system

Abstract

Provide a system. 【Solution means】 Means for storing reservation information from users in a storage device, Means for sending a reminder to the user based on the reservation information, Means for obtaining the progress of medical treatment and sending a notice for adjusting the arrival time, Means for inquiring about the user's symptoms and determining the urgency, Means for instructing the user to perform home care or come to the hospital based on the symptoms, Means for predicting the waiting time and notifying the user, Means for providing follow-up information after medical treatment and automatically setting the next reservation, Means for recommending the optimal visit destination to the user considering the traffic situation, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is a problem to solve the burden on patients due to long waiting times in hospitals and the decline in the operating efficiency of medical institutions. Specifically, it is necessary to improve the stress caused by long waiting times for patients and their companions, and the difficulties in managing the medical staff's treatment schedules. In addition, it is necessary to eliminate the waste of medical resources due to inappropriate management of the priority order of medical treatment.

Means for Solving the Problems

[0005] This invention provides a system that stores user reservation information in a storage device and sends reminders based on the reservation time. Furthermore, it minimizes waiting times by tracking the progress of treatment in real time and sending notifications to adjust the arrival time. Functioning as a diagnostic assistant, it analyzes the user's symptoms using an AI algorithm, determines the urgency, and provides appropriate instructions. By predicting and notifying users of waiting times, it provides an environment in which users can wait efficiently. By automatically providing follow-up information after treatment and setting the next appointment, the operation of the medical institution is made more efficient. Through these means, a reduction in waiting times and an improvement in treatment efficiency are achieved.

[0006] "Appointment information" refers to detailed information such as the date and time a patient has set in advance to receive medical treatment at a healthcare facility, as well as the doctor in charge.

[0007] A "memory device" is a device used to electronically store data, and is used to maintain reservation information and medical treatment status.

[0008] A "reminder" is a notification that informs patients in advance of a specific date, time, or event, and is a function designed to encourage them not to forget.

[0009] "Progress of treatment" refers to information indicating the stage of medical treatment or whether it is progressing according to plan.

[0010] "Arrival time" refers to the time a patient should arrive at the hospital for their scheduled appointment.

[0011] A "medical interview" is the process of gathering information through questions about the patient's symptoms and health condition.

[0012] "Urgency" is an indicator that shows how urgently a patient's symptoms require medical treatment.

[0013] "Home care" refers to medical treatments and health management methods that patients can perform at home in cases of mild illness.

[0014] "Follow-up information" refers to advice and instructions regarding future health management and necessary measures provided to patients after medical treatment.

[0015] "Next reservation" refers to the schedule of the date and time and medical services set for the patient to receive medical treatment next.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The present invention is an integrated support system that aims to improve the efficiency of waiting times in hospitals by providing users with reservation management, diagnostic assistance, waiting time notifications, and post-consultation follow-up. The goal is to reduce patient waiting times and improve the operational efficiency of medical institutions.

[0038] 1. Regarding the reception and storage of appointment information, the server receives appointment requests from users (patients) via the online platform or telephone and records them in the database. As the appointment date approaches, the terminal sends a reminder to the patient to prompt them to reconfirm their appointment.

[0039] 2. In managing the progress of medical treatment, the server retrieves progress information from healthcare professionals and verifies whether treatment is proceeding as planned. If delays occur, it notifies patients of the appropriate arrival time, reducing unnecessary waiting in the waiting room.

[0040] 3. In the diagnostic assistant function, users can input their symptoms online. Based on this information, the server uses AI to analyze the symptoms and determine the urgency of the patient's condition. The terminal displays messages such as home care instructions for patients with low urgency, and prompt hospital visits for those with high urgency.

[0041] 4. As a waiting time notification function, the server compares the progress data of medical treatments with reservation information to predict waiting times. It then notifies the patient of the waiting time via their terminal, and if a long wait is expected, it suggests ways to utilize the time outside the hospital.

[0042] 5. During post-consultation follow-up, the server automatically sets up post-consultation instructions and the next appointment, and sends them to the patient via their terminal. This ensures that patients do not forget to make their next appointment and supports health management based on their consultation results.

[0043] As a concrete example, if a patient makes an appointment for a medical consultation due to fever, the server saves the appointment and sends a reminder notification to the device the day before. Based on the progress on the day of the consultation, the server adjusts the patient's arrival time as needed and notifies them to minimize waiting time. The diagnostic assistant instructs the patient on fever-reducing measures they can take at home and instructs them to come to the clinic if necessary. After the consultation, the device notifies the patient about medication and lifestyle precautions, and the next appointment is automatically scheduled. This reduces patient waiting times and enables efficient operation of medical facilities.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users make appointments online or by phone. They enter or provide information such as the appointment date and time, the doctor in charge, and their symptoms.

[0047] Step 2:

[0048] The server stores the reservation information received from the user in a database. It then verifies that the reservation data has been recorded accurately.

[0049] Step 3:

[0050] The server sets a reminder the day before the reservation date and prepares to send a reservation notification to the device.

[0051] Step 4:

[0052] The device displays a reminder to the user the day before the reservation date, stating, "You have a reservation tomorrow at 10 AM," prompting them to confirm.

[0053] Step 5:

[0054] The server receives progress reports from healthcare professionals on the day of the consultation and verifies that the schedule is progressing as planned.

[0055] Step 6:

[0056] If there is a delay in the consultation, the server will adjust the user's arrival time and send a notification to their device.

[0057] Step 7:

[0058] When a user enters their symptoms into an online medical questionnaire, the server receives and stores the data.

[0059] Step 8:

[0060] The server uses an AI algorithm to analyze symptom data and determine the urgency of the patient's condition.

[0061] Step 9:

[0062] Based on the analysis results, the device displays home care guidance for patients with mild symptoms, and messages urging them to come to the hospital immediately if their condition is more urgent.

[0063] Step 10:

[0064] The server predicts waiting times based on patient appointment status and treatment progress data, and calculates the optimal waiting time.

[0065] Step 11:

[0066] The device notifies the user of the waiting time, displaying a message such as, "The current waiting time is approximately 20 minutes." If the wait is prolonged, it suggests an efficient waiting location.

[0067] Step 12:

[0068] After the consultation, the server generates follow-up information based on the patient's data and automatically schedules the next appointment.

[0069] Step 13:

[0070] The device notifies the user of the details of the post-consultation follow-up and information about the next appointment, supporting their health management.

[0071] (Example 1)

[0072] 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."

[0073] It is necessary to reduce waiting times at medical institutions to improve patient convenience while maintaining an appropriate order of consultations. However, the current system is inadequate in appointment management and post-diagnosis follow-up, reducing efficiency for both patients and medical institutions. Furthermore, it is difficult to accurately assess the urgency of a patient's condition, making it challenging to provide optimal medical care.

[0074] 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.

[0075] In this invention, the server includes means for storing reservation information from users in a recording function, means for acquiring information on the progress of medical treatment and adjusting visit information to improve convenience, and means for analyzing the symptoms of users and evaluating their urgency. This makes it possible to improve the efficiency of medical treatment at medical institutions, reduce patient waiting times, and enable appropriate medical treatment.

[0076] "Reservation information" refers to information about the date, time, and symptoms that a user provides to a medical institution in order to receive medical treatment.

[0077] A "recording function" is a software or hardware function that saves received information to a database.

[0078] A "notification" is a message or alert designed to provide information to a user.

[0079] "Progress information" refers to data that shows the progress of a specific process, in this case, medical treatment.

[0080] "Visit information" refers to data regarding the optimal time for a patient to visit a medical institution for treatment.

[0081] "Analysis" is the process of evaluating and judging information and conditions based on collected data.

[0082] "Urgency" refers to the degree to which a patient's symptoms require immediate attention.

[0083] A "generative AI model" is a programming method that uses artificial intelligence to analyze specific data and derive the optimal result.

[0084] As an embodiment of the present invention, an integrated support system for improving the efficiency of medical treatment at a medical institution is used. This system consists of a server, a terminal, and a user. The server is equipped with software that receives reservation information and records it in a database, and manages the information entered by the user through a web portal. For example, reservation information requires the desired date and time of consultation and a brief description of symptoms. The server also uses an AI model to analyze symptoms and determine their urgency. This AI model employs a generative AI model to calculate the optimal order of consultations and arrival time based on the information entered by the patient.

[0085] The terminal is responsible for sending reminders and notifications related to the patient's medical treatment based on instructions from the server. This includes the date and time of the next appointment, post-treatment instructions, and advice on managing health at home. It also predicts waiting times based on the progress of the treatment and suggests possible ways for the patient to spend their time as needed.

[0086] As a concrete example of operation, consider a case where a user makes a medical appointment through a web platform. When the user registers an appointment, the server saves the information to a database and sends a reminder via the terminal the day before the appointment. On the day of the appointment, the server also receives progress information from the clinic and monitors whether the appointment is proceeding as planned. If there is a delay, the arrival time will be adjusted. In addition, if symptoms such as fever are entered, the server uses an AI algorithm to assess the urgency and displays a notification on the terminal according to the urgency.

[0087] An example of a prompt message can be written to the generating AI model as follows: "Explain the appointment scheduling process for patients with fever symptoms, and then show the subsequent progress management and waiting time reduction process." In this way, it becomes possible to improve the operational efficiency of medical institutions and increase patient satisfaction.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server receives appointment information from the user. This information includes the user's preferred date and time for the appointment and a brief description of their symptoms, entered via a web platform. The server stores this information in a database. Specifically, a new entry is added to the appointment table in the database, and the server checks whether the appointment details are consistent with other appointment information.

[0091] Step 2:

[0092] As the appointment date approaches, the server sends a reminder to the terminal. The server performs an evaluation by comparing the current date with existing appointment information. The server generates a reminder message the day before the appointment and notifies the user via the terminal. Specifically, the notification data is sent using a message queue.

[0093] Step 3:

[0094] The device displays received reminder messages to the user. The input is the message content received from the server. The device displays the message on the screen with a notification sound, prompting the user to acknowledge it. Specifically, the device uses a notification API to display the message.

[0095] Step 4:

[0096] The server retrieves treatment progress information from medical institutions. The input includes data on the current progress in each examination room. Based on this information, the server analyzes the schedule in real time and determines whether treatment will be completed within the scheduled time. Specifically, it connects the progress database with the schedule management system.

[0097] Step 5:

[0098] If a delay occurs in a patient's appointment, the server adjusts the appointment time and sends this information to the terminal. Inputs include the results of progress analysis and current appointment information. The server calculates the new appointment time, generates adjustment information, and transfers it to the terminal. Specifically, an optimization process using an algorithm is performed.

[0099] Step 6:

[0100] The user inputs their symptoms into the system and uses the diagnostic assistant function. The input is the user's symptom information. The server uses a generated AI model to analyze the symptoms and determine the urgency. The output is a recommendation of countermeasures according to the urgency. Specifically, data is input into the AI ​​model and the analysis results are output.

[0101] Step 7:

[0102] The terminal displays countermeasures and instructions for visiting a hospital, if necessary, to the user based on the urgency assessment. The input consists of recommendations received from the server. The terminal displays detailed instructions to the user and, in urgent cases, prompts for quick action. Specifically, it displays alerts on the user interface.

[0103] Step 8:

[0104] The server generates follow-up information after the medical consultation is completed and automatically schedules the next appointment. Inputs include the consultation results and the next appointment schedule. The server creates a follow-up message and sends it to the user from the terminal. Furthermore, it registers the next appointment in the database. Specifically, data communication is performed using a messaging system.

[0105] (Application Example 1)

[0106] 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."

[0107] Long waiting times at medical facilities hinder efficient patient care and reduce the operational efficiency of these facilities. Furthermore, patients may have difficulty choosing the appropriate medical facility and receiving treatment at the optimal time. This leads to increased patient dissatisfaction and anxiety, ultimately resulting in a decline in the quality of medical services.

[0108] 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.

[0109] In this invention, the server includes means for storing reservation information from users in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of medical treatment and sending notifications to adjust the time of visit, means for interviewing users about their symptoms and determining the urgency, means for instructing users to receive home care or come to the clinic based on their symptoms, means for predicting waiting times and notifying users, means for providing follow-up information after medical treatment and automatically setting the next appointment, and means for recommending the most suitable destination to the user considering traffic conditions. As a result, patients can reduce unnecessary waiting times and receive necessary medical care efficiently and effectively.

[0110] "User" refers to an individual or organization that utilizes the system of the present invention.

[0111] "Reservation information" refers to data related to appointments made by users in advance with medical institutions for the purpose of medical treatment.

[0112] A "storage device" is hardware or software used to store information and make it available as needed.

[0113] A "reminder" is information or a message that is sent in advance to prevent the user from forgetting something.

[0114] "Progress of treatment" refers to information indicating whether treatment at a medical institution is progressing as planned or behind schedule.

[0115] "Arrival time" refers to the time when the user is expected to arrive at the medical institution.

[0116] "Notification" is a means of conveying information to a user, and is a form of information transmission that takes place via voice, message, or application.

[0117] A "medical interview" is a preliminary interview conducted to confirm the patient's symptoms and condition and to determine the direction of medical treatment.

[0118] "Urgency level" is an indicator that shows how urgently a user's symptoms require attention.

[0119] "Home care" refers to health management and first aid that users can perform at home.

[0120] "Waiting time" refers to the time it takes for a user to receive medical services.

[0121] "Follow-up information" refers to information that patients should pay attention to, such as necessary follow-up measures, medication, and lifestyle guidance after a medical examination.

[0122] A "next appointment" is a reservation made in advance for the user's next visit to a medical institution.

[0123] "Traffic conditions" refers to information about road conditions and traffic volume that affect geographical travel.

[0124] A "place of visit" refers to a medical institution or clinic where a user goes to receive medical treatment.

[0125] The system implementing the present invention consists of three elements: a server, a terminal, and a user. The server centrally stores reservation information from users and generates reminders based on this information, which are then sent to the terminal. Furthermore, the server acquires real-time data on the progress of medical treatment provided by medical institutions and transmits notifications to the terminal if delays occur. This notification is effective in preventing users from wasting time waiting.

[0126] The server also analyzes online medical questionnaire data using AI algorithms to determine the urgency of the symptoms. If the urgency is low, it guides the user through their device on how to care for themselves at home; if the urgency is high, it displays an alert instructing them to come to the clinic immediately. This AI analysis uses machine learning frameworks such as TENSORFLOW®.

[0127] For predicting waiting times, the user's device uses the Google® Maps API to consider traffic conditions and suggest the most efficient destination and route. The server also automatically manages follow-up after consultations, such as scheduling the next appointment and medication instructions, and notifies the user through their device.

[0128] As a concrete example, consider a scenario where a user enters their fever symptoms into the application. The server analyzes past data to find a medical facility with a short waiting time and recommends it to the user. The user then plans their trip based on the traffic information displayed on their device, enabling a smoother medical consultation.

[0129] An example of a prompt message would be: "User input: 'I have a fever and a headache. Which hospital is the least crowded?'" in response to the AI ​​system's prompt: "Analyzing user symptoms... We will now suggest clinics considering the waiting times at the nearest medical facilities and the urgency of the situation."

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The server receives user reservation information and stores it in its storage device. The input is the reservation details, and the output is the stored reservation information. The data is organized and properly stored by a database management system.

[0133] Step 2:

[0134] As the reservation date approaches, the server searches for the user's reservation details in the stored reservation information and sends a reminder notification to the device. The input is the reservation information, and the output is the reminder notification. A search algorithm is used to identify the relevant information, and it is sent to the device via a push notification system.

