notification function can also be linked with a map application for user convenience.

[0259] After the consultation, the server uses a follow-up system to send the consultation results and next appointment information to the user. This automatically schedules the next appointment, and the user can review it and adjust the plan accordingly.

[0260] As a concrete example, the following is an example of a prompt sentence to be input to the generating AI model: "Please describe the procedure for updating the appointment status in the medical appointment system and sending a notification to the patient." By using this prompt, the effectiveness of the invention can be maximized, and the entire medical process can be carried out smoothly.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The user enters their hospital appointment information using a terminal. They enter the necessary information (e.g., department, date and time, patient information) into the input form on the terminal and press the submit button to send the information to the server.

[0264] Step 2:

[0265] The server analyzes the reservation information received from the terminal. It accesses the database and checks availability by comparing it with the current reservation status. It adjusts reservations as needed and updates the database with new schedules. At this time, new reservation information is created in the database and the availability is updated.

[0266] Step 3:

[0267] Once the reservation is complete, the server sends confirmation information to the user's terminal. The user receives this confirmation notification and can reconfirm the reservation details. The notification includes reservation details (e.g., date and time, department, doctor's name).

[0268] Step 4:

[0269] As the appointment date approaches, the server sends a reminder notification to the user's device. The reminder is automatically generated at the specified date and time and sent to the user via email or app notification. Here, the notification information based on the schedule is output.

[0270] Step 5:

[0271] Before coming to the clinic, users complete an online medical questionnaire via their device. They enter their symptoms and health status into the questionnaire form and submit it to the server. Based on the input data, the server analyzes the symptoms using diagnostic support tools and determines the urgency of the situation. The analysis results are output directly and influence the next step in treatment.

[0272] Step 6:

[0273] Upon arrival at the hospital, the server estimates the waiting time based on the progress of the consultation. The server retrieves consultation progress data from the database and calculates the waiting time using a waiting time management system. The results are notified to the terminal, and suggestions for how to spend time outside the hospital (e.g., information on nearby facilities) are made as needed.

[0274] Step 7:

[0275] After the consultation, the server sends follow-up information to the user's terminal. It automatically generates and notifies the user of information including the consultation results, next appointment scheduling, and a self-follow-up guide. This allows the user to plan their next visit.

[0276] Through these steps, users can have a smoother consultation experience, and healthcare institutions can achieve more efficient operations.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In urban hospitals, the complexity of appointment management, which ensures patients receive appropriate medical care, is leading to decreased efficiency. Furthermore, patients are unable to effectively utilize their waiting time at hospitals, resulting in a lack of smooth medical delivery. These problems not only impair patient convenience but also reduce the operational efficiency of healthcare institutions. Solutions are needed to improve this situation.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes a reservation management means that monitors the patient's reservation status in real time and adjusts the timing of their visit based on the progress of their treatment; a diagnostic support means that analyzes the medical questionnaire information entered by the patient and determines the urgency of the situation; and a health care navigation means that centrally manages the hospital's reservation status and recommends the most suitable medical service to the patient. This enables the provision of efficient medical services to patients and allows for rapid responses both inside and outside the hospital.

[0282] "Patient appointment status" refers to information that shows the status of appointments made at a hospital based on the patient's preferred date and time for treatment.

[0283] "Real-time monitoring" is a process of immediately monitoring information that changes moment by moment and always grasping the latest status.

[0284] "Medical treatment progress information" is information indicating at what stage the medical treatment in the hospital is progressing.

[0285] "Adjustment of hospital visit timing" refers to the management and its settings to enable patients to visit the hospital at an appropriate time.

[0286] "Diagnostic support means" is a function for analyzing medical information collected from patients and assisting the diagnostic process.

[0287] "Severity determination" is a process of evaluating the severity and urgency of a patient's symptoms.

[0288] "Batch management" refers to comprehensively managing multiple data and information and processing them in a unified manner.

[0289] "Healthcare navigation" is a system that provides information and recommendations to enable patients to use the most appropriate medical services.

[0290] "Waiting time prediction" is to predict the waiting time until the start of medical treatment and provide information to patients.

[0291] "External waiting options" are options for providing patients with available waiting places and services outside the hospital.

[0292] "Follow-up information" is information for monitoring the treatment progress after medical treatment.

[0293] "Automation of next reservation" refers to the system automatically setting the reservation for the next medical treatment after the medical treatment.

[0294] To implement this invention, a system is built in which a server, a terminal, and a user work together. The server requires dedicated software to monitor patient appointment status in real time and track the progress of medical treatment. This software is developed using programming languages ​​such as Python or Java and works in conjunction with a database to manage appointment information and treatment progress.

[0295] The terminal is a computer device such as a smartphone or tablet, and it provides an interface for patients to make appointments and check their appointment schedules. Furthermore, the terminal receives reminder notifications sent from the server and functions as a tool for patients to reconfirm their appointment details themselves as needed.

[0296] The user is a patient who uses a terminal to access the hospital's appointment system and enter the necessary information. The server uses this information to provide diagnostic support and determine the urgency of the situation. Depending on the diagnosis, a video call can be arranged if necessary.

[0297] As a concrete example, in a hospital system in a certain region, when a patient makes an appointment via smartphone, the server updates the appointment status in real time and automatically sends a reminder as the appointment date approaches. Upon receiving this reminder, the patient can check the appointment date and time and make adjustments as needed.

[0298] An example of a prompt message generated using an AI model is: "Please provide the information needed to make a medical appointment. For example, I would like to know if I can go to a local hospital and what time slots are available for appointments."

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user makes a medical appointment using a terminal. The input is the desired medical appointment date and time and the medical department. The terminal sends this data to the server. The server refers to the database, checks the available time slots and the doctor's schedule, and generates a response prompting the user to confirm or reselect the appointment. The output is the confirmed appointment information or a list of time slots that require selection.

[0302] Step 2:

[0303] When the medical appointment date approaches, the server generates a reminder notification based on the appointment information. The input is the appointment date and time obtained from the database. The server uses this information to create a reminder notification based on the specified date and time and sends it to the terminal. The output is the reminder notification displayed on the terminal.

[0304] Step 3:

[0305] The user fills out an online consultation from the terminal. The input is the user's consultation information and description of symptoms. The terminal sends this to the server, and the server analyzes the data through a diagnostic support system and determines the urgency. Depending on the diagnostic result, a video call with a doctor may also be recommended. The output is an assessment of the urgency and recommended countermeasures.

[0306] Step 4:

[0307] When the user arrives at the hospital, the server estimates the waiting time and sends a notification to the terminal. The input is data on the progress of examinations within the hospital. The server analyzes this information and performs calculations to estimate the waiting time. The output is an estimate of the waiting time that the user can tolerate and guidance on external waiting options if necessary.

[0308] Step 5:

[0309] After the consultation, the server automatically generates follow-up information and schedules the next appointment. Inputs include the final diagnosis and the patient's schedule. Based on this, the server suggests the next appointment and sends a follow-up notification to the terminal. Outputs include detailed information about the next appointment and follow-up recommendations.

[0310] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0311] This invention is an information system designed to enable patients to receive medical care without stress during the treatment process in a medical setting. This system can not only consistently manage the user (patient) from appointment scheduling to post-treatment care, but can also recognize the user's emotions and customize the service content accordingly.

[0312] First, the user makes an appointment using their device. The server receives this appointment information and monitors its progress in real time. As the appointment date approaches, the server automatically generates a reminder and sends a notification to the device. This allows the user to reconfirm their appointment and prepare for their visit with ample time.

[0313] Before coming to the clinic, users complete an online questionnaire via their device and send information about their symptoms to the server. The server analyzes this data, determines the urgency of the situation, and adjusts the priority of treatment accordingly. It also uses an emotion engine to recognize emotions from the user's input data and provides psychological support as needed. In this case, content promoting relaxation may be displayed on the device.

[0314] On the day of the appointment, when the user arrives at the hospital, the server monitors the progress of the consultation and calculates the waiting time. Here too, the emotion engine is used, and based on the user's emotional state, appropriate entertainment and relaxation options are suggested to the device while they wait.

[0315] Once the consultation is complete, the server sends the consultation results to the terminal as follow-up information and automatically schedules the next appointment. Based on the results of the emotion engine, a follow-up method tailored to the user's physical and mental state is recommended. This makes it easier for users to manage their own health. In this way, this system enhances patient satisfaction throughout the entire medical service and improves the operational efficiency of medical institutions.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user uses a terminal to enter the desired medical department and date / time for their appointment and sends it to the server. The server stores the received appointment information in a database and monitors the appointment status in real time.

[0319] Step 2:

[0320] The server automatically generates a reminder the day before the reservation date. The server sends this reminder to the user's device, and the user confirms the reservation upon receiving it.

[0321] Step 3:

[0322] Users use their devices to answer online questionnaires and send information about their symptoms and current health status to the server. The server analyzes this information and determines the urgency of the situation.

[0323] Step 4:

[0324] Based on the diagnostic results, the server uses an emotion engine to recognize the user's emotions. For example, if it determines that the user is feeling stressed, it will display relaxation-promoting content on the device.