[0135] Step 3:

[0136] On the day of the consultation, the server retrieves the progress of the consultation from the medical institution in real time. The input is consultation progress data, and the output is updated information on the progress. Data is accessed via API to obtain highly reliable information.

[0137] Step 4:

[0138] If a patient's appointment is delayed, the server analyzes the acquired progress data and notifies the terminal of an appropriate arrival time. Inputs are progress data and appointment data, and output is a notification message. The system compares the data, uses a time adjustment algorithm to calculate the optimal arrival time, and sends a notification to the terminal.

[0139] Step 5:

[0140] The user enters their symptoms into a questionnaire form on their device. The input is symptom data, and the output is an AI-generated assessment of the severity of the symptoms. The server uses a generative AI model to analyze the symptoms and evaluate the severity.

[0141] Step 6:

[0142] The device displays action instructions to the user based on the AI ​​analysis results, tailored to their symptoms. For low-urgency cases, the device provides instructions for home care; for high-urgency cases, it displays a message encouraging immediate hospital visits. The input is the analysis results, and the output is action instructions.

[0143] Step 7:

[0144] The server recommends the optimal destination based on the user's location and traffic data. Inputs are the user's location and traffic data, while output is the recommended destination and travel route. It utilizes location services to analyze data, calculate recommendations, and compiles them into notifications.

[0145] Step 8:

[0146] After the consultation is complete, the server automatically generates the necessary follow-up information and sends it to the terminal along with the next appointment. The input is consultation result data, and the output is next appointment information and follow-up details. An automated appointment system and data management platform are used to generate and provide appropriate information.

[0147] 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.

[0148] This invention proposes a system aimed at reducing hospital waiting times and improving the efficiency of medical treatment, while also providing medical support that takes into account the user's emotional state. In addition to appointment scheduling, diagnostic assistance, waiting time prediction and notification, and post-consultation follow-up, this system incorporates an emotion engine to recognize the user's emotions and improve the quality of medical support.

[0149] 1. In the medical appointment management system, when a user makes an appointment online or by phone, the information is stored in the server's storage device. The server prepares to send a reminder to the terminal the day before the appointment.

[0150] 2. The diagnostic assistant receives the symptoms entered by the user in the online medical questionnaire form and analyzes them using an AI algorithm. This analysis determines the urgency of the situation and provides the user with appropriate instructions (home care or hospital visit).

[0151] 3. On the day of the appointment, the server retrieves the progress of the appointment from the medical staff and sends a notification to the user to adjust their arrival time. This reduces unnecessary waiting time.

[0152] 4. The emotion engine analyzes data collected from the user's device (e.g., facial expressions and voice patterns) to identify the user's emotional state. Based on this information, the server provides care guidance and relaxation content to reduce stress.

[0153] 5. Waiting time prediction is performed by the server, which utilizes real-time data from the clinic to notify the user of the expected waiting time. If a long wait is predicted, the system will suggest more comfortable waiting methods via the terminal.

[0154] 6. After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The terminal notifies the user of this information to support continuous health management.

[0155] For example, if a user requests a medical consultation due to illness, the server records the appointment once it's completed and sends a reminder to the user's device the day before. On the day of the appointment, the user's arrival time is adjusted based on the progress of the medical staff's consultations, and a notification is sent to the device. The emotion engine senses the user's anxiety and suggests playing relaxation music. After the consultation, follow-up information, including how to take medication, is displayed on the device, and the next appointment is automatically scheduled. This entire process reduces patient waiting times and improves the efficiency of hospital consultations.

[0156] The following describes the processing flow.

[0157] Step 1:

[0158] Users make appointments online or by phone. Users provide the necessary information, including the patient's name, appointment date and time, and the doctor in charge.

[0159] Step 2:

[0160] The server receives the reservation information and records it in the database. The recorded data is stored for later reference and processing.

[0161] Step 3:

[0162] The server creates a reminder the day before the reservation date and sets up a notification on the user's device.

[0163] Step 4:

[0164] The day before the reservation date, the device displays a reminder to the user saying, "You have a reservation tomorrow at 10 AM," and asks for confirmation.

[0165] Step 5:

[0166] On the day of the appointment, the server retrieves progress data from the medical staff. This data is used to verify that the appointment is proceeding as scheduled.

[0167] Step 6:

[0168] If the server detects a delay in the patient's appointment, it will adjust the user's arrival time and notify them of specific instructions via their device.

[0169] Step 7:

[0170] Before their appointment, users fill out and submit an online questionnaire about their symptoms. This information includes a detailed description of their symptoms and their medical history.

[0171] Step 8:

[0172] The server analyzes the patient information using an AI algorithm and assesses the urgency of the situation. Processing proceeds based on the severity level.

[0173] Step 9:

[0174] The device notifies the user of instructions such as "You can rest at home" or "Please come to the hospital immediately" based on the analysis results.

[0175] Step 10:

[0176] The emotion engine acquires emotion data on the device. This data is collected by reading emotions from the user's facial expressions and voice.

[0177] Step 11:

[0178] Based on data from the emotion engine, the server analyzes the user's stress level and suggests relaxation methods as needed.

[0179] Step 12:

[0180] The server predicts the waiting time and, based on reservation status and progress data, notifies the user's device of the estimated waiting time. Information such as "The current waiting time is approximately 30 minutes" is provided.

[0181] Step 13:

[0182] After the consultation, the server creates follow-up information based on the consultation results and automatically schedules the next appointment.

[0183] Step 14:

[0184] The device displays follow-up information and details of the next appointment to the user, supporting continuous health management.

[0185] (Example 2)

[0186] 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".

[0187] Traditional healthcare systems have suffered from problems such as long waiting times and decreased efficiency in medical care, leading to lower patient satisfaction. Furthermore, the quality of medical services was limited because patients' emotional states were not taken into consideration. To solve these problems, a system is needed that can efficiently manage everything from appointment scheduling to follow-up, recognize patients' emotions, and provide appropriate care.

[0188] 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.

[0189] In this invention, the server includes means for identifying the user's emotional state and providing appropriate content, means for predicting waiting times and sending notifications to suggest comfortable waiting methods when long waiting times are expected, and means for obtaining the progress of medical treatment and sending notifications to adjust the time of arrival. This enables the provision of medical services that take into account the patient's emotional state, thereby reducing waiting times and improving the efficiency of medical treatment.

[0190] "Users" refers to patients or individuals who use the system to make appointments or utilize medical services.

[0191] "Reservation information" refers to information regarding the date, time, and content of the consultation that a user has secured in advance for a medical appointment.

[0192] A "storage device" refers to a hardware or software medium for storing digital data.

[0193] A "reminder" refers to a function or service that notifies users of the date and time of their medical appointment.

[0194] "Healthcare professionals" refers to specialists or staff involved in the progress of medical procedures and the treatment of patients.

[0195] "Medical treatment progress" refers to information regarding the process and status of medical treatment.

[0196] "Urgency" refers to an indicator that shows the severity of the symptoms and how quickly necessary the response should be.

[0197] A "generative model" refers to an artificial intelligence algorithm that runs on a computer and performs data processing and prediction.

[0198] "Emotional state" refers to the psychological or emotional response exhibited by the user.

[0199] "Comfortable waiting methods" refer to suggestions or services that enable users to wait in a better environment when long waiting periods are anticipated.

[0200] "Follow-up information" refers to instructions and advice regarding treatment and health management after a medical examination.

[0201] This system aims to reduce hospital waiting times and improve the efficiency of medical treatment, providing medical support that takes into account the user's emotional state. The system operates based on information exchanged between the server, terminals, and users.

[0202] First, users make appointments online or by phone. The server stores the received appointment information in a database and prepares to send a reminder to the user's device the day before the appointment. The database uses advanced storage technology to ensure the accuracy of the information.

[0203] On the day of the appointment, the server receives the appointment progress information entered by the medical staff and adjusts the user's arrival time. Real-time communication technology is used for this information exchange. The user's terminal is notified of the adjusted arrival time, which helps reduce unnecessary waiting time.

[0204] Furthermore, this system utilizes a generative AI model. The server analyzes symptom data entered by the user into an online questionnaire form to determine the urgency of the situation. This generative model enables the rapid provision of appropriate instructions to the user. In addition, to identify the user's emotional state, the server uses an emotion engine to analyze facial expressions and voice patterns transmitted from the user's device. If the server determines that the user is experiencing stress, it provides relaxation content to the device.

[0205] Real-time data from the examination rooms is used to predict waiting times. If a long waiting time is expected, the server will offer the user beverage or entertainment options via their terminal.

[0206] After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The user's device receives notifications regarding medication usage and health management advice, supporting continuous health management.

[0207] For example, if a user feels unwell and wishes to seek medical attention, this system allows for quick and efficient scheduling, from making an appointment to post-consultation follow-up.

[0208] Examples of prompt messages include the following:

[0209] "Please use an AI algorithm to analyze the urgency level based on the symptom data collected through the online medical questionnaire."

[0210] "Please describe the process of using real-time data to predict waiting times and notifying users accordingly."

[0211] It is expected that using this system will improve the efficiency of hospital medical care and increase patient satisfaction.

[0212] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0213] Step 1:

[0214] Users make appointments online or by phone. They provide their desired appointment date and time, as well as user information. The server receives this information and stores it in its storage. Specifically, the server completes the appointment by saving the date, time, and user ID to its database.

[0215] Step 2:

[0216] The day before the reservation date, the server retrieves the reservation information from storage and creates a reminder. The input includes the reservation date and time and the user's contact information. The server sends a notification to the device via email or app. As output, a reservation confirmation is displayed on the user's device. As a concrete example of sending a reminder, a notification message is sent to the registered email address.

[0217] Step 3:

[0218] On the day of the appointment, healthcare professionals enter information about the progress of the consultation into the system. The server receives this information and adjusts the patient's appointment time based on the actual consultation schedule. As output, a notification of the adjusted appointment time is sent to the terminal. Specific actions include updating the schedule and notifying the user.

[0219] Step 4:

[0220] The user enters their symptoms into an online medical questionnaire form. The server processes this data using an AI model to determine the urgency of the situation. The input includes text data about the symptoms. The server outputs the analysis results and notifies the terminal with appropriate instructions. Specifically, if the AI ​​determines the situation is "highly urgent," it advises the user to come to the clinic immediately.

[0221] Step 5:

[0222] In analyzing emotional states, the device collects facial image and audio data from the user and sends it to the server. The server analyzes the data using an emotion engine to identify the user's emotions. The output provides the device with content designed to reduce stress. Specifically, if the system determines that the user is feeling anxious, it plays relaxation music.

[0223] Step 6:

[0224] The server predicts waiting times based on real-time data. Inputs include the current operating status of the clinics and appointment information. The output notifies the user of the predicted waiting time and suggests alternative waiting methods. For example, it might suggest providing refreshments in the waiting room.

[0225] Step 7:

[0226] After the consultation, the server generates follow-up information based on the consultation details and automatically sets the next appointment. Inputs are the consultation report and the recommended date and time for the next consultation. Output is a notification to the terminal containing consultation advice and details of the next appointment. Specifically, a predetermined algorithm determines and notifies the patient of the recommended date for the next consultation.

[0227] (Application Example 2)

[0228] 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".

[0229] In an aging society, the demand for long-term care services is increasing, but long waiting times for users and the efficiency of individualized care plans remain challenges. Furthermore, the lack of adequate care that takes into account users' emotional states is contributing to low user satisfaction. Moreover, with the provision of online services, there is a growing need for real-time information sharing and service delivery that takes emotional states into account. Therefore, there is a need for efficient and effective solutions to these challenges.

[0230] 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.

[0231] In this invention, the server includes means for storing user reservation information in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of treatment and sending notifications to adjust the appointment time, means for recognizing the user's emotions and providing information to reduce stress, and means for providing follow-up information after treatment and automatically setting the next appointment. This makes it possible to provide services tailored to the individual needs of each user, thereby reducing waiting times and improving satisfaction.

[0232] "Reservation information" refers to data regarding the date, time, and content of a service provided by a user when they apply for the service in advance.

[0233] A "storage device" is a system of hardware or software that stores digital data and makes it accessible as needed.

[0234] A "reminder" is a means of notifying users of pre-set events to help them remember them.

[0235] "Progress of treatment" refers to information regarding the current stage of progress or completion within the treatment process.

[0236] "Notifications to adjust appointment times" refer to information sent to users to optimize their scheduled appointment times and inform them of appropriate times to arrive at the clinic.

[0237] "Emotional recognition" is the process of identifying a user's emotional state and collecting and analyzing the information necessary to respond appropriately to that emotion.

[0238] "Information for reducing stress" refers to data on advice and relaxation content provided with the aim of reducing the psychological burden on users.

[0239] "Follow-up information" refers to additional announcements after the service has been provided, as well as information regarding future activity plans.

[0240] "Next reservation" refers to reservation information prepared in advance for a user's next visit.

[0241] This invention is a system for improving the quality of services provided to users in nursing care facilities or home care settings. Several key components are necessary to realize this system.

[0242] First, the server receives user reservation information and stores it in storage. A database management system is used for this, such as relational databases like MySQL® or PostgreSQL. The server also builds the backend using programming languages ​​such as Go or Python, allowing users to easily access the reservation information.

[0243] Next, the server sends a reminder notification to the user based on the reservation information. This is achieved using push notification technology (such as Google FI® rebase Cloud Messaging). The reminder is then sent to the user's device, such as a smartphone or tablet.

[0244] Furthermore, if a consultation is in progress, the server tracks the progress of the consultation and sends notifications in real time to adjust the user's appointment time. This incorporates a tracking and notification system that works with the frontend using JavaScript® frameworks in addition to Go and Python.

[0245] Furthermore, for emotion recognition, the user's device acquires facial expressions and voice data and sends this data to the server. The server analyzes the emotional state using tools such as TensorFlow and OpenCV. Based on the results, it provides information and care plans to reduce stress.

[0246] As a concrete example, when a user books a care service, the server records it and sends a reminder the day before the scheduled appointment. On the day of the appointment, if the emotion engine detects that the user is feeling anxious, a notification is sent to the user's device suggesting that relaxation music be played. Once the service is completed, the next appointment is automatically scheduled and the user is notified.

[0247] Here's an example of a prompt using a generative AI model to provide flexible guidance based on emotional state: "Please input smile expression data, analyze the user's emotional state, and suggest relaxation content."

[0248] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0249] Step 1:

[0250] The server receives reservation information from users and stores it in a database. Inputs include the user's reservation date and time, location, and service details, and this data is structured and stored by the database management system. The output is a confirmation message indicating that the reservation information has been accurately recorded.

[0251] Step 2:

[0252] The server sends a reminder to the user the day before the reservation date. The inputs are reservation information retrieved from the database and the user's contact information. The data processing involves generating a reminder message and sending it to the user's device via a push notification system. The output is the notification message received by the user.

[0253] Step 3:

[0254] On the day of the appointment, the server tracks the progress of the consultation in real time and sends notifications to the user to adjust their appointment time. The input is progress data obtained from the consultation room, and the server uses this to calculate the optimal appointment time. The output is a notification message that includes the adjusted appointment time.

[0255] Step 4:

[0256] The user's device collects facial and voice data for emotion recognition and sends it to the server. The input is real-time data from the user's camera and microphone, which is analyzed using data processing tools such as TensorFlow and OpenCV. The output is the analysis result indicating the user's emotional state.

[0257] Step 5:

[0258] The server generates and provides information to reduce stress based on the user's emotional state. The input is the result of emotion recognition analysis, which the AI ​​model uses to select relaxation content and care plans. The output is a suggestion notification displayed on the user's device.

[0259] Step 6:

[0260] After the consultation, the server generates follow-up information and automatically schedules the next appointment. Inputs include the consultation record and the user's preferred date and time for the next appointment. Data processing generates follow-up information and determines the next appointment date and time. Output is a confirmation message for the next appointment sent to the user.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] [Second Embodiment]

[0265] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0266] 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.

[0267] 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).

[0268] 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.

[0269] 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.

[0270] 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).

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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.

[0275] 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.

[0276] 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".

[0277] The present invention is an integrated support system that aims to improve the efficiency of waiting times in hospitals by providing users with reservation management, diagnostic assistance, waiting time notifications, and post-consultation follow-up. The goal is to reduce patient waiting times and improve the operational efficiency of medical institutions.

[0278] 1. Regarding the reception and storage of appointment information, the server receives appointment requests from users (patients) via the online platform or telephone and records them in the database. As the appointment date approaches, the terminal sends a reminder to the patient to prompt them to reconfirm their appointment.

[0279] 2. In the progress management of medical treatment, the server obtains progress information from medical staff and checks whether the medical treatment is proceeding as planned. If a delay occurs, by notifying the patient of an appropriate arrival time, unnecessary waiting in the waiting room can be reduced.

[0280] 3. In the diagnostic assistant function, the user can input symptoms online. Based on this information, the server analyzes the symptoms using AI and determines the urgency of the patient. The terminal displays a message to guide home care for patients with low urgency and prompt urgent hospital visits for those with high urgency.

[0281] 4. As a waiting time notification function, the server compares the progress data of medical treatment with reservation information to predict the waiting time. Then, it notifies the patient of the waiting time through the terminal and proposes ways to utilize time outside the hospital if a long wait is expected.

[0282] 5. In the follow-up after medical treatment, the server automatically sets post-treatment precautions and the next reservation and sends them to the patient through the terminal. This enables the patient to make the next reservation without forgetting and supports health management based on the medical treatment results.

[0283] As a specific example, when a patient makes a medical treatment reservation due to fever, when the user makes a reservation, the server saves it and a notification reaches the terminal as a reminder the day before. Based on the progress on the day of medical treatment, the server adjusts the patient's arrival time as needed and notifies to shorten the waiting time. The diagnostic assistant guides the patient on possible antipyretic measures at home and instructs the patient to come to the hospital if necessary. After medical treatment, the terminal notifies the patient about medication and points to note in daily life, and the next reservation is automatically set. This realizes the shortening of the patient's waiting time and the efficient operation of the medical institution.

[0284] The following explains the process flow.

[0285] Step 1:

[0286] The user makes a medical appointment online or by phone, entering or transmitting information such as the appointment date and time, the attending doctor, and symptoms.

[0287] Step 2:

[0288] The server saves the appointment information received from the user in the database and checks that the appointment data has been accurately recorded.

[0289] Step 3:

[0290] The server sets a reminder the day before the appointment and prepares to send a notification of the appointment details to the terminal.

[0291] Step 4:

[0292] The terminal displays a reminder to the user "You have an appointment at 10 am tomorrow" the day before the appointment and prompts for confirmation.

[0293] Step 5:

[0294] The server receives the progress of the medical treatment from the medical staff on the day of the appointment and checks whether the schedule is proceeding as planned.

[0295] Step 6:

[0296] If there is a delay in the medical treatment, the server adjusts the user's arrival time and sends a notification to the terminal.

[0297] Step 7:

[0298] When the user enters symptoms in the online consultation form, the server receives and saves the data.

[0299] Step 8:

[0300] The server analyzes the symptom data using an AI algorithm to determine the urgency of the patient.

[0301] Step 9:

[0302] Based on the analysis results, the terminal displays home care guidance for mild patients and a message prompting urgent hospital visit in case of high urgency.

[0303] Step 10:

[0304] The server predicts the waiting time based on the patient's reservation status and medical treatment progress data, and calculates the optimal waiting time.

[0305] Step 11:

[0306] The terminal notifies the user of the waiting time and displays "The current waiting time is about 20 minutes". If the waiting time is prolonged, it proposes an efficient waiting place.

[0307] Step 12:

[0308] After the medical treatment, the server generates follow-up information based on the patient's data and automatically sets the next reservation.

[0309] Step 13:

[0310] The terminal notifies the user of the content of the follow-up after medical treatment and the information of the next reservation, and supports health management.

[0311] (Example 1)

[0312] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] It is necessary to shorten the waiting time for medical treatment in medical institutions, improve the convenience of patients, and maintain an appropriate medical treatment order. However, in the current system, reservation management and follow-up after diagnosis are insufficient, and the efficiency for both patients and medical institutions has decreased. Furthermore, it is difficult to accurately judge the urgency of patients, and it is difficult to provide optimal medical treatment.

[0314] 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.

[0315] In this invention, the server includes means for storing reservation information from users in a recording function, means for acquiring information on the progress of medical treatment and adjusting visit information to improve convenience, and means for analyzing the symptoms of users and evaluating their urgency. This makes it possible to improve the efficiency of medical treatment at medical institutions, reduce patient waiting times, and enable appropriate medical treatment.

[0316] "Reservation information" refers to information about the date, time, and symptoms that a user provides to a medical institution in order to receive medical treatment.

[0317] A "recording function" is a software or hardware function that saves received information to a database.

[0318] A "notification" is a message or alert designed to provide information to a user.

[0319] "Progress information" refers to data that shows the progress of a specific process, in this case, medical treatment.

[0320] "Visit information" refers to data regarding the optimal time for a patient to visit a medical institution for treatment.

[0321] "Analysis" is the process of evaluating and judging information and conditions based on collected data.

[0322] "Urgency" refers to the degree to which a patient's symptoms require immediate attention.

[0323] A "generative AI model" is a programming method that uses artificial intelligence to analyze specific data and derive the optimal result.

[0324] As an embodiment of the present invention, an integrated support system for improving the efficiency of medical treatment at a medical institution is used. This system consists of a server, a terminal, and a user. The server is equipped with software that receives reservation information and records it in a database, and manages the information entered by the user through a web portal. For example, reservation information requires the desired date and time of consultation and a brief description of symptoms. The server also uses an AI model to analyze symptoms and determine their urgency. This AI model employs a generative AI model to calculate the optimal order of consultations and arrival time based on the information entered by the patient.

[0325] The terminal is responsible for sending reminders and notifications related to the patient's medical treatment based on instructions from the server. This includes the date and time of the next appointment, post-treatment instructions, and advice on managing health at home. It also predicts waiting times based on the progress of the treatment and suggests possible ways for the patient to spend their time as needed.

[0326] As a concrete example of operation, consider a case where a user makes a medical appointment through a web platform. When the user registers an appointment, the server saves the information to a database and sends a reminder via the terminal the day before the appointment. On the day of the appointment, the server also receives progress information from the clinic and monitors whether the appointment is proceeding as planned. If there is a delay, the arrival time will be adjusted. In addition, if symptoms such as fever are entered, the server uses an AI algorithm to assess the urgency and displays a notification on the terminal according to the urgency.

[0327] An example of a prompt message can be written to the generating AI model as follows: "Explain the appointment scheduling process for patients with fever symptoms, and then show the subsequent progress management and waiting time reduction process." In this way, it becomes possible to improve the operational efficiency of medical institutions and increase patient satisfaction.

[0328] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0329] Step 1:

[0330] The server receives appointment information from the user. This information includes the user's preferred date and time for the appointment and a brief description of their symptoms, entered via a web platform. The server stores this information in a database. Specifically, a new entry is added to the appointment table in the database, and the server checks whether the appointment details are consistent with other appointment information.

[0331] Step 2:

[0332] As the appointment date approaches, the server sends a reminder to the terminal. The server performs an evaluation by comparing the current date with existing appointment information. The server generates a reminder message the day before the appointment and notifies the user via the terminal. Specifically, the notification data is sent using a message queue.

[0333] Step 3:

[0334] The device displays received reminder messages to the user. The input is the message content received from the server. The device displays the message on the screen with a notification sound, prompting the user to acknowledge it. Specifically, the device uses a notification API to display the message.

[0335] Step 4:

[0336] The server retrieves treatment progress information from medical institutions. The input includes data on the current progress in each examination room. Based on this information, the server analyzes the schedule in real time and determines whether treatment will be completed within the scheduled time. Specifically, it connects the progress database with the schedule management system.

[0337] Step 5:

[0338] If a delay occurs in a patient's appointment, the server adjusts the appointment time and sends this information to the terminal. Inputs include the results of progress analysis and current appointment information. The server calculates the new appointment time, generates adjustment information, and transfers it to the terminal. Specifically, an optimization process using an algorithm is performed.

[0339] Step 6:

[0340] The user inputs their symptoms into the system and uses the diagnostic assistant function. The input is the user's symptom information. The server uses a generated AI model to analyze the symptoms and determine the urgency. The output is a recommendation of countermeasures according to the urgency. Specifically, data is input into the AI ​​model and the analysis results are output.

[0341] Step 7:

[0342] The terminal displays countermeasures and instructions for visiting a hospital, if necessary, to the user based on the urgency assessment. The input consists of recommendations received from the server. The terminal displays detailed instructions to the user and, in urgent cases, prompts for quick action. Specifically, it displays alerts on the user interface.

[0343] Step 8:

[0344] The server generates follow-up information after the medical consultation is completed and automatically schedules the next appointment. Inputs include the consultation results and the next appointment schedule. The server creates a follow-up message and sends it to the user from the terminal. Furthermore, it registers the next appointment in the database. Specifically, data communication is performed using a messaging system.

[0345] (Application Example 1)

[0346] 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."

[0347] Long waiting times at medical facilities hinder efficient patient care and reduce the operational efficiency of these facilities. Furthermore, patients may have difficulty choosing the appropriate medical facility and receiving treatment at the optimal time. This leads to increased patient dissatisfaction and anxiety, ultimately resulting in a decline in the quality of medical services.

[0348] 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.

[0349] In this invention, the server includes means for storing reservation information from users in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of medical treatment and sending notifications to adjust the time of visit, means for interviewing users about their symptoms and determining the urgency, means for instructing users to receive home care or come to the clinic based on their symptoms, means for predicting waiting times and notifying users, means for providing follow-up information after medical treatment and automatically setting the next appointment, and means for recommending the most suitable destination to the user considering traffic conditions. As a result, patients can reduce unnecessary waiting times and receive necessary medical care efficiently and effectively.

[0350] "User" refers to an individual or organization that utilizes the system of the present invention.

[0351] "Reservation information" refers to data related to appointments made by users in advance with medical institutions for the purpose of medical treatment.

[0352] A "storage device" is hardware or software used to store information and make it available as needed.

[0353] A "reminder" is information or a message that is sent in advance to prevent the user from forgetting something.

[0354] "Progress of treatment" refers to information indicating whether treatment at a medical institution is progressing as planned or behind schedule.

[0355] "Arrival time" refers to the time when the user is expected to arrive at the medical institution.

[0356] "Notification" is a means of conveying information to a user, and is a form of information transmission that takes place via voice, message, or application.

[0357] A "medical interview" is a preliminary interview conducted to confirm the patient's symptoms and condition and to determine the direction of medical treatment.

[0358] "Urgency level" is an indicator that shows how urgently a user's symptoms require attention.

[0359] "Home care" refers to health management and first aid that users can perform at home.

[0360] "Waiting time" refers to the time it takes for a user to receive medical services.

[0361] "Follow-up information" refers to information that patients should pay attention to, such as necessary follow-up measures, medication, and lifestyle guidance after a medical examination.

[0362] A "next appointment" is a reservation made in advance for the user's next visit to a medical institution.

[0363] "Traffic conditions" refers to information about road conditions and traffic volume that affect geographical travel.

[0364] A "place of visit" refers to a medical institution or clinic where a user goes to receive medical treatment.

[0365] The system implementing the present invention consists of three elements: a server, a terminal, and a user. The server centrally stores reservation information from users and generates reminders based on this information, which are then sent to the terminal. Furthermore, the server acquires real-time data on the progress of medical treatment provided by medical institutions and transmits notifications to the terminal if delays occur. This notification is effective in preventing users from wasting time waiting.

[0366] The server also analyzes online medical questionnaire data using AI algorithms to determine the urgency of the symptoms. If the urgency is low, it guides the user through their device on how to care for themselves at home; if the urgency is high, it displays an alert instructing them to come to the clinic immediately. This AI analysis uses machine learning frameworks such as TensorFlow.

[0367] For predicting waiting times, the user's device uses the Google Maps API to consider traffic conditions and suggest the most efficient destination and route. The server also automatically manages follow-up after consultations, such as scheduling the next appointment and medication instructions, and notifies the user through their device.

[0368] As a concrete example, consider a scenario where a user enters their fever symptoms into the application. The server analyzes past data to find a medical facility with a short waiting time and recommends it to the user. The user then plans their trip based on the traffic information displayed on their device, enabling a smoother medical consultation.

[0369] An example of a prompt message would be: "User input: 'I have a fever and a headache. Which hospital is the least crowded?'" in response to the AI ​​system's prompt: "Analyzing user symptoms... We will now suggest clinics considering the waiting times at the nearest medical facilities and the urgency of the situation."

[0370] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0371] Step 1:

[0372] The server receives user reservation information and stores it in its storage device. The input is the reservation details, and the output is the stored reservation information. The data is organized and properly stored by a database management system.

[0373] Step 2:

[0374] As the reservation date approaches, the server searches for the user's reservation details in the stored reservation information and sends a reminder notification to the device. The input is the reservation information, and the output is the reminder notification. A search algorithm is used to identify the relevant information, and it is sent to the device via a push notification system.

[0375] Step 3:

[0376] On the day of the consultation, the server retrieves the progress of the consultation from the medical institution in real time. The input is consultation progress data, and the output is updated information on the progress. Data is accessed via API to obtain highly reliable information.

[0377] Step 4:

[0378] If a patient's appointment is delayed, the server analyzes the acquired progress data and notifies the terminal of an appropriate arrival time. Inputs are progress data and appointment data, and output is a notification message. The system compares the data, uses a time adjustment algorithm to calculate the optimal arrival time, and sends a notification to the terminal.

[0379] Step 5:

[0380] The user enters their symptoms into a questionnaire form on their device. The input is symptom data, and the output is an AI-generated assessment of the severity of the symptoms. The server uses a generative AI model to analyze the symptoms and evaluate the severity.

[0381] Step 6:

[0382] The device displays action instructions to the user based on the AI ​​analysis results, tailored to their symptoms. For low-urgency cases, the device provides instructions for home care; for high-urgency cases, it displays a message encouraging immediate hospital visits. The input is the analysis results, and the output is action instructions.

[0383] Step 7:

[0384] The server recommends the optimal destination based on the user's location and traffic data. Inputs are the user's location and traffic data, while output is the recommended destination and travel route. It utilizes location services to analyze data, calculate recommendations, and compiles them into notifications.

[0385] Step 8:

[0386] After the consultation is complete, the server automatically generates the necessary follow-up information and sends it to the terminal along with the next appointment. The input is consultation result data, and the output is next appointment information and follow-up details. An automated appointment system and data management platform are used to generate and provide appropriate information.

[0387] 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.

[0388] This invention proposes a system aimed at reducing hospital waiting times and improving the efficiency of medical treatment, while also providing medical support that takes into account the user's emotional state. In addition to appointment scheduling, diagnostic assistance, waiting time prediction and notification, and post-consultation follow-up, this system incorporates an emotion engine to recognize the user's emotions and improve the quality of medical support.