[0325] Step 5:

[0326] When a user arrives at the hospital, the server monitors the progress of consultations within the hospital. The server predicts the waiting time and notifies the user of this information. Based on the user's emotional response, it suggests appropriate ways to spend the waiting time.

[0327] Step 6:

[0328] After the consultation is complete, the server collects the consultation results and automatically schedules the next appointment. In addition, the server generates follow-up information and notifies the user via their device.

[0329] Step 7:

[0330] Based on the analysis results of the emotion engine, the device suggests appropriate follow-up actions to the user. The user can then review this information and take the necessary actions to manage their own health.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] In the medical treatment process at healthcare facilities, there are problems that make it difficult for patients to receive treatment smoothly and comfortably. Specifically, there is a lack of support that takes into account the patient's psychological state at each stage, from making an appointment to the consultation and post-consultation follow-up. In addition, the lack of information provided regarding the progress of treatment and waiting times is a factor that causes anxiety and dissatisfaction among patients.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes a reservation management means for monitoring the patient's appointment status in real time and adjusting the timing of their visit; a diagnostic support means for analyzing the patient's entered medical information and determining the urgency of the situation; and an emotion analysis means for analyzing the patient's emotional state and providing psychological support. This enables medical support tailored to the patient's psychological state and improves patient satisfaction throughout the entire medical process.

[0336] A "reservation management system" is a system or program that has the function of monitoring a patient's reservation status in real time and adjusting the optimal timing of their visit based on information on the progress of their medical treatment.

[0337] A "diagnostic support tool" is a system or program that analyzes patient-provided medical information and determines the urgency of symptoms based on that data, thereby prioritizing appropriate medical treatment.

[0338] A "waiting time management system" is a system or program that predicts waiting times based on the progress of medical examinations within a hospital and provides appropriate notifications to patients.

[0339] A "follow-up method" refers to a system or program that provides follow-up information to patients after a medical consultation and has the function of automating the scheduling of the next appointment.

[0340] An "emotional analysis tool" is a system or program that analyzes patient input data, understands the patient's emotional state based on the analysis results, and provides psychological support.

[0341] A "reminder mechanism" is a system or program that has the function of sending a notification to the user's terminal in advance regarding a scheduled medical appointment and requesting confirmation of the appointment details.

[0342] A "customized information provision method" refers to a system or program that has the function of providing personalized medical and health information to patients, taking into account medical interview data and the patient's emotional state.

[0343] This invention is an information system that consistently manages the patient's treatment process in a medical institution and provides support that takes into account the patient's psychological state. This system provides various functions related to appointment management, diagnostic support, waiting time management, follow-up, and emotion analysis.

[0344] Users access the medical institution's reservation system using a device (e.g., smartphone or computer). They enter their desired appointment date and symptoms into a reservation form and submit it, which transmits the reservation information to the server. The server is hosted on the cloud and stores the reservation information in a database (e.g., MySQL). Using programming languages ​​such as Python or Java, the server monitors the reservation status in real time and adjusts the timing of appointments based on the reservations.

[0345] Before visiting the clinic, users complete an online questionnaire and send the entered data to the server via their device. The server uses machine learning libraries (e.g., scikit-learn and TensorFlow) to analyze the questionnaire information and assess the urgency of the patient's visit. Simultaneously, an emotion engine recognizes the user's emotions from their input data and provides psychological support as needed. Relaxation-enhancing content may also be displayed on the device.

[0346] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation and notifies the user of the estimated waiting time on their device. During the waiting time, entertainment and relaxation options are suggested, taking into account the patient's emotional state. After the consultation, the results are provided as follow-up information, and the next appointment is automatically scheduled.

[0347] For example, if the emotion engine detects an anxious state when a user makes a medical appointment, the server will display a message on the device such as, "Music to encourage deep breathing is available. Listen and relax."

[0348] An example of a prompt message is: "User is feeling anxious about the upcoming doctor's appointment. Suggest relaxing activities or content to help alleviate stress."

[0349] In this way, the present invention aims to improve the overall efficiency of medical care within healthcare institutions while simultaneously enhancing patient satisfaction.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] Users access the reservation system using their terminal and enter their desired appointment date and symptoms. The entered information (date, time, symptoms, etc.) is sent to the server via the reservation form. The server stores the received reservation information in a database, allowing for real-time monitoring of the reservation progress.

[0353] Step 2:

[0354] As the appointment date approaches, the server generates a reminder based on the appointment information. This involves checking the configured schedule record and using a reminder template to send the reminder as an email or push notification. The server then sends this reminder information to the user's device to prompt confirmation.

[0355] Step 3:

[0356] Before coming to the clinic, users complete an online questionnaire via their device. They enter their symptoms, medical history, lifestyle, etc., into the questionnaire form and submit it. The server receives this data, which is then analyzed using machine learning algorithms to determine the urgency of the situation. This allows for the calculation of treatment priority.

[0357] Step 4:

[0358] The server inputs the received questionnaire data into an emotion engine and uses natural language processing technology to recognize the user's emotions. Based on the results of the emotion analysis, if it is determined that psychological support is needed, the server selects content and support messages that promote relaxation and presents them to the device.

[0359] Step 5:

[0360] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation in real time. It retrieves consultation progress data and calculates the waiting time. Based on the calculated waiting time, the server sends a notification to the user's device, providing the total waiting time and progress information.

[0361] Step 6:

[0362] While waiting, the server generates prompts from an AI model based on the user's emotion analysis results, suggesting entertainment and relaxation options to the device. Specifically, it selects and delivers content such as videos and music that the user can relax with.

[0363] Step 7:

[0364] After the consultation is complete, the server summarizes the results and generates follow-up information. This information, including scheduling the next appointment, is automatically sent to the user's device. The follow-up content is personalized based on the results of the sentiment analysis and provided as health management advice.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0367] In current long-term care services, appointment management and waiting time adjustments for users are inadequate, often causing anxiety and stress for users. Furthermore, insufficient follow-up care results in inadequate health management for users. Additionally, a lack of responses to emotional changes negatively impacts the quality of service. These issues need to be resolved.

[0368] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0369] In this invention, the server includes reservation management means that monitors the user's reservation status in real time and adjusts the timing of visits based on care progress information; diagnostic support means that analyzes the medical questionnaire information entered by the user and determines the urgency; and emotional support means that evaluates the user's psychological state using emotion analysis technology and provides relaxation content. This makes it possible to reduce user stress and improve the overall quality of the service.

[0370] A "reservation management system" is a function that monitors the user's reservation status in real time and adjusts the timing of visits based on information about the progress of care.

[0371] A "diagnostic support tool" is an analytical function that determines the urgency of a patient's condition based on the medical information entered by the user.

[0372] The "waiting time management method" is a function that predicts the waiting time for home care visits and provides notifications, including options for waiting outside the home.

[0373] "Follow-up methods" refer to functions that provide follow-up information after care and automate the scheduling of the next appointment.

[0374] "Emotional support measures" refer to functions that use emotion analysis technology to evaluate the user's psychological state and provide relaxation content.

[0375] The system for realizing this invention is built using a cloud server, a user terminal (such as a smartphone), and a software platform equipped with emotion analysis technology. The cloud server receives reservation information and medical questionnaire data, and monitors and analyzes it in real time. The user terminal displays notifications and relaxation content based on the received information and is responsible for interacting with the user.

[0376] The server first checks the reservation status from users in real time and adjusts the timing of care provision based on the progress information. During this process, it utilizes cloud services to process data and feeds the analysis results back to the user's terminal in real time. Next, the server analyzes the medical history data to determine the urgency level. Based on this determination, it dynamically adjusts priorities and modifies the care content as needed.

[0377] As a means of emotional support, the server uses facial recognition and natural language processing technologies to evaluate the user's emotional state. Specifically, it analyzes facial expressions using technologies such as Amazon Rekognition and determines emotions from text using Google Cloud Natural Language. Based on the analysis results, appropriate relaxation content is sent to the user's device, and the user can use it to reduce stress.

[0378] For example, if a user makes a home care appointment and there is questionnaire data indicating signs of stress, the device will display "To help alleviate your anxiety, we suggest some relaxing music," and play meditation music. An example of a specific prompt might be, "Based on the user's emotional analysis, please suggest content to reduce stress. The current emotional state is 'anxious'."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The server receives reservation information from the user's terminal. The input includes the user's reservation date and time and care details. The server saves this information to a cloud database and prepares for real-time monitoring. The output is a reservation confirmation notification.

[0382] Step 2:

[0383] When a user completes an online medical questionnaire on their device, the data is sent to the server. The input consists of information about the user's health status and symptoms, and the server analyzes this data to determine the urgency of the situation. The output is the result of the urgency determination.

[0384] Step 3:

[0385] The server analyzes the user's emotions using facial recognition APIs and natural language processing tools. Input consists of facial expression data and questionnaire text provided by the terminal; the server analyzes this data and performs a psychological assessment. The output is the analyzed emotional data.

[0386] Step 4:

[0387] Based on the analyzed emotional data, the server provides appropriate relaxation content to the user's terminal. The input is emotional data, and the output is the selection and transmission of relaxation content. In this process, content such as music and videos are suggested.