[0389] 1. In the medical appointment management system, when a user makes an appointment online or by phone, the information is stored in the server's storage device. The server prepares to send a reminder to the terminal the day before the appointment.

[0390] 2. The diagnostic assistant receives the symptoms entered by the user in the online medical questionnaire form and analyzes them using an AI algorithm. This analysis determines the urgency of the situation and provides the user with appropriate instructions (home care or hospital visit).

[0391] 3. On the day of the appointment, the server retrieves the progress of the appointment from the medical staff and sends a notification to the user to adjust their arrival time. This reduces unnecessary waiting time.

[0392] 4. The emotion engine analyzes data collected from the user's device (e.g., facial expressions and voice patterns) to identify the user's emotional state. Based on this information, the server provides care guidance and relaxation content to reduce stress.

[0393] 5. Waiting time prediction is performed by the server, which utilizes real-time data from the clinic to notify the user of the expected waiting time. If a long wait is predicted, the system will suggest more comfortable waiting methods via the terminal.

[0394] 6. After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The terminal notifies the user of this information to support continuous health management.

[0395] For example, if a user requests a medical consultation due to illness, the server records the appointment once it's completed and sends a reminder to the user's device the day before. On the day of the appointment, the user's arrival time is adjusted based on the progress of the medical staff's consultations, and a notification is sent to the device. The emotion engine senses the user's anxiety and suggests playing relaxation music. After the consultation, follow-up information, including how to take medication, is displayed on the device, and the next appointment is automatically scheduled. This entire process reduces patient waiting times and improves the efficiency of hospital consultations.

[0396] The following describes the processing flow.

[0397] Step 1:

[0398] Users make appointments online or by phone. Users provide the necessary information, including the patient's name, appointment date and time, and the doctor in charge.

[0399] Step 2:

[0400] The server receives the reservation information and records it in the database. The recorded data is stored for later reference and processing.

[0401] Step 3:

[0402] The server creates a reminder the day before the reservation date and sets up a notification on the user's device.

[0403] Step 4:

[0404] The day before the reservation date, the device displays a reminder to the user saying, "You have a reservation tomorrow at 10 AM," and asks for confirmation.

[0405] Step 5:

[0406] On the day of the appointment, the server retrieves progress data from the medical staff. This data is used to verify that the appointment is proceeding as scheduled.

[0407] Step 6:

[0408] If the server detects a delay in the patient's appointment, it will adjust the user's arrival time and notify them of specific instructions via their device.

[0409] Step 7:

[0410] Before their appointment, users fill out and submit an online questionnaire about their symptoms. This information includes a detailed description of their symptoms and their medical history.

[0411] Step 8:

[0412] The server analyzes the patient information using an AI algorithm and assesses the urgency of the situation. Processing proceeds based on the severity level.

[0413] Step 9:

[0414] The device notifies the user of instructions such as "You can rest at home" or "Please come to the hospital immediately" based on the analysis results.

[0415] Step 10:

[0416] The emotion engine acquires emotion data on the device. This data is collected by reading emotions from the user's facial expressions and voice.

[0417] Step 11:

[0418] Based on data from the emotion engine, the server analyzes the user's stress level and suggests relaxation methods as needed.

[0419] Step 12:

[0420] The server predicts the waiting time and, based on reservation status and progress data, notifies the user's device of the estimated waiting time. Information such as "The current waiting time is approximately 30 minutes" is provided.

[0421] Step 13:

[0422] After the consultation, the server creates follow-up information based on the consultation results and automatically schedules the next appointment.

[0423] Step 14:

[0424] The device displays follow-up information and details of the next appointment to the user, supporting continuous health management.

[0425] (Example 2)

[0426] 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".

[0427] Traditional healthcare systems have suffered from problems such as long waiting times and decreased efficiency in medical care, leading to lower patient satisfaction. Furthermore, the quality of medical services was limited because patients' emotional states were not taken into consideration. To solve these problems, a system is needed that can efficiently manage everything from appointment scheduling to follow-up, recognize patients' emotions, and provide appropriate care.

[0428] 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.

[0429] In this invention, the server includes means for identifying the user's emotional state and providing appropriate content, means for predicting waiting times and sending notifications to suggest comfortable waiting methods when long waiting times are expected, and means for obtaining the progress of medical treatment and sending notifications to adjust the time of arrival. This enables the provision of medical services that take into account the patient's emotional state, thereby reducing waiting times and improving the efficiency of medical treatment.

[0430] "Users" refers to patients or individuals who use the system to make appointments or utilize medical services.

[0431] "Reservation information" refers to information regarding the date, time, and content of the consultation that a user has secured in advance for a medical appointment.

[0432] A "storage device" refers to a hardware or software medium for storing digital data.

[0433] A "reminder" refers to a function or service that notifies users of the date and time of their medical appointment.

[0434] "Healthcare professionals" refers to specialists or staff involved in the progress of medical procedures and the treatment of patients.

[0435] "Medical treatment progress" refers to information regarding the process and status of medical treatment.

[0436] "Urgency" refers to an indicator that shows the severity of the symptoms and how quickly necessary the response should be.

[0437] A "generative model" refers to an artificial intelligence algorithm that runs on a computer and performs data processing and prediction.

[0438] "Emotional state" refers to the psychological or emotional response exhibited by the user.

[0439] "Comfortable waiting methods" refer to suggestions or services that enable users to wait in a better environment when long waiting periods are anticipated.

[0440] "Follow-up information" refers to instructions and advice regarding treatment and health management after a medical examination.

[0441] This system aims to reduce hospital waiting times and improve the efficiency of medical treatment, providing medical support that takes into account the user's emotional state. The system operates based on information exchanged between the server, terminals, and users.

[0442] First, users make appointments online or by phone. The server stores the received appointment information in a database and prepares to send a reminder to the user's device the day before the appointment. The database uses advanced storage technology to ensure the accuracy of the information.

[0443] On the day of the appointment, the server receives the appointment progress information entered by the medical staff and adjusts the user's arrival time. Real-time communication technology is used for this information exchange. The user's terminal is notified of the adjusted arrival time, which helps reduce unnecessary waiting time.

[0444] Furthermore, this system utilizes a generative AI model. The server analyzes symptom data entered by the user into an online questionnaire form to determine the urgency of the situation. This generative model enables the rapid provision of appropriate instructions to the user. In addition, to identify the user's emotional state, the server uses an emotion engine to analyze facial expressions and voice patterns transmitted from the user's device. If the server determines that the user is experiencing stress, it provides relaxation content to the device.

[0445] Real-time data from the examination rooms is used to predict waiting times. If a long waiting time is expected, the server will offer the user beverage or entertainment options via their terminal.

[0446] After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The user's device receives notifications regarding medication usage and health management advice, supporting continuous health management.

[0447] For example, if a user feels unwell and wishes to seek medical attention, this system allows for quick and efficient scheduling, from making an appointment to post-consultation follow-up.

[0448] Examples of prompt messages include the following:

[0449] "Please use an AI algorithm to analyze the urgency level based on the symptom data collected through the online medical questionnaire."

[0450] "Please describe the process of using real-time data to predict waiting times and notifying users accordingly."

[0451] It is expected that using this system will improve the efficiency of hospital medical care and increase patient satisfaction.

[0452] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0453] Step 1:

[0454] Users make appointments online or by phone. They provide their desired appointment date and time, as well as user information. The server receives this information and stores it in its storage. Specifically, the server completes the appointment by saving the date, time, and user ID to its database.

[0455] Step 2:

[0456] The day before the reservation date, the server retrieves the reservation information from storage and creates a reminder. The input includes the reservation date and time and the user's contact information. The server sends a notification to the device via email or app. As output, a reservation confirmation is displayed on the user's device. As a concrete example of sending a reminder, a notification message is sent to the registered email address.

[0457] Step 3:

[0458] On the day of the appointment, healthcare professionals enter information about the progress of the consultation into the system. The server receives this information and adjusts the patient's appointment time based on the actual consultation schedule. As output, a notification of the adjusted appointment time is sent to the terminal. Specific actions include updating the schedule and notifying the user.

[0459] Step 4:

[0460] The user enters their symptoms into an online medical questionnaire form. The server processes this data using an AI model to determine the urgency of the situation. The input includes text data about the symptoms. The server outputs the analysis results and notifies the terminal with appropriate instructions. Specifically, if the AI ​​determines the situation is "highly urgent," it advises the user to come to the clinic immediately.

[0461] Step 5:

[0462] In analyzing emotional states, the device collects facial image and audio data from the user and sends it to the server. The server analyzes the data using an emotion engine to identify the user's emotions. The output provides the device with content designed to reduce stress. Specifically, if the system determines that the user is feeling anxious, it plays relaxation music.

[0463] Step 6:

[0464] The server predicts waiting times based on real-time data. Inputs include the current operating status of the clinics and appointment information. The output notifies the user of the predicted waiting time and suggests alternative waiting methods. For example, it might suggest providing refreshments in the waiting room.

[0465] Step 7:

[0466] After the consultation, the server generates follow-up information based on the consultation details and automatically sets the next appointment. Inputs are the consultation report and the recommended date and time for the next consultation. Output is a notification to the terminal containing consultation advice and details of the next appointment. Specifically, a predetermined algorithm determines and notifies the patient of the recommended date for the next consultation.

[0467] (Application Example 2)

[0468] 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."

[0469] In an aging society, the demand for long-term care services is increasing, but long waiting times for users and the efficiency of individualized care plans remain challenges. Furthermore, the lack of adequate care that takes into account users' emotional states is contributing to low user satisfaction. Moreover, with the provision of online services, there is a growing need for real-time information sharing and service delivery that takes emotional states into account. Therefore, there is a need for efficient and effective solutions to these challenges.

[0470] 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.

[0471] In this invention, the server includes means for storing user reservation information in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of treatment and sending notifications to adjust the appointment time, means for recognizing the user's emotions and providing information to reduce stress, and means for providing follow-up information after treatment and automatically setting the next appointment. This makes it possible to provide services tailored to the individual needs of each user, thereby reducing waiting times and improving satisfaction.

[0472] "Reservation information" refers to data regarding the date, time, and content of a service provided by a user when they apply for the service in advance.

[0473] A "storage device" is a system of hardware or software that stores digital data and makes it accessible as needed.

[0474] A "reminder" is a means of notifying users of pre-set events to help them remember them.

[0475] "Progress of treatment" refers to information regarding the current stage of progress or completion within the treatment process.

[0476] "Notifications to adjust appointment times" refer to information sent to users to optimize their scheduled appointment times and inform them of appropriate times to arrive at the clinic.

[0477] "Emotional recognition" is the process of identifying a user's emotional state and collecting and analyzing the information necessary to respond appropriately to that emotion.

[0478] "Information for reducing stress" refers to data on advice and relaxation content provided with the aim of reducing the psychological burden on users.

[0479] "Follow-up information" refers to additional announcements after the service has been provided, as well as information regarding future activity plans.

[0480] "Next reservation" refers to reservation information prepared in advance for a user's next visit.

[0481] This invention is a system for improving the quality of services provided to users in nursing care facilities or home care settings. Several key components are necessary to realize this system.

[0482] First, the server receives user reservation information and stores it in storage. A database management system is used for this, such as relational databases like MySQL or PostgreSQL. The server also builds the backend using programming languages ​​like Go or Python, allowing users to easily access the reservation information.

[0483] Next, the server sends a reminder notification to the user based on the reservation information. This is achieved using push notification technology (such as Google Firebase Cloud Messaging). The reminder is then sent to the user's device, such as a smartphone or tablet.

[0484] Furthermore, if a consultation is in progress, the server tracks the progress of the consultation and sends notifications in real time to adjust the user's appointment time. This incorporates a tracking and notification system that works with the frontend by leveraging JavaScript frameworks in addition to Go and Python.

[0485] Furthermore, for emotion recognition, the user's device acquires facial expressions and voice data and sends this data to the server. The server analyzes the emotional state using tools such as TensorFlow and OpenCV. Based on the results, it provides information and care plans to reduce stress.

[0486] As a concrete example, when a user books a care service, the server records it and sends a reminder the day before the scheduled appointment. On the day of the appointment, if the emotion engine detects that the user is feeling anxious, a notification is sent to the user's device suggesting that relaxation music be played. Once the service is completed, the next appointment is automatically scheduled and the user is notified.

[0487] Here's an example of a prompt using a generative AI model to provide flexible guidance based on emotional state: "Please input smile expression data, analyze the user's emotional state, and suggest relaxation content."

[0488] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0489] Step 1:

[0490] The server receives reservation information from users and stores it in a database. Inputs include the user's reservation date and time, location, and service details, and this data is structured and stored by the database management system. The output is a confirmation message indicating that the reservation information has been accurately recorded.

[0491] Step 2:

[0492] The server sends a reminder to the user the day before the reservation date. The inputs are reservation information retrieved from the database and the user's contact information. The data processing involves generating a reminder message and sending it to the user's device via a push notification system. The output is the notification message received by the user.

[0493] Step 3:

[0494] On the day of the appointment, the server tracks the progress of the consultation in real time and sends notifications to the user to adjust their appointment time. The input is progress data obtained from the consultation room, and the server uses this to calculate the optimal appointment time. The output is a notification message that includes the adjusted appointment time.

[0495] Step 4:

[0496] The user's device collects facial and voice data for emotion recognition and sends it to the server. The input is real-time data from the user's camera and microphone, which is analyzed using data processing tools such as TensorFlow and OpenCV. The output is the analysis result indicating the user's emotional state.

[0497] Step 5:

[0498] The server generates and provides information to reduce stress based on the user's emotional state. The input is the result of emotion recognition analysis, which the AI ​​model uses to select relaxation content and care plans. The output is a suggestion notification displayed on the user's device.

[0499] Step 6:

[0500] After the consultation, the server generates follow-up information and automatically schedules the next appointment. Inputs include the consultation record and the user's preferred date and time for the next appointment. Data processing generates follow-up information and determines the next appointment date and time. Output is a confirmation message for the next appointment sent to the user.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] [Third Embodiment]

[0505] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0506] 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.

[0507] 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).

[0508] 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.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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.

[0516] 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".

[0517] The present invention is an integrated support system that aims to improve the efficiency of waiting times in hospitals by providing users with reservation management, diagnostic assistance, waiting time notifications, and post-consultation follow-up. The goal is to reduce patient waiting times and improve the operational efficiency of medical institutions.

[0518] 1. Regarding the reception and storage of appointment information, the server receives appointment requests from users (patients) via the online platform or telephone and records them in the database. As the appointment date approaches, the terminal sends a reminder to the patient to prompt them to reconfirm their appointment.

[0519] 2. In managing the progress of medical treatment, the server retrieves progress information from healthcare professionals and verifies whether treatment is proceeding as planned. If delays occur, it notifies patients of the appropriate arrival time, reducing unnecessary waiting in the waiting room.

[0520] 3. In the diagnostic assistant function, users can input their symptoms online. Based on this information, the server uses AI to analyze the symptoms and determine the urgency of the patient's condition. The terminal displays messages such as home care instructions for patients with low urgency, and prompt hospital visits for those with high urgency.

[0521] 4. As a waiting time notification function, the server compares the progress data of medical treatments with reservation information to predict waiting times. It then notifies the patient of the waiting time via their terminal, and if a long wait is expected, it suggests ways to utilize the time outside the hospital.

[0522] 5. During post-consultation follow-up, the server automatically sets up post-consultation instructions and the next appointment, and sends them to the patient via their terminal. This ensures that patients do not forget to make their next appointment and supports health management based on their consultation results.

[0523] As a concrete example, if a patient makes an appointment for a medical consultation due to fever, the server saves the appointment and sends a reminder notification to the device the day before. Based on the progress on the day of the consultation, the server adjusts the patient's arrival time as needed and notifies them to minimize waiting time. The diagnostic assistant instructs the patient on fever-reducing measures they can take at home and instructs them to come to the clinic if necessary. After the consultation, the device notifies the patient about medication and lifestyle precautions, and the next appointment is automatically scheduled. This reduces patient waiting times and enables efficient operation of medical facilities.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] Users make appointments online or by phone. They enter or provide information such as the appointment date and time, the doctor in charge, and their symptoms.

[0527] Step 2:

[0528] The server stores the reservation information received from the user in a database. It then verifies that the reservation data has been recorded accurately.

[0529] Step 3:

[0530] The server sets a reminder the day before the reservation date and prepares to send a reservation notification to the device.

[0531] Step 4:

[0532] The device displays a reminder to the user the day before the reservation date, stating, "You have a reservation tomorrow at 10 AM," prompting them to confirm.

[0533] Step 5:

[0534] The server receives progress reports from healthcare professionals on the day of the consultation and verifies that the schedule is progressing as planned.

[0535] Step 6:

[0536] If there is a delay in the consultation, the server will adjust the user's arrival time and send a notification to their device.

[0537] Step 7:

[0538] When a user enters their symptoms into an online medical questionnaire, the server receives and stores the data.

[0539] Step 8:

[0540] The server uses an AI algorithm to analyze symptom data and determine the urgency of the patient's condition.

[0541] Step 9:

[0542] Based on the analysis results, the device displays home care guidance for patients with mild symptoms, and messages urging them to come to the hospital immediately if their condition is more urgent.

[0543] Step 10:

[0544] The server predicts waiting times based on patient appointment status and treatment progress data, and calculates the optimal waiting time.

[0545] Step 11:

[0546] The device notifies the user of the waiting time, displaying a message such as, "The current waiting time is approximately 20 minutes." If the wait is prolonged, it suggests an efficient waiting location.

[0547] Step 12:

[0548] After the consultation, the server generates follow-up information based on the patient's data and automatically schedules the next appointment.

[0549] Step 13:

[0550] The device notifies the user of the details of the post-consultation follow-up and information about the next appointment, supporting their health management.

[0551] (Example 1)

[0552] 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."

[0553] It is necessary to reduce waiting times at medical institutions to improve patient convenience while maintaining an appropriate order of consultations. However, the current system is inadequate in appointment management and post-diagnosis follow-up, reducing efficiency for both patients and medical institutions. Furthermore, it is difficult to accurately assess the urgency of a patient's condition, making it challenging to provide optimal medical care.

[0554] 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.

[0555] In this invention, the server includes means for storing reservation information from users in a recording function, means for acquiring information on the progress of medical treatment and adjusting visit information to improve convenience, and means for analyzing the symptoms of users and evaluating their urgency. This makes it possible to improve the efficiency of medical treatment at medical institutions, reduce patient waiting times, and enable appropriate medical treatment.

[0556] "Reservation information" refers to information about the date, time, and symptoms that a user provides to a medical institution in order to receive medical treatment.

[0557] A "recording function" is a software or hardware function that saves received information to a database.

[0558] A "notification" is a message or alert designed to provide information to a user.

[0559] "Progress information" refers to data that shows the progress of a specific process, in this case, medical treatment.

[0560] "Visit information" refers to data regarding the optimal time for a patient to visit a medical institution for treatment.

[0561] "Analysis" is the process of evaluating and judging information and conditions based on collected data.

[0562] "Urgency" refers to the degree to which a patient's symptoms require immediate attention.

[0563] A "generative AI model" is a programming method that uses artificial intelligence to analyze specific data and derive the optimal result.

[0564] As an embodiment of the present invention, an integrated support system for improving the efficiency of medical treatment at a medical institution is used. This system consists of a server, a terminal, and a user. The server is equipped with software that receives reservation information and records it in a database, and manages the information entered by the user through a web portal. For example, reservation information requires the desired date and time of consultation and a brief description of symptoms. The server also uses an AI model to analyze symptoms and determine their urgency. This AI model employs a generative AI model to calculate the optimal order of consultations and arrival time based on the information entered by the patient.

[0565] The terminal is responsible for sending reminders and notifications related to the patient's medical treatment based on instructions from the server. This includes the date and time of the next appointment, post-treatment instructions, and advice on managing health at home. It also predicts waiting times based on the progress of the treatment and suggests possible ways for the patient to spend their time as needed.

[0566] As a concrete example of operation, consider a case where a user makes a medical appointment through a web platform. When the user registers an appointment, the server saves the information to a database and sends a reminder via the terminal the day before the appointment. On the day of the appointment, the server also receives progress information from the clinic and monitors whether the appointment is proceeding as planned. If there is a delay, the arrival time will be adjusted. In addition, if symptoms such as fever are entered, the server uses an AI algorithm to assess the urgency and displays a notification on the terminal according to the urgency.

[0567] An example of a prompt message can be written to the generating AI model as follows: "Explain the appointment scheduling process for patients with fever symptoms, and then show the subsequent progress management and waiting time reduction process." In this way, it becomes possible to improve the operational efficiency of medical institutions and increase patient satisfaction.

[0568] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0569] Step 1:

[0570] The server receives appointment information from the user. This information includes the user's preferred date and time for the appointment and a brief description of their symptoms, entered via a web platform. The server stores this information in a database. Specifically, a new entry is added to the appointment table in the database, and the server checks whether the appointment details are consistent with other appointment information.

[0571] Step 2:

[0572] As the appointment date approaches, the server sends a reminder to the terminal. The server performs an evaluation by comparing the current date with existing appointment information. The server generates a reminder message the day before the appointment and notifies the user via the terminal. Specifically, the notification data is sent using a message queue.

[0573] Step 3:

[0574] The device displays received reminder messages to the user. The input is the message content received from the server. The device displays the message on the screen with a notification sound, prompting the user to acknowledge it. Specifically, the device uses a notification API to display the message.

[0575] Step 4:

[0576] The server retrieves treatment progress information from medical institutions. The input includes data on the current progress in each examination room. Based on this information, the server analyzes the schedule in real time and determines whether treatment will be completed within the scheduled time. Specifically, it connects the progress database with the schedule management system.

[0577] Step 5:

[0578] If a delay occurs in a patient's appointment, the server adjusts the appointment time and sends this information to the terminal. Inputs include the results of progress analysis and current appointment information. The server calculates the new appointment time, generates adjustment information, and transfers it to the terminal. Specifically, an optimization process using an algorithm is performed.

[0579] Step 6:

[0580] The user inputs their symptoms into the system and uses the diagnostic assistant function. The input is the user's symptom information. The server uses a generated AI model to analyze the symptoms and determine the urgency. The output is a recommendation of countermeasures according to the urgency. Specifically, data is input into the AI ​​model and the analysis results are output.

[0581] Step 7:

[0582] The terminal displays countermeasures and instructions for visiting a hospital, if necessary, to the user based on the urgency assessment. The input consists of recommendations received from the server. The terminal displays detailed instructions to the user and, in urgent cases, prompts for quick action. Specifically, it displays alerts on the user interface.

[0583] Step 8:

[0584] The server generates follow-up information after the medical consultation is completed and automatically schedules the next appointment. Inputs include the consultation results and the next appointment schedule. The server creates a follow-up message and sends it to the user from the terminal. Furthermore, it registers the next appointment in the database. Specifically, data communication is performed using a messaging system.

[0585] (Application Example 1)

[0586] 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."

[0587] Long waiting times at medical facilities hinder efficient patient care and reduce the operational efficiency of these facilities. Furthermore, patients may have difficulty choosing the appropriate medical facility and receiving treatment at the optimal time. This leads to increased patient dissatisfaction and anxiety, ultimately resulting in a decline in the quality of medical services.

[0588] 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.

[0589] In this invention, the server includes means for storing reservation information from users in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of medical treatment and sending notifications to adjust the time of visit, means for interviewing users about their symptoms and determining the urgency, means for instructing users to receive home care or come to the clinic based on their symptoms, means for predicting waiting times and notifying users, means for providing follow-up information after medical treatment and automatically setting the next appointment, and means for recommending the most suitable destination to the user considering traffic conditions. As a result, patients can reduce unnecessary waiting times and receive necessary medical care efficiently and effectively.

[0590] "User" refers to an individual or organization that utilizes the system of the present invention.

[0591] "Reservation information" refers to data related to appointments made by users in advance with medical institutions for the purpose of medical treatment.

[0592] A "storage device" is hardware or software used to store information and make it available as needed.

[0593] A "reminder" is information or a message that is sent in advance to prevent the user from forgetting something.

[0594] "Progress of treatment" refers to information indicating whether treatment at a medical institution is progressing as planned or behind schedule.

[0595] "Arrival time" refers to the time when the user is expected to arrive at the medical institution.

[0596] "Notification" is a means of conveying information to a user, and is a form of information transmission that takes place via voice, message, or application.

[0597] A "medical interview" is a preliminary interview conducted to confirm the patient's symptoms and condition and to determine the direction of medical treatment.

[0598] "Urgency level" is an indicator that shows how urgently a user's symptoms require attention.

[0599] "Home care" refers to health management and first aid that users can perform at home.

[0600] "Waiting time" refers to the time it takes for a user to receive medical services.

[0601] "Follow-up information" refers to information that patients should pay attention to, such as necessary follow-up measures, medication, and lifestyle guidance after a medical examination.

[0602] A "next appointment" is a reservation made in advance for the user's next visit to a medical institution.

[0603] "Traffic conditions" refers to information about road conditions and traffic volume that affect geographical travel.

[0604] A "place of visit" refers to a medical institution or clinic where a user goes to receive medical treatment.

[0605] The system implementing the present invention consists of three elements: a server, a terminal, and a user. The server centrally stores reservation information from users and generates reminders based on this information, which are then sent to the terminal. Furthermore, the server acquires real-time data on the progress of medical treatment provided by medical institutions and transmits notifications to the terminal if delays occur. This notification is effective in preventing users from wasting time waiting.

[0606] The server also analyzes online medical questionnaire data using AI algorithms to determine the urgency of the symptoms. If the urgency is low, it guides the user through their device on how to care for themselves at home; if the urgency is high, it displays an alert instructing them to come to the clinic immediately. This AI analysis uses machine learning frameworks such as TensorFlow.

[0607] For predicting waiting times, the user's device uses the Google Maps API to consider traffic conditions and suggest the most efficient destination and route. The server also automatically manages follow-up after consultations, such as scheduling the next appointment and medication instructions, and notifies the user through their device.

[0608] As a concrete example, consider a scenario where a user enters their fever symptoms into the application. The server analyzes past data to find a medical facility with a short waiting time and recommends it to the user. The user then plans their trip based on the traffic information displayed on their device, enabling a smoother medical consultation.

[0609] An example of a prompt message would be: "User input: 'I have a fever and a headache. Which hospital is the least crowded?'" in response to the AI ​​system's prompt: "Analyzing user symptoms... We will now suggest clinics considering the waiting times at the nearest medical facilities and the urgency of the situation."

[0610] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0611] Step 1:

[0612] The server receives user reservation information and stores it in its storage device. The input is the reservation details, and the output is the stored reservation information. The data is organized and properly stored by a database management system.

[0613] Step 2:

[0614] As the reservation date approaches, the server searches for the user's reservation details in the stored reservation information and sends a reminder notification to the device. The input is the reservation information, and the output is the reminder notification. A search algorithm is used to identify the relevant information, and it is sent to the device via a push notification system.

[0615] Step 3:

[0616] On the day of the consultation, the server retrieves the progress of the consultation from the medical institution in real time. The input is consultation progress data, and the output is updated information on the progress. Data is accessed via API to obtain highly reliable information.

[0617] Step 4:

[0618] If a patient's appointment is delayed, the server analyzes the acquired progress data and notifies the terminal of an appropriate arrival time. Inputs are progress data and appointment data, and output is a notification message. The system compares the data, uses a time adjustment algorithm to calculate the optimal arrival time, and sends a notification to the terminal.

[0619] Step 5:

[0620] The user enters their symptoms into a questionnaire form on their device. The input is symptom data, and the output is an AI-generated assessment of the severity of the symptoms. The server uses a generative AI model to analyze the symptoms and evaluate the severity.

[0621] Step 6:

[0622] The device displays action instructions to the user based on the AI ​​analysis results, tailored to their symptoms. For low-urgency cases, the device provides instructions for home care; for high-urgency cases, it displays a message encouraging immediate hospital visits. The input is the analysis results, and the output is action instructions.

[0623] Step 7:

[0624] The server recommends the optimal destination based on the user's location and traffic data. Inputs are the user's location and traffic data, while output is the recommended destination and travel route. It utilizes location services to analyze data, calculate recommendations, and compiles them into notifications.

[0625] Step 8:

[0626] After the consultation is complete, the server automatically generates the necessary follow-up information and sends it to the terminal along with the next appointment. The input is consultation result data, and the output is next appointment information and follow-up details. An automated appointment system and data management platform are used to generate and provide appropriate information.

[0627] 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.

[0628] This invention proposes a system aimed at reducing hospital waiting times and improving the efficiency of medical treatment, while also providing medical support that takes into account the user's emotional state. In addition to appointment scheduling, diagnostic assistance, waiting time prediction and notification, and post-consultation follow-up, this system incorporates an emotion engine to recognize the user's emotions and improve the quality of medical support.

[0629] 1. In the medical appointment management system, when a user makes an appointment online or by phone, the information is stored in the server's storage device. The server prepares to send a reminder to the terminal the day before the appointment.

[0630] 2. The diagnostic assistant receives the symptoms entered by the user in the online medical questionnaire form and analyzes them using an AI algorithm. This analysis determines the urgency of the situation and provides the user with appropriate instructions (home care or hospital visit).

[0631] 3. On the day of the appointment, the server retrieves the progress of the appointment from the medical staff and sends a notification to the user to adjust their arrival time. This reduces unnecessary waiting time.

[0632] 4. The emotion engine analyzes data collected from the user's device (e.g., facial expressions and voice patterns) to identify the user's emotional state. Based on this information, the server provides care guidance and relaxation content to reduce stress.

[0633] 5. Waiting time prediction is performed by the server, which utilizes real-time data from the clinic to notify the user of the expected waiting time. If a long wait is predicted, the system will suggest more comfortable waiting methods via the terminal.

[0634] 6. After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The terminal notifies the user of this information to support continuous health management.

[0635] For example, if a user requests a medical consultation due to illness, the server records the appointment once it's completed and sends a reminder to the user's device the day before. On the day of the appointment, the user's arrival time is adjusted based on the progress of the medical staff's consultations, and a notification is sent to the device. The emotion engine senses the user's anxiety and suggests playing relaxation music. After the consultation, follow-up information, including how to take medication, is displayed on the device, and the next appointment is automatically scheduled. This entire process reduces patient waiting times and improves the efficiency of hospital consultations.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] Users make appointments online or by phone. Users provide the necessary information, including the patient's name, appointment date and time, and the doctor in charge.

[0639] Step 2:

[0640] The server receives the reservation information and records it in the database. The recorded data is stored for later reference and processing.

[0641] Step 3:

[0642] The server creates a reminder the day before the reservation date and sets up a notification on the user's device.

[0643] Step 4:

[0644] The day before the reservation date, the device displays a reminder to the user saying, "You have a reservation tomorrow at 10 AM," and asks for confirmation.