[0388] Step 5:

[0389] The server sends a reminder notification to the user's terminal as the scheduled date and time for the home care visit approaches. The input is the reservation information, and the output is the reminder notification. This allows the user to prepare for the visit.

[0390] Step 6:

[0391] After care, the server sends follow-up information on the care results to the user's terminal and automates the scheduling of the next appointment. The input is care result data, and the output is follow-up information and a suggestion for the next appointment. This allows the user to continue managing their health.

[0392] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0393] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0394] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0398] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0399] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0400] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0401] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0402] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0403] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0404] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0405] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0406] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0407] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0408] This invention provides an advanced information system for improving the efficiency of medical treatment processes in hospitals. In this embodiment, the system primarily operates around three components: a server, a terminal, and a user, each playing its own role.

[0409] First, the user makes a reservation via their device. The server receives this reservation, monitors the reservation status in real time using a reservation management system, and stores it in a database. For example, if a patient requests an internal medicine appointment for a specific date and time, the server checks the information and updates the schedule accordingly.

[0410] As the appointment date approaches, the server sends a reminder notification to the user's terminal, prompting the user to confirm. The user receives the notification and can reconfirm the appointment if necessary.

[0411] In the online consultation conducted before visiting the clinic, users input their symptoms from their device and send them to the server. The server's diagnostic support system analyzes this data to determine the urgency of the situation. For example, if it is determined that the situation is not urgent, the device will be notified of what to do at home and, in some cases, a video call with a doctor may be recommended.

[0412] When a user arrives at the hospital, a waiting time management system is activated. The server retrieves the progress of consultations within the hospital from a database and estimates the waiting time. It then notifies the user's terminal of the estimated waiting time and provides options for leaving the hospital if a long wait is necessary. At this time, information such as nearby cafes may also be provided.

[0413] After the consultation, a follow-up system is activated. The server sends the consultation results and follow-up information to the terminal and automatically schedules the next appointment. The user can then review this and plan their next visit. This series of operations can reduce the burden on patients and significantly improve the operational efficiency of healthcare facilities.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user requests an appointment using a terminal. The user enters the desired medical department and date / time, and sends this information to the server via the terminal.

[0417] Step 2:

[0418] The server records the received reservation request in its database and checks the availability of the reservation using the reservation management system. If the reservation is confirmed, the server notifies the terminal of this information.

[0419] Step 3:

[0420] As the appointment date approaches, the server automatically generates a reminder notification. The server sends the reminder to the user's device, notifying them to reconfirm their appointment.

[0421] Step 4:

[0422] Users complete an online medical questionnaire. They input their symptoms via their device and send the questionnaire data to a server. The input is then analyzed by diagnostic support tools.

[0423] Step 5:

[0424] The server determines the urgency of the situation based on the medical questionnaire information. The server sends the determination result to the terminal and displays it to the user. It also provides guidance on how to handle the situation at home and how to schedule a video call with a doctor, as needed.

[0425] Step 6:

[0426] After the patient arrives at the hospital, the server monitors the progress of consultations within the hospital in real time and estimates the waiting time. The server uses a waiting time management system to notify the terminal of the predicted waiting time and the option of leaving the hospital.

[0427] Step 7:

[0428] Once the consultation is complete, the server processes the results using a follow-up system. The server automatically schedules the next appointment and sends that information to the terminal.

[0429] Step 8:

[0430] The device notifies the user of their next appointment information and detailed medical results, allowing them to review the follow-up plan. After receiving the notification, the user can prepare for their next appointment.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] In modern healthcare settings, patient appointment management, optimization of waiting times for consultations, and post-consultation follow-up are often not carried out efficiently. This impairs patient convenience and reduces the operational efficiency of healthcare institutions. To solve these problems, it is necessary to automate these processes and provide more advanced information management tools.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes: appointment management means for monitoring patient appointment status in real time and adjusting the timing of patient visits based on information on the progress of medical care provision; diagnostic support means for analyzing patient-entered medical information and determining the urgency of the situation; and waiting time management means for predicting waiting times based on the progress of consultations within the medical institution and providing notifications, including options for waiting outside. This improves patient convenience and optimizes the operational efficiency of medical institutions.

[0436] A "reservation management system" is a function that receives users' reservation information, monitors it in real time, and adjusts the timing of their visits based on information about the progress of medical care provision.

[0437] A "diagnostic support tool" is a function that analyzes the medical information entered by the patient and determines the urgency of the symptoms based on that data.

[0438] A "waiting time management system" is a function that estimates waiting times based on the progress of consultations within a medical institution and provides notifications, including external waiting options, as needed.

[0439] "Follow-up services" refer to functions that provide follow-up information after medical treatment, automate the scheduling of the next appointment, and facilitate the patient's next visit.

[0440] A "reminder mechanism" is a function that sends a medical appointment reminder notification to the user's device and asks the user to confirm it.

[0441] "Communication methods" refers to functions that allow for visual communication with healthcare professionals, such as video calls, based on the patient's medical history data.

[0442] This invention is an advanced information system aimed at improving the efficiency of medical treatment processes in hospitals, and it primarily functions through three parties: a server, a terminal, and a user.

[0443] First, the user makes a hospital appointment using a device. This device is a computer such as a PC or smartphone, and requires an internet connection to access the hospital's appointment system. The appointment information entered by the user is sent to a server via the internet. The server receives this information and updates the schedule in real time using dedicated appointment management software. Specifically, this involves data processing such as storing appointment information in a database, calculating available time slots, and updating appointment status.

[0444] As the appointment date approaches, the server sends a reminder notification to the user's device. This notification is sent via email or app notification, prompting the user to confirm the appointment date, time, and location. Such a reminder system can utilize notification tools developed using Python, JavaScript, or other languages.

[0445] Users can also complete an online questionnaire before coming to the clinic. This is done via a dedicated questionnaire application that runs on the user's device. The user enters their symptoms and sends them to the server, which analyzes them using diagnostic support software to determine the urgency. If a serious case is detected, a video call system (e.g., Zoom or WebRTC) is activated using the communication method to immediately suggest a video call with a doctor.

[0446] When a user arrives at the hospital, the server retrieves consultation progress data from a database and analyzes it using waiting time management software. Based on this data, it predicts waiting times and notifies the user's terminal with information including options for leaving the hospital. The notification function can also be linked with a map application for user convenience.

[0447] After the consultation, the server uses a follow-up system to send the consultation results and next appointment information to the user. This automatically schedules the next appointment, and the user can review it and adjust the plan accordingly.

[0448] As a concrete example, the following is an example of a prompt sentence to be input to the generating AI model: "Please describe the procedure for updating the appointment status in the medical appointment system and sending a notification to the patient." By using this prompt, the effectiveness of the invention can be maximized, and the entire medical process can be carried out smoothly.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The user enters their hospital appointment information using a terminal. They enter the necessary information (e.g., department, date and time, patient information) into the input form on the terminal and press the submit button to send the information to the server.

[0452] Step 2:

[0453] The server analyzes the reservation information received from the terminal. It accesses the database and checks availability by comparing it with the current reservation status. It adjusts reservations as needed and updates the database with new schedules. At this time, new reservation information is created in the database and the availability is updated.

[0454] Step 3:

[0455] Once the reservation is complete, the server sends confirmation information to the user's terminal. The user receives this confirmation notification and can reconfirm the reservation details. The notification includes reservation details (e.g., date and time, department, doctor's name).

[0456] Step 4:

[0457] As the appointment date approaches, the server sends a reminder notification to the user's device. The reminder is automatically generated at the specified date and time and sent to the user via email or app notification. Here, the notification information based on the schedule is output.

[0458] Step 5:

[0459] Before coming to the clinic, users complete an online medical questionnaire via their device. They enter their symptoms and health status into the questionnaire form and submit it to the server. Based on the input data, the server analyzes the symptoms using diagnostic support tools and determines the urgency of the situation. The analysis results are output directly and influence the next step in treatment.

[0460] Step 6:

[0461] Upon arrival at the hospital, the server estimates the waiting time based on the progress of the consultation. The server retrieves consultation progress data from the database and calculates the waiting time using a waiting time management system. The results are notified to the terminal, and suggestions for how to spend time outside the hospital (e.g., information on nearby facilities) are made as needed.

[0462] Step 7:

[0463] After the consultation, the server sends follow-up information to the user's terminal. It automatically generates and notifies the user of information including the consultation results, next appointment scheduling, and a self-follow-up guide. This allows the user to plan their next visit.

[0464] Through these steps, users can have a smoother consultation experience, and healthcare institutions can achieve more efficient operations.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] In urban hospitals, the complexity of appointment management, which ensures patients receive appropriate medical care, is leading to decreased efficiency. Furthermore, patients are unable to effectively utilize their waiting time at hospitals, resulting in a lack of smooth medical delivery. These problems not only impair patient convenience but also reduce the operational efficiency of healthcare institutions. Solutions are needed to improve this situation.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes a reservation management means that monitors the patient's reservation status in real time and adjusts the timing of their visit based on the progress of their treatment; a diagnostic support means that analyzes the medical questionnaire information entered by the patient and determines the urgency of the situation; and a health care navigation means that centrally manages the hospital's reservation status and recommends the most suitable medical service to the patient. This enables the provision of efficient medical services to patients and allows for rapid responses both inside and outside the hospital.