[0645] Step 5:

[0646] On the day of the appointment, the server retrieves progress data from the medical staff. This data is used to verify that the appointment is proceeding as scheduled.

[0647] Step 6:

[0648] If the server detects a delay in the patient's appointment, it will adjust the user's arrival time and notify them of specific instructions via their device.

[0649] Step 7:

[0650] Before their appointment, users fill out and submit an online questionnaire about their symptoms. This information includes a detailed description of their symptoms and their medical history.

[0651] Step 8:

[0652] The server analyzes the patient information using an AI algorithm and assesses the urgency of the situation. Processing proceeds based on the severity level.

[0653] Step 9:

[0654] The device notifies the user of instructions such as "You can rest at home" or "Please come to the hospital immediately" based on the analysis results.

[0655] Step 10:

[0656] The emotion engine acquires emotion data on the device. This data is collected by reading emotions from the user's facial expressions and voice.

[0657] Step 11:

[0658] Based on data from the emotion engine, the server analyzes the user's stress level and suggests relaxation methods as needed.

[0659] Step 12:

[0660] The server predicts the waiting time and, based on reservation status and progress data, notifies the user's device of the estimated waiting time. Information such as "The current waiting time is approximately 30 minutes" is provided.

[0661] Step 13:

[0662] After the consultation, the server creates follow-up information based on the consultation results and automatically schedules the next appointment.

[0663] Step 14:

[0664] The device displays follow-up information and details of the next appointment to the user, supporting continuous health management.

[0665] (Example 2)

[0666] 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."

[0667] Traditional healthcare systems have suffered from problems such as long waiting times and decreased efficiency in medical care, leading to lower patient satisfaction. Furthermore, the quality of medical services was limited because patients' emotional states were not taken into consideration. To solve these problems, a system is needed that can efficiently manage everything from appointment scheduling to follow-up, recognize patients' emotions, and provide appropriate care.

[0668] 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.

[0669] In this invention, the server includes means for identifying the user's emotional state and providing appropriate content, means for predicting waiting times and sending notifications to suggest comfortable waiting methods when long waiting times are expected, and means for obtaining the progress of medical treatment and sending notifications to adjust the time of arrival. This enables the provision of medical services that take into account the patient's emotional state, thereby reducing waiting times and improving the efficiency of medical treatment.

[0670] "Users" refers to patients or individuals who use the system to make appointments or utilize medical services.

[0671] "Reservation information" refers to information regarding the date, time, and content of the consultation that a user has secured in advance for a medical appointment.

[0672] A "storage device" refers to a hardware or software medium for storing digital data.

[0673] A "reminder" refers to a function or service that notifies users of the date and time of their medical appointment.

[0674] "Healthcare professionals" refers to specialists or staff involved in the progress of medical procedures and the treatment of patients.

[0675] "Medical treatment progress" refers to information regarding the process and status of medical treatment.

[0676] "Urgency" refers to an indicator that shows the severity of the symptoms and how quickly necessary the response should be.

[0677] A "generative model" refers to an artificial intelligence algorithm that runs on a computer and performs data processing and prediction.

[0678] "Emotional state" refers to the psychological or emotional response exhibited by the user.

[0679] "Comfortable waiting methods" refer to suggestions or services that enable users to wait in a better environment when long waiting periods are anticipated.

[0680] "Follow-up information" refers to instructions and advice regarding treatment and health management after a medical examination.

[0681] This system aims to reduce hospital waiting times and improve the efficiency of medical treatment, providing medical support that takes into account the user's emotional state. The system operates based on information exchanged between the server, terminals, and users.

[0682] First, users make appointments online or by phone. The server stores the received appointment information in a database and prepares to send a reminder to the user's device the day before the appointment. The database uses advanced storage technology to ensure the accuracy of the information.

[0683] On the day of the appointment, the server receives the appointment progress information entered by the medical staff and adjusts the user's arrival time. Real-time communication technology is used for this information exchange. The user's terminal is notified of the adjusted arrival time, which helps reduce unnecessary waiting time.

[0684] Furthermore, this system utilizes a generative AI model. The server analyzes symptom data entered by the user into an online questionnaire form to determine the urgency of the situation. This generative model enables the rapid provision of appropriate instructions to the user. In addition, to identify the user's emotional state, the server uses an emotion engine to analyze facial expressions and voice patterns transmitted from the user's device. If the server determines that the user is experiencing stress, it provides relaxation content to the device.

[0685] Real-time data from the examination rooms is used to predict waiting times. If a long waiting time is expected, the server will offer the user beverage or entertainment options via their terminal.

[0686] After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The user's device receives notifications regarding medication usage and health management advice, supporting continuous health management.

[0687] For example, if a user feels unwell and wishes to seek medical attention, this system allows for quick and efficient scheduling, from making an appointment to post-consultation follow-up.

[0688] Examples of prompt messages include the following:

[0689] "Please use an AI algorithm to analyze the urgency level based on the symptom data collected through the online medical questionnaire."

[0690] "Please describe the process of using real-time data to predict waiting times and notifying users accordingly."

[0691] It is expected that using this system will improve the efficiency of hospital medical care and increase patient satisfaction.

[0692] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0693] Step 1:

[0694] Users make appointments online or by phone. They provide their desired appointment date and time, as well as user information. The server receives this information and stores it in its storage. Specifically, the server completes the appointment by saving the date, time, and user ID to its database.

[0695] Step 2:

[0696] The day before the reservation date, the server retrieves the reservation information from storage and creates a reminder. The input includes the reservation date and time and the user's contact information. The server sends a notification to the device via email or app. As output, a reservation confirmation is displayed on the user's device. As a concrete example of sending a reminder, a notification message is sent to the registered email address.

[0697] Step 3:

[0698] On the day of the appointment, healthcare professionals enter information about the progress of the consultation into the system. The server receives this information and adjusts the patient's appointment time based on the actual consultation schedule. As output, a notification of the adjusted appointment time is sent to the terminal. Specific actions include updating the schedule and notifying the user.

[0699] Step 4:

[0700] The user enters their symptoms into an online medical questionnaire form. The server processes this data using an AI model to determine the urgency of the situation. The input includes text data about the symptoms. The server outputs the analysis results and notifies the terminal with appropriate instructions. Specifically, if the AI ​​determines the situation is "highly urgent," it advises the user to come to the clinic immediately.

[0701] Step 5:

[0702] In analyzing emotional states, the device collects facial image and audio data from the user and sends it to the server. The server analyzes the data using an emotion engine to identify the user's emotions. The output provides the device with content designed to reduce stress. Specifically, if the system determines that the user is feeling anxious, it plays relaxation music.

[0703] Step 6:

[0704] The server predicts waiting times based on real-time data. Inputs include the current operating status of the clinics and appointment information. The output notifies the user of the predicted waiting time and suggests alternative waiting methods. For example, it might suggest providing refreshments in the waiting room.

[0705] Step 7:

[0706] After the consultation, the server generates follow-up information based on the consultation details and automatically sets the next appointment. Inputs are the consultation report and the recommended date and time for the next consultation. Output is a notification to the terminal containing consultation advice and details of the next appointment. Specifically, a predetermined algorithm determines and notifies the patient of the recommended date for the next consultation.

[0707] (Application Example 2)

[0708] 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."

[0709] In an aging society, the demand for long-term care services is increasing, but long waiting times for users and the efficiency of individualized care plans remain challenges. Furthermore, the lack of adequate care that takes into account users' emotional states is contributing to low user satisfaction. Moreover, with the provision of online services, there is a growing need for real-time information sharing and service delivery that takes emotional states into account. Therefore, there is a need for efficient and effective solutions to these challenges.

[0710] 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.

[0711] In this invention, the server includes means for storing user reservation information in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of treatment and sending notifications to adjust the appointment time, means for recognizing the user's emotions and providing information to reduce stress, and means for providing follow-up information after treatment and automatically setting the next appointment. This makes it possible to provide services tailored to the individual needs of each user, thereby reducing waiting times and improving satisfaction.

[0712] "Reservation information" refers to data regarding the date, time, and content of a service provided by a user when they apply for the service in advance.

[0713] A "storage device" is a system of hardware or software that stores digital data and makes it accessible as needed.

[0714] A "reminder" is a means of notifying users of pre-set events to help them remember them.

[0715] "Progress of treatment" refers to information regarding the current stage of progress or completion within the treatment process.

[0716] "Notifications to adjust appointment times" refer to information sent to users to optimize their scheduled appointment times and inform them of appropriate times to arrive at the clinic.

[0717] "Emotional recognition" is the process of identifying a user's emotional state and collecting and analyzing the information necessary to respond appropriately to that emotion.

[0718] "Information for reducing stress" refers to data on advice and relaxation content provided with the aim of reducing the psychological burden on users.

[0719] "Follow-up information" refers to additional announcements after the service has been provided, as well as information regarding future activity plans.

[0720] "Next reservation" refers to reservation information prepared in advance for a user's next visit.

[0721] This invention is a system for improving the quality of services provided to users in nursing care facilities or home care settings. Several key components are necessary to realize this system.

[0722] First, the server receives user reservation information and stores it in storage. A database management system is used for this, such as relational databases like MySQL or PostgreSQL. The server also builds the backend using programming languages ​​like Go or Python, allowing users to easily access the reservation information.

[0723] Next, the server sends a reminder notification to the user based on the reservation information. This is achieved using push notification technology (such as Google Firebase Cloud Messaging). The reminder is then sent to the user's device, such as a smartphone or tablet.

[0724] Furthermore, if a consultation is in progress, the server tracks the progress of the consultation and sends notifications in real time to adjust the user's appointment time. This incorporates a tracking and notification system that works with the frontend by leveraging JavaScript frameworks in addition to Go and Python.

[0725] Furthermore, for emotion recognition, the user's device acquires facial expressions and voice data and sends this data to the server. The server analyzes the emotional state using tools such as TensorFlow and OpenCV. Based on the results, it provides information and care plans to reduce stress.

[0726] As a concrete example, when a user books a care service, the server records it and sends a reminder the day before the scheduled appointment. On the day of the appointment, if the emotion engine detects that the user is feeling anxious, a notification is sent to the user's device suggesting that relaxation music be played. Once the service is completed, the next appointment is automatically scheduled and the user is notified.

[0727] Here's an example of a prompt using a generative AI model to provide flexible guidance based on emotional state: "Please input smile expression data, analyze the user's emotional state, and suggest relaxation content."

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The server receives reservation information from users and stores it in a database. Inputs include the user's reservation date and time, location, and service details, and this data is structured and stored by the database management system. The output is a confirmation message indicating that the reservation information has been accurately recorded.

[0731] Step 2:

[0732] The server sends a reminder to the user the day before the reservation date. The inputs are reservation information retrieved from the database and the user's contact information. The data processing involves generating a reminder message and sending it to the user's device via a push notification system. The output is the notification message received by the user.

[0733] Step 3:

[0734] On the day of the appointment, the server tracks the progress of the consultation in real time and sends notifications to the user to adjust their appointment time. The input is progress data obtained from the consultation room, and the server uses this to calculate the optimal appointment time. The output is a notification message that includes the adjusted appointment time.

[0735] Step 4:

[0736] The user's device collects facial and voice data for emotion recognition and sends it to the server. The input is real-time data from the user's camera and microphone, which is analyzed using data processing tools such as TensorFlow and OpenCV. The output is the analysis result indicating the user's emotional state.

[0737] Step 5:

[0738] The server generates and provides information to reduce stress based on the user's emotional state. The input is the result of emotion recognition analysis, which the AI ​​model uses to select relaxation content and care plans. The output is a suggestion notification displayed on the user's device.

[0739] Step 6:

[0740] After the consultation, the server generates follow-up information and automatically schedules the next appointment. Inputs include the consultation record and the user's preferred date and time for the next appointment. Data processing generates follow-up information and determines the next appointment date and time. Output is a confirmation message for the next appointment sent to the user.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] [Fourth Embodiment]

[0745] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0746] 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.

[0747] 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).

[0748] 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.

[0749] 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.

[0750] 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).

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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".

[0758] The present invention is an integrated support system that aims to improve the efficiency of waiting times in hospitals by providing users with reservation management, diagnostic assistance, waiting time notifications, and post-consultation follow-up. The goal is to reduce patient waiting times and improve the operational efficiency of medical institutions.

[0759] 1. Regarding the reception and storage of appointment information, the server receives appointment requests from users (patients) via the online platform or telephone and records them in the database. As the appointment date approaches, the terminal sends a reminder to the patient to prompt them to reconfirm their appointment.

[0760] 2. In managing the progress of medical treatment, the server retrieves progress information from healthcare professionals and verifies whether treatment is proceeding as planned. If delays occur, it notifies patients of the appropriate arrival time, reducing unnecessary waiting in the waiting room.

[0761] 3. In the diagnostic assistant function, users can input their symptoms online. Based on this information, the server uses AI to analyze the symptoms and determine the urgency of the patient's condition. The terminal displays messages such as home care instructions for patients with low urgency, and prompt hospital visits for those with high urgency.

[0762] 4. As a waiting time notification function, the server compares the progress data of medical treatments with reservation information to predict waiting times. It then notifies the patient of the waiting time via their terminal, and if a long wait is expected, it suggests ways to utilize the time outside the hospital.

[0763] 5. During post-consultation follow-up, the server automatically sets up post-consultation instructions and the next appointment, and sends them to the patient via their terminal. This ensures that patients do not forget to make their next appointment and supports health management based on their consultation results.

[0764] As a concrete example, if a patient makes an appointment for a medical consultation due to fever, the server saves the appointment and sends a reminder notification to the device the day before. Based on the progress on the day of the consultation, the server adjusts the patient's arrival time as needed and notifies them to minimize waiting time. The diagnostic assistant instructs the patient on fever-reducing measures they can take at home and instructs them to come to the clinic if necessary. After the consultation, the device notifies the patient about medication and lifestyle precautions, and the next appointment is automatically scheduled. This reduces patient waiting times and enables efficient operation of medical facilities.

[0765] The following describes the processing flow.

[0766] Step 1:

[0767] Users make appointments online or by phone. They enter or provide information such as the appointment date and time, the doctor in charge, and their symptoms.

[0768] Step 2:

[0769] The server stores the reservation information received from the user in a database. It then verifies that the reservation data has been recorded accurately.

[0770] Step 3:

[0771] The server sets a reminder the day before the reservation date and prepares to send a reservation notification to the device.

[0772] Step 4:

[0773] The device displays a reminder to the user the day before the reservation date, stating, "You have a reservation tomorrow at 10 AM," prompting them to confirm.

[0774] Step 5:

[0775] The server receives progress reports from healthcare professionals on the day of the consultation and verifies that the schedule is progressing as planned.

[0776] Step 6:

[0777] If there is a delay in the consultation, the server will adjust the user's arrival time and send a notification to their device.

[0778] Step 7:

[0779] When a user enters their symptoms into an online medical questionnaire, the server receives and stores the data.

[0780] Step 8:

[0781] The server uses an AI algorithm to analyze symptom data and determine the urgency of the patient's condition.

[0782] Step 9:

[0783] Based on the analysis results, the device displays home care guidance for patients with mild symptoms, and messages urging them to come to the hospital immediately if their condition is more urgent.

[0784] Step 10:

[0785] The server predicts waiting times based on patient appointment status and treatment progress data, and calculates the optimal waiting time.

[0786] Step 11:

[0787] The device notifies the user of the waiting time, displaying a message such as, "The current waiting time is approximately 20 minutes." If the wait is prolonged, it suggests an efficient waiting location.

[0788] Step 12:

[0789] After the consultation, the server generates follow-up information based on the patient's data and automatically schedules the next appointment.

[0790] Step 13:

[0791] The device notifies the user of the details of the post-consultation follow-up and information about the next appointment, supporting their health management.

[0792] (Example 1)

[0793] 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".

[0794] It is necessary to reduce waiting times at medical institutions to improve patient convenience while maintaining an appropriate order of consultations. However, the current system is inadequate in appointment management and post-diagnosis follow-up, reducing efficiency for both patients and medical institutions. Furthermore, it is difficult to accurately assess the urgency of a patient's condition, making it challenging to provide optimal medical care.

[0795] 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.

[0796] In this invention, the server includes means for storing reservation information from users in a recording function, means for acquiring information on the progress of medical treatment and adjusting visit information to improve convenience, and means for analyzing the symptoms of users and evaluating their urgency. This makes it possible to improve the efficiency of medical treatment at medical institutions, reduce patient waiting times, and enable appropriate medical treatment.

[0797] "Reservation information" refers to information about the date, time, and symptoms that a user provides to a medical institution in order to receive medical treatment.

[0798] A "recording function" is a software or hardware function that saves received information to a database.

[0799] A "notification" is a message or alert designed to provide information to a user.

[0800] "Progress information" refers to data that shows the progress of a specific process, in this case, medical treatment.

[0801] "Visit information" refers to data regarding the optimal time for a patient to visit a medical institution for treatment.

[0802] "Analysis" is the process of evaluating and judging information and conditions based on collected data.

[0803] "Urgency" refers to the degree to which a patient's symptoms require immediate attention.

[0804] A "generative AI model" is a programming method that uses artificial intelligence to analyze specific data and derive the optimal result.

[0805] As an embodiment of the present invention, an integrated support system for improving the efficiency of medical treatment at a medical institution is used. This system consists of a server, a terminal, and a user. The server is equipped with software that receives reservation information and records it in a database, and manages the information entered by the user through a web portal. For example, reservation information requires the desired date and time of consultation and a brief description of symptoms. The server also uses an AI model to analyze symptoms and determine their urgency. This AI model employs a generative AI model to calculate the optimal order of consultations and arrival time based on the information entered by the patient.

[0806] The terminal is responsible for sending reminders and notifications related to the patient's medical treatment based on instructions from the server. This includes the date and time of the next appointment, post-treatment instructions, and advice on managing health at home. It also predicts waiting times based on the progress of the treatment and suggests possible ways for the patient to spend their time as needed.

[0807] As a concrete example of operation, consider a case where a user makes a medical appointment through a web platform. When the user registers an appointment, the server saves the information to a database and sends a reminder via the terminal the day before the appointment. On the day of the appointment, the server also receives progress information from the clinic and monitors whether the appointment is proceeding as planned. If there is a delay, the arrival time will be adjusted. In addition, if symptoms such as fever are entered, the server uses an AI algorithm to assess the urgency and displays a notification on the terminal according to the urgency.

[0808] An example of a prompt message can be written to the generating AI model as follows: "Explain the appointment scheduling process for patients with fever symptoms, and then show the subsequent progress management and waiting time reduction process." In this way, it becomes possible to improve the operational efficiency of medical institutions and increase patient satisfaction.

[0809] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0810] Step 1:

[0811] The server receives appointment information from the user. This information includes the user's preferred date and time for the appointment and a brief description of their symptoms, entered via a web platform. The server stores this information in a database. Specifically, a new entry is added to the appointment table in the database, and the server checks whether the appointment details are consistent with other appointment information.

[0812] Step 2:

[0813] As the appointment date approaches, the server sends a reminder to the terminal. The server performs an evaluation by comparing the current date with existing appointment information. The server generates a reminder message the day before the appointment and notifies the user via the terminal. Specifically, the notification data is sent using a message queue.

[0814] Step 3:

[0815] The device displays received reminder messages to the user. The input is the message content received from the server. The device displays the message on the screen with a notification sound, prompting the user to acknowledge it. Specifically, the device uses a notification API to display the message.

[0816] Step 4:

[0817] The server retrieves treatment progress information from medical institutions. The input includes data on the current progress in each examination room. Based on this information, the server analyzes the schedule in real time and determines whether treatment will be completed within the scheduled time. Specifically, it connects the progress database with the schedule management system.

[0818] Step 5:

[0819] If a delay occurs in a patient's appointment, the server adjusts the appointment time and sends this information to the terminal. Inputs include the results of progress analysis and current appointment information. The server calculates the new appointment time, generates adjustment information, and transfers it to the terminal. Specifically, an optimization process using an algorithm is performed.

[0820] Step 6:

[0821] The user inputs their symptoms into the system and uses the diagnostic assistant function. The input is the user's symptom information. The server uses a generated AI model to analyze the symptoms and determine the urgency. The output is a recommendation of countermeasures according to the urgency. Specifically, data is input into the AI ​​model and the analysis results are output.

[0822] Step 7:

[0823] The terminal displays countermeasures and instructions for visiting a hospital, if necessary, to the user based on the urgency assessment. The input consists of recommendations received from the server. The terminal displays detailed instructions to the user and, in urgent cases, prompts for quick action. Specifically, it displays alerts on the user interface.

[0824] Step 8:

[0825] The server generates follow-up information after the medical consultation is completed and automatically schedules the next appointment. Inputs include the consultation results and the next appointment schedule. The server creates a follow-up message and sends it to the user from the terminal. Furthermore, it registers the next appointment in the database. Specifically, data communication is performed using a messaging system.

[0826] (Application Example 1)

[0827] 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".

[0828] Long waiting times at medical facilities hinder efficient patient care and reduce the operational efficiency of these facilities. Furthermore, patients may have difficulty choosing the appropriate medical facility and receiving treatment at the optimal time. This leads to increased patient dissatisfaction and anxiety, ultimately resulting in a decline in the quality of medical services.

[0829] 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.

[0830] In this invention, the server includes means for storing reservation information from users in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of medical treatment and sending notifications to adjust the time of visit, means for interviewing users about their symptoms and determining the urgency, means for instructing users to receive home care or come to the clinic based on their symptoms, means for predicting waiting times and notifying users, means for providing follow-up information after medical treatment and automatically setting the next appointment, and means for recommending the most suitable destination to the user considering traffic conditions. As a result, patients can reduce unnecessary waiting times and receive necessary medical care efficiently and effectively.

[0831] "User" refers to an individual or organization that utilizes the system of the present invention.

[0832] "Reservation information" refers to data related to appointments made by users in advance with medical institutions for the purpose of medical treatment.

[0833] A "storage device" is hardware or software used to store information and make it available as needed.

[0834] A "reminder" is information or a message that is sent in advance to prevent the user from forgetting something.

[0835] "Progress of treatment" refers to information indicating whether treatment at a medical institution is progressing as planned or behind schedule.

[0836] "Arrival time" refers to the time when the user is expected to arrive at the medical institution.

[0837] "Notification" is a means of conveying information to a user, and is a form of information transmission that takes place via voice, message, or application.

[0838] A "medical interview" is a preliminary interview conducted to confirm the patient's symptoms and condition and to determine the direction of medical treatment.

[0839] "Urgency level" is an indicator that shows how urgently a user's symptoms require attention.

[0840] "Home care" refers to health management and first aid that users can perform at home.

[0841] "Waiting time" refers to the time it takes for a user to receive medical services.

[0842] "Follow-up information" refers to information that patients should pay attention to, such as necessary follow-up measures, medication, and lifestyle guidance after a medical examination.

[0843] A "next appointment" is a reservation made in advance for the user's next visit to a medical institution.

[0844] "Traffic conditions" refers to information about road conditions and traffic volume that affect geographical travel.

[0845] A "place of visit" refers to a medical institution or clinic where a user goes to receive medical treatment.

[0846] The system implementing the present invention consists of three elements: a server, a terminal, and a user. The server centrally stores reservation information from users and generates reminders based on this information, which are then sent to the terminal. Furthermore, the server acquires real-time data on the progress of medical treatment provided by medical institutions and transmits notifications to the terminal if delays occur. This notification is effective in preventing users from wasting time waiting.

[0847] The server also analyzes online medical questionnaire data using AI algorithms to determine the urgency of the symptoms. If the urgency is low, it guides the user through their device on how to care for themselves at home; if the urgency is high, it displays an alert instructing them to come to the clinic immediately. This AI analysis uses machine learning frameworks such as TensorFlow.

[0848] For predicting waiting times, the user's device uses the Google Maps API to consider traffic conditions and suggest the most efficient destination and route. The server also automatically manages follow-up after consultations, such as scheduling the next appointment and medication instructions, and notifies the user through their device.

[0849] As a concrete example, consider a scenario where a user enters their fever symptoms into the application. The server analyzes past data to find a medical facility with a short waiting time and recommends it to the user. The user then plans their trip based on the traffic information displayed on their device, enabling a smoother medical consultation.

[0850] An example of a prompt message would be: "User input: 'I have a fever and a headache. Which hospital is the least crowded?'" in response to the AI ​​system's prompt: "Analyzing user symptoms... We will now suggest clinics considering the waiting times at the nearest medical facilities and the urgency of the situation."

[0851] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0852] Step 1:

[0853] The server receives user reservation information and stores it in its storage device. The input is the reservation details, and the output is the stored reservation information. The data is organized and properly stored by a database management system.

[0854] Step 2:

[0855] As the reservation date approaches, the server searches for the user's reservation details in the stored reservation information and sends a reminder notification to the device. The input is the reservation information, and the output is the reminder notification. A search algorithm is used to identify the relevant information, and it is sent to the device via a push notification system.

[0856] Step 3:

[0857] On the day of the consultation, the server retrieves the progress of the consultation from the medical institution in real time. The input is consultation progress data, and the output is updated information on the progress. Data is accessed via API to obtain highly reliable information.

[0858] Step 4:

[0859] If a patient's appointment is delayed, the server analyzes the acquired progress data and notifies the terminal of an appropriate arrival time. Inputs are progress data and appointment data, and output is a notification message. The system compares the data, uses a time adjustment algorithm to calculate the optimal arrival time, and sends a notification to the terminal.

[0860] Step 5:

[0861] The user enters their symptoms into a questionnaire form on their device. The input is symptom data, and the output is an AI-generated assessment of the severity of the symptoms. The server uses a generative AI model to analyze the symptoms and evaluate the severity.

[0862] Step 6:

[0863] The device displays action instructions to the user based on the AI ​​analysis results, tailored to their symptoms. For low-urgency cases, the device provides instructions for home care; for high-urgency cases, it displays a message encouraging immediate hospital visits. The input is the analysis results, and the output is action instructions.

[0864] Step 7:

[0865] The server recommends the optimal destination based on the user's location and traffic data. Inputs are the user's location and traffic data, while output is the recommended destination and travel route. It utilizes location services to analyze data, calculate recommendations, and compiles them into notifications.

[0866] Step 8:

[0867] After the consultation is complete, the server automatically generates the necessary follow-up information and sends it to the terminal along with the next appointment. The input is consultation result data, and the output is next appointment information and follow-up details. An automated appointment system and data management platform are used to generate and provide appropriate information.

[0868] 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.

[0869] This invention proposes a system aimed at reducing hospital waiting times and improving the efficiency of medical treatment, while also providing medical support that takes into account the user's emotional state. In addition to appointment scheduling, diagnostic assistance, waiting time prediction and notification, and post-consultation follow-up, this system incorporates an emotion engine to recognize the user's emotions and improve the quality of medical support.

[0870] 1. In the medical appointment management system, when a user makes an appointment online or by phone, the information is stored in the server's storage device. The server prepares to send a reminder to the terminal the day before the appointment.

[0871] 2. The diagnostic assistant receives the symptoms entered by the user in the online medical questionnaire form and analyzes them using an AI algorithm. This analysis determines the urgency of the situation and provides the user with appropriate instructions (home care or hospital visit).

[0872] 3. On the day of the appointment, the server retrieves the progress of the appointment from the medical staff and sends a notification to the user to adjust their arrival time. This reduces unnecessary waiting time.

[0873] 4. The emotion engine analyzes data collected from the user's device (e.g., facial expressions and voice patterns) to identify the user's emotional state. Based on this information, the server provides care guidance and relaxation content to reduce stress.

[0874] 5. Waiting time prediction is performed by the server, which utilizes real-time data from the clinic to notify the user of the expected waiting time. If a long wait is predicted, the system will suggest more comfortable waiting methods via the terminal.

[0875] 6. After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The terminal notifies the user of this information to support continuous health management.

[0876] For example, if a user requests a medical consultation due to illness, the server records the appointment once it's completed and sends a reminder to the user's device the day before. On the day of the appointment, the user's arrival time is adjusted based on the progress of the medical staff's consultations, and a notification is sent to the device. The emotion engine senses the user's anxiety and suggests playing relaxation music. After the consultation, follow-up information, including how to take medication, is displayed on the device, and the next appointment is automatically scheduled. This entire process reduces patient waiting times and improves the efficiency of hospital consultations.

[0877] The following describes the processing flow.

[0878] Step 1:

[0879] Users make appointments online or by phone. Users provide the necessary information, including the patient's name, appointment date and time, and the doctor in charge.

[0880] Step 2:

[0881] The server receives the reservation information and records it in the database. The recorded data is stored for later reference and processing.

[0882] Step 3:

[0883] The server creates a reminder the day before the reservation date and sets up a notification on the user's device.

[0884] Step 4:

[0885] The day before the reservation date, the device displays a reminder to the user saying, "You have a reservation tomorrow at 10 AM," and asks for confirmation.

[0886] Step 5:

[0887] On the day of the appointment, the server retrieves progress data from the medical staff. This data is used to verify that the appointment is proceeding as scheduled.

[0888] Step 6:

[0889] If the server detects a delay in the patient's appointment, it will adjust the user's arrival time and notify them of specific instructions via their device.

[0890] Step 7:

[0891] Before their appointment, users fill out and submit an online questionnaire about their symptoms. This information includes a detailed description of their symptoms and their medical history.

[0892] Step 8:

[0893] The server analyzes the patient information using an AI algorithm and assesses the urgency of the situation. Processing proceeds based on the severity level.

[0894] Step 9:

[0895] The device notifies the user of instructions such as "You can rest at home" or "Please come to the hospital immediately" based on the analysis results.

[0896] Step 10:

[0897] The emotion engine acquires emotion data on the device. This data is collected by reading emotions from the user's facial expressions and voice.

[0898] Step 11:

[0899] Based on data from the emotion engine, the server analyzes the user's stress level and suggests relaxation methods as needed.

[0900] Step 12:

[0901] The server predicts the waiting time and, based on reservation status and progress data, notifies the user's device of the estimated waiting time. Information such as "The current waiting time is approximately 30 minutes" is provided.

[0902] Step 13:

[0903] After the consultation, the server creates follow-up information based on the consultation results and automatically schedules the next appointment.

[0904] Step 14:

[0905] The device displays follow-up information and details of the next appointment to the user, supporting continuous health management.

[0906] (Example 2)

[0907] 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".

[0908] Traditional healthcare systems have suffered from problems such as long waiting times and decreased efficiency in medical care, leading to lower patient satisfaction. Furthermore, the quality of medical services was limited because patients' emotional states were not taken into consideration. To solve these problems, a system is needed that can efficiently manage everything from appointment scheduling to follow-up, recognize patients' emotions, and provide appropriate care.

[0909] 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.

[0910] In this invention, the server includes means for identifying the user's emotional state and providing appropriate content, means for predicting waiting times and sending notifications to suggest comfortable waiting methods when long waiting times are expected, and means for obtaining the progress of medical treatment and sending notifications to adjust the time of arrival. This enables the provision of medical services that take into account the patient's emotional state, thereby reducing waiting times and improving the efficiency of medical treatment.