[0470] "Patient appointment status" refers to information that shows the status of appointments made at a hospital based on the patient's preferred date and time for treatment.

[0471] "Real-time monitoring" is the process of instantly monitoring constantly changing information and always being aware of the latest status.

[0472] "Medical treatment progress information" refers to information that indicates the stage of treatment within the hospital.

[0473] "Adjusting the timing of hospital visits" refers to the management and settings required to ensure that patients visit the hospital at the appropriate time.

[0474] "Diagnostic support tools" are functions that analyze medical information collected from patients and assist in the diagnostic process.

[0475] "Assessing the severity of the situation" is the process of evaluating the severity and urgency of a patient's symptoms.

[0476] "Centralized management" refers to the comprehensive management and centralized processing of multiple data and pieces of information.

[0477] "Health Care Navigation" is a system that provides information and recommendations to help patients access the most appropriate medical services.

[0478] "Waiting time prediction" refers to predicting the waiting time before a consultation begins and providing this information to the patient.

[0479] "Off-site waiting options" refer to the option of providing patients with waiting locations or services available outside the hospital.

[0480] "Follow-up information" refers to information used to monitor the progress of treatment after a medical examination.

[0481] "Automated next appointment scheduling" refers to the system automatically scheduling the next appointment after the current consultation.

[0482] To implement this invention, a system is built in which a server, a terminal, and a user work together. The server requires dedicated software to monitor patient appointment status in real time and track the progress of medical treatment. This software is developed using programming languages ​​such as Python or Java and works in conjunction with a database to manage appointment information and treatment progress.

[0483] The terminal is a computer device such as a smartphone or tablet, and it provides an interface for patients to make appointments and check their appointment schedules. Furthermore, the terminal receives reminder notifications sent from the server and functions as a tool for patients to reconfirm their appointment details themselves as needed.

[0484] The user is a patient who uses a terminal to access the hospital's appointment system and enter the necessary information. The server uses this information to provide diagnostic support and determine the urgency of the situation. Depending on the diagnosis, a video call can be arranged if necessary.

[0485] As a concrete example, in a hospital system in a certain region, when a patient makes an appointment via smartphone, the server updates the appointment status in real time and automatically sends a reminder as the appointment date approaches. Upon receiving this reminder, the patient can check the appointment date and time and make adjustments as needed.

[0486] An example of a prompt message generated using an AI model is: "Please provide the information needed to make a medical appointment. For example, I would like to know if I can go to a local hospital and what time slots are available for appointments."

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] Users make appointments using a terminal. The input includes the user's desired appointment date and time, and the medical department. The terminal sends this data to the server. The server consults a database to check available time slots and doctor schedules, and generates a response prompting the user to confirm or re-select the appointment. The output is either the confirmed appointment information or a list of time slots requiring selection.

[0490] Step 2:

[0491] The server generates a reminder notification as the appointment date approaches, based on the reservation information. The input is the reservation date and time retrieved from the database. The server uses this information to create a reminder notification based on the specified date and time and sends it to the device. The output is the reminder notification displayed on the device.

[0492] Step 3:

[0493] The user completes an online medical questionnaire from their device. The input consists of the user's medical information and a description of their symptoms. The device sends this information to a server, which analyzes the data through a diagnostic support system to determine the urgency of the situation. Based on the diagnosis, a video call with a doctor may be recommended in some cases. The output includes an assessment of the urgency of the situation and recommended actions.

[0494] Step 4:

[0495] When a user arrives at the hospital, the server estimates the waiting time and sends a notification to the terminal. The input is data on the progress of consultations within the hospital. The server analyzes this information and performs calculations to estimate the waiting time. The output is an estimate of the user's acceptable waiting time and guidance on external waiting options if necessary.

[0496] Step 5:

[0497] After the consultation, the server automatically generates follow-up information and schedules the next appointment. Inputs include the final diagnosis and the patient's schedule. Based on this, the server suggests the next appointment and sends a follow-up notification to the terminal. Outputs include detailed information about the next appointment and follow-up recommendations.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention is an information system designed to enable patients to receive medical care without stress during the treatment process in a medical setting. This system can not only consistently manage the user (patient) from appointment scheduling to post-treatment care, but can also recognize the user's emotions and customize the service content accordingly.

[0500] First, the user makes an appointment using their device. The server receives this appointment information and monitors its progress in real time. As the appointment date approaches, the server automatically generates a reminder and sends a notification to the device. This allows the user to reconfirm their appointment and prepare for their visit with ample time.

[0501] Before coming to the clinic, users complete an online questionnaire via their device and send information about their symptoms to the server. The server analyzes this data, determines the urgency of the situation, and adjusts the priority of treatment accordingly. It also uses an emotion engine to recognize emotions from the user's input data and provides psychological support as needed. In this case, content promoting relaxation may be displayed on the device.

[0502] On the day of the appointment, when the user arrives at the hospital, the server monitors the progress of the consultation and calculates the waiting time. Here too, the emotion engine is used, and based on the user's emotional state, appropriate entertainment and relaxation options are suggested to the device while they wait.

[0503] Once the consultation is complete, the server sends the consultation results to the terminal as follow-up information and automatically schedules the next appointment. Based on the results of the emotion engine, a follow-up method tailored to the user's physical and mental state is recommended. This makes it easier for users to manage their own health. In this way, this system enhances patient satisfaction throughout the entire medical service and improves the operational efficiency of medical institutions.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The user uses a terminal to enter the desired medical department and date / time for their appointment and sends it to the server. The server stores the received appointment information in a database and monitors the appointment status in real time.

[0507] Step 2:

[0508] The server automatically generates a reminder the day before the reservation date. The server sends this reminder to the user's device, and the user confirms the reservation upon receiving it.

[0509] Step 3:

[0510] Users use their devices to answer online questionnaires and send information about their symptoms and current health status to the server. The server analyzes this information and determines the urgency of the situation.

[0511] Step 4:

[0512] Based on the diagnostic results, the server uses an emotion engine to recognize the user's emotions. For example, if it determines that the user is feeling stressed, it will display relaxation-promoting content on the device.

[0513] Step 5:

[0514] When a user arrives at the hospital, the server monitors the progress of consultations within the hospital. The server predicts the waiting time and notifies the user of this information. Based on the user's emotional response, it suggests appropriate ways to spend the waiting time.

[0515] Step 6:

[0516] After the consultation is complete, the server collects the consultation results and automatically schedules the next appointment. In addition, the server generates follow-up information and notifies the user via their device.

[0517] Step 7:

[0518] Based on the analysis results of the emotion engine, the device suggests appropriate follow-up actions to the user. The user can then review this information and take the necessary actions to manage their own health.

[0519] (Example 2)

[0520] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0521] In the medical treatment process at healthcare facilities, there are problems that make it difficult for patients to receive treatment smoothly and comfortably. Specifically, there is a lack of support that takes into account the patient's psychological state at each stage, from making an appointment to the consultation and post-consultation follow-up. In addition, the lack of information provided regarding the progress of treatment and waiting times is a factor that causes anxiety and dissatisfaction among patients.

[0522] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0523] In this invention, the server includes a reservation management means for monitoring the patient's appointment status in real time and adjusting the timing of their visit; a diagnostic support means for analyzing the patient's entered medical information and determining the urgency of the situation; and an emotion analysis means for analyzing the patient's emotional state and providing psychological support. This enables medical support tailored to the patient's psychological state and improves patient satisfaction throughout the entire medical process.

[0524] A "reservation management system" is a system or program that has the function of monitoring a patient's reservation status in real time and adjusting the optimal timing of their visit based on information on the progress of their medical treatment.

[0525] A "diagnostic support tool" is a system or program that analyzes patient-provided medical information and determines the urgency of symptoms based on that data, thereby prioritizing appropriate medical treatment.

[0526] A "waiting time management system" is a system or program that predicts waiting times based on the progress of medical examinations within a hospital and provides appropriate notifications to patients.

[0527] A "follow-up method" refers to a system or program that provides follow-up information to patients after a medical consultation and has the function of automating the scheduling of the next appointment.

[0528] An "emotional analysis tool" is a system or program that analyzes patient input data, understands the patient's emotional state based on the analysis results, and provides psychological support.

[0529] A "reminder mechanism" is a system or program that has the function of sending a notification to the user's terminal in advance regarding a scheduled medical appointment and requesting confirmation of the appointment details.

[0530] A "customized information provision method" refers to a system or program that has the function of providing personalized medical and health information to patients, taking into account medical interview data and the patient's emotional state.

[0531] This invention is an information system that consistently manages the patient's treatment process in a medical institution and provides support that takes into account the patient's psychological state. This system provides various functions related to appointment management, diagnostic support, waiting time management, follow-up, and emotion analysis.