[0911] "Users" refers to patients or individuals who use the system to make appointments or utilize medical services.

[0912] "Reservation information" refers to information regarding the date, time, and content of the consultation that a user has secured in advance for a medical appointment.

[0913] A "storage device" refers to a hardware or software medium for storing digital data.

[0914] A "reminder" refers to a function or service that notifies users of the date and time of their medical appointment.

[0915] "Healthcare professionals" refers to specialists or staff involved in the progress of medical procedures and the treatment of patients.

[0916] "Medical treatment progress" refers to information regarding the process and status of medical treatment.

[0917] "Urgency" refers to an indicator that shows the severity of the symptoms and how quickly necessary the response should be.

[0918] A "generative model" refers to an artificial intelligence algorithm that runs on a computer and performs data processing and prediction.

[0919] "Emotional state" refers to the psychological or emotional response exhibited by the user.

[0920] "Comfortable waiting methods" refer to suggestions or services that enable users to wait in a better environment when long waiting periods are anticipated.

[0921] "Follow-up information" refers to instructions and advice regarding treatment and health management after a medical examination.

[0922] This system aims to reduce hospital waiting times and improve the efficiency of medical treatment, providing medical support that takes into account the user's emotional state. The system operates based on information exchanged between the server, terminals, and users.

[0923] First, users make appointments online or by phone. The server stores the received appointment information in a database and prepares to send a reminder to the user's device the day before the appointment. The database uses advanced storage technology to ensure the accuracy of the information.

[0924] On the day of the appointment, the server receives the appointment progress information entered by the medical staff and adjusts the user's arrival time. Real-time communication technology is used for this information exchange. The user's terminal is notified of the adjusted arrival time, which helps reduce unnecessary waiting time.

[0925] Furthermore, this system utilizes a generative AI model. The server analyzes symptom data entered by the user into an online questionnaire form to determine the urgency of the situation. This generative model enables the rapid provision of appropriate instructions to the user. In addition, to identify the user's emotional state, the server uses an emotion engine to analyze facial expressions and voice patterns transmitted from the user's device. If the server determines that the user is experiencing stress, it provides relaxation content to the device.

[0926] Real-time data from the examination rooms is used to predict waiting times. If a long waiting time is expected, the server will offer the user beverage or entertainment options via their terminal.

[0927] After the consultation, the server generates follow-up information based on the consultation details and automatically schedules the next appointment. The user's device receives notifications regarding medication usage and health management advice, supporting continuous health management.

[0928] For example, if a user feels unwell and wishes to seek medical attention, this system allows for quick and efficient scheduling, from making an appointment to post-consultation follow-up.

[0929] Examples of prompt messages include the following:

[0930] "Please use an AI algorithm to analyze the urgency level based on the symptom data collected through the online medical questionnaire."

[0931] "Please describe the process of using real-time data to predict waiting times and notifying users accordingly."

[0932] It is expected that using this system will improve the efficiency of hospital medical care and increase patient satisfaction.

[0933] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0934] Step 1:

[0935] Users make appointments online or by phone. They provide their desired appointment date and time, as well as user information. The server receives this information and stores it in its storage. Specifically, the server completes the appointment by saving the date, time, and user ID to its database.

[0936] Step 2:

[0937] The day before the reservation date, the server retrieves the reservation information from storage and creates a reminder. The input includes the reservation date and time and the user's contact information. The server sends a notification to the device via email or app. As output, a reservation confirmation is displayed on the user's device. As a concrete example of sending a reminder, a notification message is sent to the registered email address.

[0938] Step 3:

[0939] On the day of the appointment, healthcare professionals enter information about the progress of the consultation into the system. The server receives this information and adjusts the patient's appointment time based on the actual consultation schedule. As output, a notification of the adjusted appointment time is sent to the terminal. Specific actions include updating the schedule and notifying the user.

[0940] Step 4:

[0941] The user enters their symptoms into an online medical questionnaire form. The server processes this data using an AI model to determine the urgency of the situation. The input includes text data about the symptoms. The server outputs the analysis results and notifies the terminal with appropriate instructions. Specifically, if the AI ​​determines the situation is "highly urgent," it advises the user to come to the clinic immediately.

[0942] Step 5:

[0943] In analyzing emotional states, the device collects facial image and audio data from the user and sends it to the server. The server analyzes the data using an emotion engine to identify the user's emotions. The output provides the device with content designed to reduce stress. Specifically, if the system determines that the user is feeling anxious, it plays relaxation music.

[0944] Step 6:

[0945] The server predicts waiting times based on real-time data. Inputs include the current operating status of the clinics and appointment information. The output notifies the user of the predicted waiting time and suggests alternative waiting methods. For example, it might suggest providing refreshments in the waiting room.

[0946] Step 7:

[0947] After the consultation, the server generates follow-up information based on the consultation details and automatically sets the next appointment. Inputs are the consultation report and the recommended date and time for the next consultation. Output is a notification to the terminal containing consultation advice and details of the next appointment. Specifically, a predetermined algorithm determines and notifies the patient of the recommended date for the next consultation.

[0948] (Application Example 2)

[0949] 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".

[0950] In an aging society, the demand for long-term care services is increasing, but long waiting times for users and the efficiency of individualized care plans remain challenges. Furthermore, the lack of adequate care that takes into account users' emotional states is contributing to low user satisfaction. Moreover, with the provision of online services, there is a growing need for real-time information sharing and service delivery that takes emotional states into account. Therefore, there is a need for efficient and effective solutions to these challenges.

[0951] 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.

[0952] In this invention, the server includes means for storing user reservation information in a storage device, means for sending reminders to users based on the reservation information, means for obtaining the progress of treatment and sending notifications to adjust the appointment time, means for recognizing the user's emotions and providing information to reduce stress, and means for providing follow-up information after treatment and automatically setting the next appointment. This makes it possible to provide services tailored to the individual needs of each user, thereby reducing waiting times and improving satisfaction.

[0953] "Reservation information" refers to data regarding the date, time, and content of a service provided by a user when they apply for the service in advance.

[0954] A "storage device" is a system of hardware or software that stores digital data and makes it accessible as needed.

[0955] A "reminder" is a means of notifying users of pre-set events to help them remember them.

[0956] "Progress of treatment" refers to information regarding the current stage of progress or completion within the treatment process.

[0957] "Notifications to adjust appointment times" refer to information sent to users to optimize their scheduled appointment times and inform them of appropriate times to arrive at the clinic.

[0958] "Emotional recognition" is the process of identifying a user's emotional state and collecting and analyzing the information necessary to respond appropriately to that emotion.

[0959] "Information for reducing stress" refers to data on advice and relaxation content provided with the aim of reducing the psychological burden on users.

[0960] "Follow-up information" refers to additional announcements after the service has been provided, as well as information regarding future activity plans.

[0961] "Next reservation" refers to reservation information prepared in advance for a user's next visit.

[0962] This invention is a system for improving the quality of services provided to users in nursing care facilities or home care settings. Several key components are necessary to realize this system.

[0963] First, the server receives user reservation information and stores it in storage. A database management system is used for this, such as relational databases like MySQL or PostgreSQL. The server also builds the backend using programming languages ​​like Go or Python, allowing users to easily access the reservation information.

[0964] Next, the server sends a reminder notification to the user based on the reservation information. This is achieved using push notification technology (such as Google Firebase Cloud Messaging). The reminder is then sent to the user's device, such as a smartphone or tablet.

[0965] Furthermore, if a consultation is in progress, the server tracks the progress of the consultation and sends notifications in real time to adjust the user's appointment time. This incorporates a tracking and notification system that works with the frontend by leveraging JavaScript frameworks in addition to Go and Python.

[0966] Furthermore, for emotion recognition, the user's device acquires facial expressions and voice data and sends this data to the server. The server analyzes the emotional state using tools such as TensorFlow and OpenCV. Based on the results, it provides information and care plans to reduce stress.

[0967] As a concrete example, when a user books a care service, the server records it and sends a reminder the day before the scheduled appointment. On the day of the appointment, if the emotion engine detects that the user is feeling anxious, a notification is sent to the user's device suggesting that relaxation music be played. Once the service is completed, the next appointment is automatically scheduled and the user is notified.

[0968] Here's an example of a prompt using a generative AI model to provide flexible guidance based on emotional state: "Please input smile expression data, analyze the user's emotional state, and suggest relaxation content."

[0969] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0970] Step 1:

[0971] The server receives reservation information from users and stores it in a database. Inputs include the user's reservation date and time, location, and service details, and this data is structured and stored by the database management system. The output is a confirmation message indicating that the reservation information has been accurately recorded.

[0972] Step 2:

[0973] The server sends a reminder to the user the day before the reservation date. The inputs are reservation information retrieved from the database and the user's contact information. The data processing involves generating a reminder message and sending it to the user's device via a push notification system. The output is the notification message received by the user.

[0974] Step 3:

[0975] On the day of the appointment, the server tracks the progress of the consultation in real time and sends notifications to the user to adjust their appointment time. The input is progress data obtained from the consultation room, and the server uses this to calculate the optimal appointment time. The output is a notification message that includes the adjusted appointment time.

[0976] Step 4:

[0977] The user's device collects facial and voice data for emotion recognition and sends it to the server. The input is real-time data from the user's camera and microphone, which is analyzed using data processing tools such as TensorFlow and OpenCV. The output is the analysis result indicating the user's emotional state.

[0978] Step 5:

[0979] The server generates and provides information to reduce stress based on the user's emotional state. The input is the result of emotion recognition analysis, which the AI ​​model uses to select relaxation content and care plans. The output is a suggestion notification displayed on the user's device.

[0980] Step 6:

[0981] After the consultation, the server generates follow-up information and automatically schedules the next appointment. Inputs include the consultation record and the user's preferred date and time for the next appointment. Data processing generates follow-up information and determines the next appointment date and time. Output is a confirmation message for the next appointment sent to the user.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] 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.

[0990] 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."

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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 as being incorporated by reference.

[1003] The following is further disclosed regarding the embodiments described above.

[1004] (Claim 1)

[1005] A means of storing reservation information from users in a storage device,

[1006] A means of sending reminders to users based on reservation information,

[1007] A means of obtaining the progress of medical treatment and sending notifications to adjust the appointment time,

[1008] A means of interviewing users about their symptoms and determining the urgency of the situation,

[1009] A means of instructing users to receive home care or visit a clinic based on their symptoms,

[1010] A means of predicting waiting times and notifying users,

[1011] A method for providing follow-up information after a consultation and automatically scheduling the next appointment,

[1012] A system that includes this.

[1013] (Claim 2)

[1014] The system according to claim 1, wherein the urgency determination means analyzes the user's symptoms using an AI algorithm.

[1015] (Claim 3)

[1016] The system according to claim 1, wherein the waiting time prediction means notifies the user of the waiting time in real time based on progress data from the examination room.

[1017] "Example 1"

[1018] (Claim 1)

[1019] A means of storing reservation information from usage methods in a recording function,

[1020] A means of sending notifications to the user based on reservation information,

[1021] A means of obtaining information on the progress of medical treatment and adjusting visit information to improve convenience,

[1022] A means of analyzing the symptoms of the means of use and evaluating the urgency,

[1023] A means of guiding users to appropriate methods based on their symptoms,

[1024] A means of calculating the waiting time and notifying the user,

[1025] A method to provide information after the consultation and automatically schedule the next appointment,

[1026] Information analysis and generation using an AI model for optimizing information utilization methods,

[1027] A system that includes this.

[1028] (Claim 2)

[1029] The system according to claim 1, wherein the urgency assessment means analyzes the symptoms of the usage means using AI calculations.

[1030] (Claim 3)

[1031] The system according to claim 1, wherein the waiting time calculation means notifies the user of the waiting time in accordance with the dynamics based on progress information from the examination room.

[1032] "Application Example 1"

[1033] (Claim 1)

[1034] A means of storing reservation information from users in a storage device,

[1035] A means of sending reminders to users based on reservation information,

[1036] A means of obtaining the progress of medical treatment and sending notifications to adjust the appointment time,

[1037] A means of interviewing users about their symptoms and determining the urgency of the situation,

[1038] A means of instructing users to receive home care or visit a clinic based on their symptoms,

[1039] A means of predicting waiting times and notifying users,

[1040] A method for providing follow-up information after a consultation and automatically scheduling the next appointment,

[1041] A method for recommending the most suitable destination to users, taking into account traffic conditions,

[1042] A system that includes this.

[1043] (Claim 2)

[1044] The system according to claim 1, wherein the urgency determination means analyzes the user's symptoms using an AI algorithm.

[1045] (Claim 3)

[1046] The system according to claim 1, wherein the waiting time prediction means notifies the user of the waiting time in real time based on progress data from the examination room.

[1047] "Example 2 of combining an emotion engine"

[1048] (Claim 1)

[1049] A means of storing reservation information from users in a storage device,

[1050] A means of sending reminders to users based on reservation information,

[1051] A means of obtaining the progress of treatment from healthcare professionals and sending notifications to adjust the appointment time,

[1052] A method that uses a generative model to interview users about their symptoms and determine the urgency of the situation,

[1053] A means of instructing users to receive home care or visit a clinic based on their symptoms,

[1054] A means of identifying the emotional state of users and providing appropriate content,

[1055] A means of predicting waiting time and sending notifications to suggest comfortable waiting methods when long waiting times are expected,

[1056] A method for providing follow-up information after a consultation and automatically scheduling the next appointment,

[1057] A system that includes this.

[1058] (Claim 2)

[1059] The system according to claim 1, wherein the urgency determination means analyzes the user's symptoms using a generative model.

[1060] (Claim 3)

[1061] The system according to claim 1, comprising means for identifying the emotional state of a user and providing appropriate content.

[1062] "Application example 2 when combining with an emotional engine"

[1063] (Claim 1)

[1064] A means of storing reservation information from users in a storage device,

[1065] A means of sending reminders to users based on reservation information,

[1066] A means of obtaining the progress of medical treatment and sending notifications to adjust the appointment time,

[1067] A means of interviewing users about their symptoms and determining the urgency of the situation,

[1068] A means of instructing users to receive home care or visit a clinic based on their symptoms,

[1069] A means of predicting waiting times and notifying users,

[1070] A means of recognizing the user's emotions and providing information to reduce stress,

[1071] A method for providing follow-up information after a consultation and automatically scheduling the next appointment,

[1072] A system that includes this.

[1073] (Claim 2)

[1074] The system according to claim 1, wherein the urgency determination means analyzes the user's symptoms using an AI algorithm.

[1075] (Claim 3)

[1076] The system according to claim 1, wherein the waiting time prediction means notifies the user of the waiting time in real time based on progress data from the examination room. [Explanation of Symbols]

[1077] 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 means of storing reservation information from users in a storage device, A means of sending reminders to users based on reservation information, A means of obtaining the progress of medical treatment and sending notifications to adjust the appointment time, A means of interviewing users about their symptoms and determining the urgency of the situation, A means of instructing users to receive home care or visit a clinic based on their symptoms, A means of predicting waiting times and notifying users, A method for providing follow-up information after a consultation and automatically scheduling the next appointment, A method for recommending the most suitable destination to users, taking into account traffic conditions, A system that includes this.

2. The system according to claim 1, wherein the urgency determination means analyzes the user's symptoms using an AI algorithm.

3. The system according to claim 1, wherein the waiting time prediction means notifies the user of the waiting time in real time based on progress data from the examination room.

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