[0532] Users access the medical institution's reservation system using a device (e.g., smartphone or computer). They enter their desired appointment date and symptoms into a reservation form and submit it, which transmits the reservation information to the server. The server is hosted on the cloud and stores the reservation information in a database (e.g., MySQL). Using programming languages ​​such as Python or Java, the server monitors the reservation status in real time and adjusts the timing of appointments based on the reservations.

[0533] Before visiting the clinic, users complete an online questionnaire and send the entered data to the server via their device. The server uses machine learning libraries (e.g., scikit-learn and TensorFlow) to analyze the questionnaire information and assess the urgency of the patient's visit. Simultaneously, an emotion engine recognizes the user's emotions from their input data and provides psychological support as needed. Relaxation-enhancing content may also be displayed on the device.

[0534] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation and notifies the user of the estimated waiting time on their device. During the waiting time, entertainment and relaxation options are suggested, taking into account the patient's emotional state. After the consultation, the results are provided as follow-up information, and the next appointment is automatically scheduled.

[0535] For example, if the emotion engine detects an anxious state when a user makes a medical appointment, the server will display a message on the device such as, "Music to encourage deep breathing is available. Listen and relax."

[0536] An example of a prompt message is: "User is feeling anxious about the upcoming doctor's appointment. Suggest relaxing activities or content to help alleviate stress."

[0537] In this way, the present invention aims to improve the overall efficiency of medical care within healthcare institutions while simultaneously enhancing patient satisfaction.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] Users access the reservation system using their terminal and enter their desired appointment date and symptoms. The entered information (date, time, symptoms, etc.) is sent to the server via the reservation form. The server stores the received reservation information in a database, allowing for real-time monitoring of the reservation progress.

[0541] Step 2:

[0542] As the appointment date approaches, the server generates a reminder based on the appointment information. This involves checking the configured schedule record and using a reminder template to send the reminder as an email or push notification. The server then sends this reminder information to the user's device to prompt confirmation.

[0543] Step 3:

[0544] Before coming to the clinic, users complete an online questionnaire via their device. They enter their symptoms, medical history, lifestyle, etc., into the questionnaire form and submit it. The server receives this data, which is then analyzed using machine learning algorithms to determine the urgency of the situation. This allows for the calculation of treatment priority.

[0545] Step 4:

[0546] The server inputs the received questionnaire data into an emotion engine and uses natural language processing technology to recognize the user's emotions. Based on the results of the emotion analysis, if it is determined that psychological support is needed, the server selects content and support messages that promote relaxation and presents them to the device.

[0547] Step 5:

[0548] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation in real time. It retrieves consultation progress data and calculates the waiting time. Based on the calculated waiting time, the server sends a notification to the user's device, providing the total waiting time and progress information.

[0549] Step 6:

[0550] While waiting, the server generates prompts from an AI model based on the user's emotion analysis results, suggesting entertainment and relaxation options to the device. Specifically, it selects and delivers content such as videos and music that the user can relax with.

[0551] Step 7:

[0552] After the consultation is complete, the server summarizes the results and generates follow-up information. This information, including scheduling the next appointment, is automatically sent to the user's device. The follow-up content is personalized based on the results of the sentiment analysis and provided as health management advice.

[0553] (Application Example 2)

[0554] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] In current long-term care services, appointment management and waiting time adjustments for users are inadequate, often causing anxiety and stress for users. Furthermore, insufficient follow-up care results in inadequate health management for users. Additionally, a lack of responses to emotional changes negatively impacts the quality of service. These issues need to be resolved.

[0556] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0557] In this invention, the server includes reservation management means that monitors the user's reservation status in real time and adjusts the timing of visits based on care progress information; diagnostic support means that analyzes the medical questionnaire information entered by the user and determines the urgency; and emotional support means that evaluates the user's psychological state using emotion analysis technology and provides relaxation content. This makes it possible to reduce user stress and improve the overall quality of the service.

[0558] A "reservation management system" is a function that monitors the user's reservation status in real time and adjusts the timing of visits based on information about the progress of care.

[0559] A "diagnostic support tool" is an analytical function that determines the urgency of a patient's condition based on the medical information entered by the user.

[0560] The "waiting time management method" is a function that predicts the waiting time for home care visits and provides notifications, including options for waiting outside the home.

[0561] "Follow-up methods" refer to functions that provide follow-up information after care and automate the scheduling of the next appointment.

[0562] "Emotional support measures" refer to functions that use emotion analysis technology to evaluate the user's psychological state and provide relaxation content.

[0563] The system for realizing this invention is built using a cloud server, a user terminal (such as a smartphone), and a software platform equipped with emotion analysis technology. The cloud server receives reservation information and medical questionnaire data, and monitors and analyzes it in real time. The user terminal displays notifications and relaxation content based on the received information and is responsible for interacting with the user.

[0564] The server first checks the reservation status from users in real time and adjusts the timing of care provision based on the progress information. During this process, it utilizes cloud services to process data and feeds the analysis results back to the user's terminal in real time. Next, the server analyzes the medical history data to determine the urgency level. Based on this determination, it dynamically adjusts priorities and modifies the care content as needed.

[0565] As a means of emotional support, the server uses facial recognition and natural language processing technologies to evaluate the user's emotional state. Specifically, it analyzes facial expressions using technologies such as Amazon Rekognition and determines emotions from text using Google Cloud Natural Language. Based on the analysis results, appropriate relaxation content is sent to the user's device, and the user can use it to reduce stress.

[0566] For example, if a user makes a home care appointment and there is questionnaire data indicating signs of stress, the device will display "To help alleviate your anxiety, we suggest some relaxing music," and play meditation music. An example of a specific prompt might be, "Based on the user's emotional analysis, please suggest content to reduce stress. The current emotional state is 'anxious'."

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] The server receives reservation information from the user's terminal. The input includes the user's reservation date and time and care details. The server saves this information to a cloud database and prepares for real-time monitoring. The output is a reservation confirmation notification.

[0570] Step 2:

[0571] When a user completes an online medical questionnaire on their device, the data is sent to the server. The input consists of information about the user's health status and symptoms, and the server analyzes this data to determine the urgency of the situation. The output is the result of the urgency determination.

[0572] Step 3:

[0573] The server analyzes the user's emotions using facial recognition APIs and natural language processing tools. Input consists of facial expression data and questionnaire text provided by the terminal; the server analyzes this data and performs a psychological assessment. The output is the analyzed emotional data.

[0574] Step 4:

[0575] Based on the analyzed emotional data, the server provides appropriate relaxation content to the user's terminal. The input is emotional data, and the output is the selection and transmission of relaxation content. In this process, content such as music and videos are suggested.

[0576] Step 5:

[0577] The server sends a reminder notification to the user's terminal as the scheduled date and time for the home care visit approaches. The input is the reservation information, and the output is the reminder notification. This allows the user to prepare for the visit.

[0578] Step 6:

[0579] After care, the server sends follow-up information on the care results to the user's terminal and automates the scheduling of the next appointment. The input is care result data, and the output is follow-up information and a suggestion for the next appointment. This allows the user to continue managing their health.

[0580] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0581] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0582] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0586] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0587] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0588] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0589] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0590] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0591] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0592] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0593] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0594] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0595] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0596] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] This invention provides an advanced information system for improving the efficiency of medical treatment processes in hospitals. In this embodiment, the system primarily operates around three components: a server, a terminal, and a user, each playing its own role.

[0598] First, the user makes a reservation via their device. The server receives this reservation, monitors the reservation status in real time using a reservation management system, and stores it in a database. For example, if a patient requests an internal medicine appointment for a specific date and time, the server checks the information and updates the schedule accordingly.

[0599] As the appointment date approaches, the server sends a reminder notification to the user's terminal, prompting the user to confirm. The user receives the notification and can reconfirm the appointment if necessary.

[0600] In the online consultation conducted before visiting the clinic, users input their symptoms from their device and send them to the server. The server's diagnostic support system analyzes this data to determine the urgency of the situation. For example, if it is determined that the situation is not urgent, the device will be notified of what to do at home and, in some cases, a video call with a doctor may be recommended.

[0601] When a user arrives at the hospital, a waiting time management system is activated. The server retrieves the progress of consultations within the hospital from a database and estimates the waiting time. It then notifies the user's terminal of the estimated waiting time and provides options for leaving the hospital if a long wait is necessary. At this time, information such as nearby cafes may also be provided.

[0602] After the consultation, a follow-up system is activated. The server sends the consultation results and follow-up information to the terminal and automatically schedules the next appointment. The user can then review this and plan their next visit. This series of operations can reduce the burden on patients and significantly improve the operational efficiency of healthcare facilities.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user requests an appointment using a terminal. The user enters the desired medical department and date / time, and sends this information to the server via the terminal.

[0606] Step 2:

[0607] The server records the received reservation request in its database and checks the availability of the reservation using the reservation management system. If the reservation is confirmed, the server notifies the terminal of this information.

[0608] Step 3:

[0609] As the appointment date approaches, the server automatically generates a reminder notification. The server sends the reminder to the user's device, notifying them to reconfirm their appointment.

[0610] Step 4:

[0611] Users complete an online medical questionnaire. They input their symptoms via their device and send the questionnaire data to a server. The input is then analyzed by diagnostic support tools.

[0612] Step 5:

[0613] The server determines the urgency of the situation based on the medical questionnaire information. The server sends the determination result to the terminal and displays it to the user. It also provides guidance on how to handle the situation at home and how to schedule a video call with a doctor, as needed.

[0614] Step 6:

[0615] After the patient arrives at the hospital, the server monitors the progress of consultations within the hospital in real time and estimates the waiting time. The server uses a waiting time management system to notify the terminal of the predicted waiting time and the option of leaving the hospital.

[0616] Step 7:

[0617] Once the consultation is complete, the server processes the results using a follow-up system. The server automatically schedules the next appointment and sends that information to the terminal.

[0618] Step 8:

[0619] The device notifies the user of their next appointment information and detailed medical results, allowing them to review the follow-up plan. After receiving the notification, the user can prepare for their next appointment.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In modern healthcare settings, patient appointment management, optimization of waiting times for consultations, and post-consultation follow-up are often not carried out efficiently. This impairs patient convenience and reduces the operational efficiency of healthcare institutions. To solve these problems, it is necessary to automate these processes and provide more advanced information management tools.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes: appointment management means for monitoring patient appointment status in real time and adjusting the timing of patient visits based on information on the progress of medical care provision; diagnostic support means for analyzing patient-entered medical information and determining the urgency of the situation; and waiting time management means for predicting waiting times based on the progress of consultations within the medical institution and providing notifications, including options for waiting outside. This improves patient convenience and optimizes the operational efficiency of medical institutions.

[0625] A "reservation management system" is a function that receives users' reservation information, monitors it in real time, and adjusts the timing of their visits based on information about the progress of medical care provision.

[0626] A "diagnostic support tool" is a function that analyzes the medical information entered by the patient and determines the urgency of the symptoms based on that data.

[0627] A "waiting time management system" is a function that estimates waiting times based on the progress of consultations within a medical institution and provides notifications, including external waiting options, as needed.

[0628] "Follow-up services" refer to functions that provide follow-up information after medical treatment, automate the scheduling of the next appointment, and facilitate the patient's next visit.

[0629] A "reminder mechanism" is a function that sends a medical appointment reminder notification to the user's device and asks the user to confirm it.

[0630] "Communication methods" refers to functions that allow for visual communication with healthcare professionals, such as video calls, based on the patient's medical history data.

[0631] This invention is an advanced information system aimed at improving the efficiency of medical treatment processes in hospitals, and it primarily functions through three parties: a server, a terminal, and a user.

[0632] First, the user makes a hospital appointment using a device. This device is a computer such as a PC or smartphone, and requires an internet connection to access the hospital's appointment system. The appointment information entered by the user is sent to a server via the internet. The server receives this information and updates the schedule in real time using dedicated appointment management software. Specifically, this involves data processing such as storing appointment information in a database, calculating available time slots, and updating appointment status.

[0633] As the appointment date approaches, the server sends a reminder notification to the user's device. This notification is sent via email or app notification, prompting the user to confirm the appointment date, time, and location. Such a reminder system can utilize notification tools developed using Python, JavaScript, or other languages.

[0634] Users can also complete an online questionnaire before coming to the clinic. This is done via a dedicated questionnaire application that runs on the user's device. The user enters their symptoms and sends them to the server, which analyzes them using diagnostic support software to determine the urgency. If a serious case is detected, a video call system (e.g., Zoom or WebRTC) is activated using the communication method to immediately suggest a video call with a doctor.

[0635] When a user arrives at the hospital, the server retrieves consultation progress data from a database and analyzes it using waiting time management software. Based on this data, it predicts waiting times and notifies the user's terminal with information including options for leaving the hospital. The notification function can also be linked with a map application for user convenience.

[0636] After the consultation, the server uses a follow-up system to send the consultation results and next appointment information to the user. This automatically schedules the next appointment, and the user can review it and adjust the plan accordingly.

[0637] As a concrete example, the following is an example of a prompt sentence to be input to the generating AI model: "Please describe the procedure for updating the appointment status in the medical appointment system and sending a notification to the patient." By using this prompt, the effectiveness of the invention can be maximized, and the entire medical process can be carried out smoothly.

[0638] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0639] Step 1:

[0640] The user enters their hospital appointment information using a terminal. They enter the necessary information (e.g., department, date and time, patient information) into the input form on the terminal and press the submit button to send the information to the server.

[0641] Step 2:

[0642] The server analyzes the reservation information received from the terminal. It accesses the database and checks availability by comparing it with the current reservation status. It adjusts reservations as needed and updates the database with new schedules. At this time, new reservation information is created in the database and the availability is updated.

[0643] Step 3:

[0644] Once the reservation is complete, the server sends confirmation information to the user's terminal. The user receives this confirmation notification and can reconfirm the reservation details. The notification includes reservation details (e.g., date and time, department, doctor's name).

[0645] Step 4:

[0646] As the appointment date approaches, the server sends a reminder notification to the user's device. The reminder is automatically generated at the specified date and time and sent to the user via email or app notification. Here, the notification information based on the schedule is output.

[0647] Step 5:

[0648] Before coming to the clinic, users complete an online medical questionnaire via their device. They enter their symptoms and health status into the questionnaire form and submit it to the server. Based on the input data, the server analyzes the symptoms using diagnostic support tools and determines the urgency of the situation. The analysis results are output directly and influence the next step in treatment.

[0649] Step 6:

[0650] Upon arrival at the hospital, the server estimates the waiting time based on the progress of the consultation. The server retrieves consultation progress data from the database and calculates the waiting time using a waiting time management system. The results are notified to the terminal, and suggestions for how to spend time outside the hospital (e.g., information on nearby facilities) are made as needed.

[0651] Step 7:

[0652] After the consultation, the server sends follow-up information to the user's terminal. It automatically generates and notifies the user of information including the consultation results, next appointment scheduling, and a self-follow-up guide. This allows the user to plan their next visit.

[0653] Through these steps, users can have a smoother consultation experience, and healthcare institutions can achieve more efficient operations.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] In urban hospitals, the complexity of appointment management, which ensures patients receive appropriate medical care, is leading to decreased efficiency. Furthermore, patients are unable to effectively utilize their waiting time at hospitals, resulting in a lack of smooth medical delivery. These problems not only impair patient convenience but also reduce the operational efficiency of healthcare institutions. Solutions are needed to improve this situation.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes a reservation management means that monitors the patient's reservation status in real time and adjusts the timing of their visit based on the progress of their treatment; a diagnostic support means that analyzes the medical questionnaire information entered by the patient and determines the urgency of the situation; and a health care navigation means that centrally manages the hospital's reservation status and recommends the most suitable medical service to the patient. This enables the provision of efficient medical services to patients and allows for rapid responses both inside and outside the hospital.

[0659] "Patient appointment status" refers to information that shows the status of appointments made at a hospital based on the patient's preferred date and time for treatment.

[0660] "Real-time monitoring" is the process of instantly monitoring constantly changing information and always being aware of the latest status.

[0661] "Medical treatment progress information" refers to information that indicates the stage of treatment within the hospital.

[0662] "Adjusting the timing of hospital visits" refers to the management and settings required to ensure that patients visit the hospital at the appropriate time.

[0663] "Diagnostic support tools" are functions that analyze medical information collected from patients and assist in the diagnostic process.

[0664] "Assessing the severity of the situation" is the process of evaluating the severity and urgency of a patient's symptoms.

[0665] "Centralized management" refers to the comprehensive management and centralized processing of multiple data and pieces of information.

[0666] "Health Care Navigation" is a system that provides information and recommendations to help patients access the most appropriate medical services.

[0667] "Waiting time prediction" refers to predicting the waiting time before a consultation begins and providing this information to the patient.

[0668] "Off-site waiting options" refer to the option of providing patients with waiting locations or services available outside the hospital.

[0669] "Follow-up information" refers to information used to monitor the progress of treatment after a medical examination.

[0670] "Automated next appointment scheduling" refers to the system automatically scheduling the next appointment after the current consultation.

[0671] To implement this invention, a system is built in which a server, a terminal, and a user work together. The server requires dedicated software to monitor patient appointment status in real time and track the progress of medical treatment. This software is developed using programming languages ​​such as Python or Java and works in conjunction with a database to manage appointment information and treatment progress.

[0672] The terminal is a computer device such as a smartphone or tablet, and it provides an interface for patients to make appointments and check their appointment schedules. Furthermore, the terminal receives reminder notifications sent from the server and functions as a tool for patients to reconfirm their appointment details themselves as needed.

[0673] The user is a patient who uses a terminal to access the hospital's appointment system and enter the necessary information. The server uses this information to provide diagnostic support and determine the urgency of the situation. Depending on the diagnosis, a video call can be arranged if necessary.

[0674] As a concrete example, in a hospital system in a certain region, when a patient makes an appointment via smartphone, the server updates the appointment status in real time and automatically sends a reminder as the appointment date approaches. Upon receiving this reminder, the patient can check the appointment date and time and make adjustments as needed.

[0675] An example of a prompt message generated using an AI model is: "Please provide the information needed to make a medical appointment. For example, I would like to know if I can go to a local hospital and what time slots are available for appointments."

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] Users make appointments using a terminal. The input includes the user's desired appointment date and time, and the medical department. The terminal sends this data to the server. The server consults a database to check available time slots and doctor schedules, and generates a response prompting the user to confirm or re-select the appointment. The output is either the confirmed appointment information or a list of time slots requiring selection.

[0679] Step 2:

[0680] The server generates a reminder notification as the appointment date approaches, based on the reservation information. The input is the reservation date and time retrieved from the database. The server uses this information to create a reminder notification based on the specified date and time and sends it to the device. The output is the reminder notification displayed on the device.

[0681] Step 3:

[0682] The user completes an online medical questionnaire from their device. The input consists of the user's medical information and a description of their symptoms. The device sends this information to a server, which analyzes the data through a diagnostic support system to determine the urgency of the situation. Based on the diagnosis, a video call with a doctor may be recommended in some cases. The output includes an assessment of the urgency of the situation and recommended actions.

[0683] Step 4:

[0684] When a user arrives at the hospital, the server estimates the waiting time and sends a notification to the terminal. The input is data on the progress of consultations within the hospital. The server analyzes this information and performs calculations to estimate the waiting time. The output is an estimate of the user's acceptable waiting time and guidance on external waiting options if necessary.

[0685] Step 5:

[0686] After the consultation, the server automatically generates follow-up information and schedules the next appointment. Inputs include the final diagnosis and the patient's schedule. Based on this, the server suggests the next appointment and sends a follow-up notification to the terminal. Outputs include detailed information about the next appointment and follow-up recommendations.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] This invention is an information system designed to enable patients to receive medical care without stress during the treatment process in a medical setting. This system can not only consistently manage the user (patient) from appointment scheduling to post-treatment care, but can also recognize the user's emotions and customize the service content accordingly.

[0689] First, the user makes an appointment using their device. The server receives this appointment information and monitors its progress in real time. As the appointment date approaches, the server automatically generates a reminder and sends a notification to the device. This allows the user to reconfirm their appointment and prepare for their visit with ample time.

[0690] Before coming to the clinic, users complete an online questionnaire via their device and send information about their symptoms to the server. The server analyzes this data, determines the urgency of the situation, and adjusts the priority of treatment accordingly. It also uses an emotion engine to recognize emotions from the user's input data and provides psychological support as needed. In this case, content promoting relaxation may be displayed on the device.

[0691] On the day of the appointment, when the user arrives at the hospital, the server monitors the progress of the consultation and calculates the waiting time. Here too, the emotion engine is used, and based on the user's emotional state, appropriate entertainment and relaxation options are suggested to the device while they wait.

[0692] Once the consultation is complete, the server sends the consultation results to the terminal as follow-up information and automatically schedules the next appointment. Based on the results of the emotion engine, a follow-up method tailored to the user's physical and mental state is recommended. This makes it easier for users to manage their own health. In this way, this system enhances patient satisfaction throughout the entire medical service and improves the operational efficiency of medical institutions.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The user uses a terminal to enter the desired medical department and date / time for their appointment and sends it to the server. The server stores the received appointment information in a database and monitors the appointment status in real time.

[0696] Step 2:

[0697] The server automatically generates a reminder the day before the reservation date. The server sends this reminder to the user's device, and the user confirms the reservation upon receiving it.

[0698] Step 3:

[0699] Users use their devices to answer online questionnaires and send information about their symptoms and current health status to the server. The server analyzes this information and determines the urgency of the situation.

[0700] Step 4:

[0701] Based on the diagnostic results, the server uses an emotion engine to recognize the user's emotions. For example, if it determines that the user is feeling stressed, it will display relaxation-promoting content on the device.

[0702] Step 5:

[0703] When a user arrives at the hospital, the server monitors the progress of consultations within the hospital. The server predicts the waiting time and notifies the user of this information. Based on the user's emotional response, it suggests appropriate ways to spend the waiting time.

[0704] Step 6:

[0705] After the consultation is complete, the server collects the consultation results and automatically schedules the next appointment. In addition, the server generates follow-up information and notifies the user via their device.

[0706] Step 7:

[0707] Based on the analysis results of the emotion engine, the device suggests appropriate follow-up actions to the user. The user can then review this information and take the necessary actions to manage their own health.

[0708] (Example 2)

[0709] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0710] In the medical treatment process at healthcare facilities, there are problems that make it difficult for patients to receive treatment smoothly and comfortably. Specifically, there is a lack of support that takes into account the patient's psychological state at each stage, from making an appointment to the consultation and post-consultation follow-up. In addition, the lack of information provided regarding the progress of treatment and waiting times is a factor that causes anxiety and dissatisfaction among patients.

[0711] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0712] In this invention, the server includes a reservation management means for monitoring the patient's appointment status in real time and adjusting the timing of their visit; a diagnostic support means for analyzing the patient's entered medical information and determining the urgency of the situation; and an emotion analysis means for analyzing the patient's emotional state and providing psychological support. This enables medical support tailored to the patient's psychological state and improves patient satisfaction throughout the entire medical process.

[0713] A "reservation management system" is a system or program that has the function of monitoring a patient's reservation status in real time and adjusting the optimal timing of their visit based on information on the progress of their medical treatment.

[0714] A "diagnostic support tool" is a system or program that analyzes patient-provided medical information and determines the urgency of symptoms based on that data, thereby prioritizing appropriate medical treatment.

[0715] A "waiting time management system" is a system or program that predicts waiting times based on the progress of medical examinations within a hospital and provides appropriate notifications to patients.

[0716] A "follow-up method" refers to a system or program that provides follow-up information to patients after a medical consultation and has the function of automating the scheduling of the next appointment.

[0717] An "emotional analysis tool" is a system or program that analyzes patient input data, understands the patient's emotional state based on the analysis results, and provides psychological support.

[0718] A "reminder mechanism" is a system or program that has the function of sending a notification to the user's terminal in advance regarding a scheduled medical appointment and requesting confirmation of the appointment details.

[0719] A "customized information provision method" refers to a system or program that has the function of providing personalized medical and health information to patients, taking into account medical interview data and the patient's emotional state.

[0720] This invention is an information system that consistently manages the patient's treatment process in a medical institution and provides support that takes into account the patient's psychological state. This system provides various functions related to appointment management, diagnostic support, waiting time management, follow-up, and emotion analysis.

[0721] Users access the medical institution's reservation system using a device (e.g., smartphone or computer). They enter their desired appointment date and symptoms into a reservation form and submit it, which transmits the reservation information to the server. The server is hosted on the cloud and stores the reservation information in a database (e.g., MySQL). Using programming languages ​​such as Python or Java, the server monitors the reservation status in real time and adjusts the timing of appointments based on the reservations.

[0722] Before visiting the clinic, users complete an online questionnaire and send the entered data to the server via their device. The server uses machine learning libraries (e.g., scikit-learn and TensorFlow) to analyze the questionnaire information and assess the urgency of the patient's visit. Simultaneously, an emotion engine recognizes the user's emotions from their input data and provides psychological support as needed. Relaxation-enhancing content may also be displayed on the device.

[0723] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation and notifies the user of the estimated waiting time on their device. During the waiting time, entertainment and relaxation options are suggested, taking into account the patient's emotional state. After the consultation, the results are provided as follow-up information, and the next appointment is automatically scheduled.

[0724] For example, if the emotion engine detects an anxious state when a user makes a medical appointment, the server will display a message on the device such as, "Music to encourage deep breathing is available. Listen and relax."

[0725] An example of a prompt message is: "User is feeling anxious about the upcoming doctor's appointment. Suggest relaxing activities or content to help alleviate stress."

[0726] In this way, the present invention aims to improve the overall efficiency of medical care within healthcare institutions while simultaneously enhancing patient satisfaction.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] Users access the reservation system using their terminal and enter their desired appointment date and symptoms. The entered information (date, time, symptoms, etc.) is sent to the server via the reservation form. The server stores the received reservation information in a database, allowing for real-time monitoring of the reservation progress.

[0730] Step 2:

[0731] As the appointment date approaches, the server generates a reminder based on the appointment information. This involves checking the configured schedule record and using a reminder template to send the reminder as an email or push notification. The server then sends this reminder information to the user's device to prompt confirmation.

[0732] Step 3:

[0733] Before coming to the clinic, users complete an online questionnaire via their device. They enter their symptoms, medical history, lifestyle, etc., into the questionnaire form and submit it. The server receives this data, which is then analyzed using machine learning algorithms to determine the urgency of the situation. This allows for the calculation of treatment priority.

[0734] Step 4:

[0735] The server inputs the received questionnaire data into an emotion engine and uses natural language processing technology to recognize the user's emotions. Based on the results of the emotion analysis, if it is determined that psychological support is needed, the server selects content and support messages that promote relaxation and presents them to the device.

[0736] Step 5:

[0737] When a user arrives at the hospital on the day of their appointment, the server monitors the progress of the consultation in real time. It retrieves consultation progress data and calculates the waiting time. Based on the calculated waiting time, the server sends a notification to the user's device, providing the total waiting time and progress information.

[0738] Step 6:

[0739] While waiting, the server generates prompts from an AI model based on the user's emotion analysis results, suggesting entertainment and relaxation options to the device. Specifically, it selects and delivers content such as videos and music that the user can relax with.

[0740] Step 7:

[0741] After the consultation is complete, the server summarizes the results and generates follow-up information. This information, including scheduling the next appointment, is automatically sent to the user's device. The follow-up content is personalized based on the results of the sentiment analysis and provided as health management advice.

[0742] (Application Example 2)

[0743] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0744] In current long-term care services, appointment management and waiting time adjustments for users are inadequate, often causing anxiety and stress for users. Furthermore, insufficient follow-up care results in inadequate health management for users. Additionally, a lack of responses to emotional changes negatively impacts the quality of service. These issues need to be resolved.

[0745] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0746] In this invention, the server includes reservation management means that monitors the user's reservation status in real time and adjusts the timing of visits based on care progress information; diagnostic support means that analyzes the medical questionnaire information entered by the user and determines the urgency; and emotional support means that evaluates the user's psychological state using emotion analysis technology and provides relaxation content. This makes it possible to reduce user stress and improve the overall quality of the service.

[0747] A "reservation management system" is a function that monitors the user's reservation status in real time and adjusts the timing of visits based on information about the progress of care.

[0748] A "diagnostic support tool" is an analytical function that determines the urgency of a patient's condition based on the medical information entered by the user.

[0749] The "waiting time management method" is a function that predicts the waiting time for home care visits and provides notifications, including options for waiting outside the home.

[0750] "Follow-up methods" refer to functions that provide follow-up information after care and automate the scheduling of the next appointment.

[0751] "Emotional support measures" refer to functions that use emotion analysis technology to evaluate the user's psychological state and provide relaxation content.

[0752] The system for realizing this invention is built using a cloud server, a user terminal (such as a smartphone), and a software platform equipped with emotion analysis technology. The cloud server receives reservation information and medical questionnaire data, and monitors and analyzes it in real time. The user terminal displays notifications and relaxation content based on the received information and is responsible for interacting with the user.

[0753] The server first checks the reservation status from users in real time and adjusts the timing of care provision based on the progress information. During this process, it utilizes cloud services to process data and feeds the analysis results back to the user's terminal in real time. Next, the server analyzes the medical history data to determine the urgency level. Based on this determination, it dynamically adjusts priorities and modifies the care content as needed.

[0754] As a means of emotional support, the server uses facial recognition and natural language processing technologies to evaluate the user's emotional state. Specifically, it analyzes facial expressions using technologies such as Amazon Rekognition and determines emotions from text using Google Cloud Natural Language. Based on the analysis results, appropriate relaxation content is sent to the user's device, and the user can use it to reduce stress.

[0755] For example, if a user makes a home care appointment and there is questionnaire data indicating signs of stress, the device will display "To help alleviate your anxiety, we suggest some relaxing music," and play meditation music. An example of a specific prompt might be, "Based on the user's emotional analysis, please suggest content to reduce stress. The current emotional state is 'anxious'."

[0756] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0757] Step 1:

[0758] The server receives reservation information from the user's terminal. The input includes the user's reservation date and time and care details. The server saves this information to a cloud database and prepares for real-time monitoring. The output is a reservation confirmation notification.

[0759] Step 2:

[0760] When a user completes an online medical questionnaire on their device, the data is sent to the server. The input consists of information about the user's health status and symptoms, and the server analyzes this data to determine the urgency of the situation. The output is the result of the urgency determination.

[0761] Step 3:

[0762] The server analyzes the user's emotions using facial recognition APIs and natural language processing tools. Input consists of facial expression data and questionnaire text provided by the terminal; the server analyzes this data and performs a psychological assessment. The output is the analyzed emotional data.

[0763] Step 4:

[0764] Based on the analyzed emotional data, the server provides appropriate relaxation content to the user's terminal. The input is emotional data, and the output is the selection and transmission of relaxation content. In this process, content such as music and videos are suggested.

[0765] Step 5:

[0766] The server sends a reminder notification to the user's terminal as the scheduled date and time for the home care visit approaches. The input is the reservation information, and the output is the reminder notification. This allows the user to prepare for the visit.

[0767] Step 6:

[0768] After care, the server sends follow-up information on the care results to the user's terminal and automates the scheduling of the next appointment. The input is care result data, and the output is follow-up information and a suggestion for the next appointment. This allows the user to continue managing their health.

[0769] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0770] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0771] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0772] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0773] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0774] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0775] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0776] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0777] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0778] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0779] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0780] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0781] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0782] 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.

[0783] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0784] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0785] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0786] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0787] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0788] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0789] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0790] The following is further disclosed regarding the embodiments described above.

[0791] (Claim 1)

[0792] A reservation management system that monitors patient appointment status in real time and adjusts the timing of visits based on the progress of treatment,

[0793] A diagnostic support system that analyzes patient-entered medical information to determine the urgency of a situation,

[0794] A waiting time management system that predicts waiting times based on the progress of consultations within the hospital and provides notifications including options for waiting outside,

[0795] A follow-up method that provides follow-up information after consultation and automates the next appointment,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, further comprising a reminder means for sending a reminder notification for a scheduled medical appointment to the user's terminal and requesting confirmation.

[0799] (Claim 3)

[0800] The system according to claim 1, comprising a communication means for setting up a video call with a doctor based on medical interview data.

[0801] "Example 1"

[0802] (Claim 1)

[0803] A reservation management system that monitors patient appointment status in real time and adjusts the timing of hospital visits based on information on the progress of medical care delivery,

[0804] A diagnostic support system that analyzes patient-entered medical information to determine the urgency of a situation,

[0805] A waiting time management system that predicts waiting times based on the progress of consultations within the medical institution and provides notifications including external waiting options,

[0806] A follow-up method that provides follow-up information after medical treatment and automates the next appointment,

[0807] A reminder system that sends a medical appointment reminder notification to the user's terminal and requests confirmation,

[0808] A communication method for setting up visual communication with healthcare professionals based on medical interview data,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, comprising means for storing medical appointment information in a database and updating the overall schedule.

[0812] (Claim 3)

[0813] The system according to claim 1, comprising means for providing standby time and external time usage options.

[0814] "Application Example 1"

[0815] (Claim 1)

[0816] A reservation management system that monitors patient appointment status in real time and adjusts the timing of visits based on the progress of treatment,

[0817] A diagnostic support system that analyzes patient-entered medical information to determine the urgency of a situation,

[0818] A waiting time management system that predicts waiting times based on the progress of consultations within the hospital and provides notifications including options for waiting outside,

[0819] A follow-up method that provides follow-up information after consultation and automates the next appointment,

[0820] A health care navigation system that centrally manages hospital appointment status and recommends optimal medical services to patients,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising a reminder means for sending a reminder notification for a scheduled medical appointment to the user's terminal and requesting confirmation.

[0824] (Claim 3)

[0825] The system according to claim 1, comprising a communication means for setting up a video call with a doctor based on medical interview data.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A reservation management system that monitors patient appointment status in real time and adjusts the timing of visits based on the progress of treatment,

[0829] A diagnostic support system that analyzes patient-entered medical information to determine the urgency of a situation,

[0830] A waiting time management system that predicts waiting times based on the progress of consultations within the hospital and provides notifications including options for waiting outside,

[0831] A follow-up method that provides follow-up information after consultation and automates the next appointment,

[0832] An emotion analysis tool that analyzes a patient's emotional state and provides psychological support based on that analysis,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising a reminder means for sending a reminder notification for a scheduled medical appointment to the user's terminal and requesting confirmation.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising a customized information provision means for providing personalized medical and health information based on medical interview data and emotional state.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] A reservation management system that monitors the user's reservation status in real time and adjusts visit timing based on care progress information,

[0841] A diagnostic support tool that analyzes the medical questionnaire information entered by the user to determine the urgency of the situation,

[0842] A waiting time management system that predicts waiting times for home care and provides notifications including external waiting options,

[0843] A follow-up method that provides follow-up information after care and automates the next appointment,

[0844] An emotional support method that evaluates the user's psychological state using emotion analysis technology and provides relaxation content,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising a reminder means for sending a reminder notification for a scheduled home care visit to the user's terminal and requesting confirmation.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising a communication means for setting up a video call with a caregiver based on medical interview data. [Explanation of Symbols]

[0850] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A reservation management system that monitors patient appointment status in real time and adjusts the timing of visits based on the progress of treatment, A diagnostic support system that analyzes patient-entered medical information to determine the urgency of a situation, A waiting time management system that predicts waiting times based on the progress of consultations within the hospital and provides notifications including options for waiting outside, A follow-up method that provides follow-up information after consultation and automates the next appointment, A system that includes this.

2. The system according to claim 1, further comprising a reminder means for sending a reminder notification for a scheduled medical appointment to the user's terminal and requesting confirmation.

3. The system according to claim 1, comprising a communication means for setting up a video call with a doctor based on medical interview data.

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