system

Abstract

We provide the system. [Solution] A means of collecting patient information from a database, A data analysis method using generative AI to analyze medical history based on collected patient information, A means for automatically generating a patient's medical treatment schedule based on the analysis results, A means of sending notifications to healthcare workers' terminals according to an automatically generated schedule, A means of evaluating and proposing the need for follow-up, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a 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】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 The management of a large number of patients in a medical institution is a great burden on doctors and nurses. In particular, the adjustment of the medical schedule and the improvement of the efficiency of medication guidance are required. If these tasks cannot be properly performed due to busyness, there is a possibility of a decline in the quality of patient care and a delay in treatment. The purpose of this invention is to solve the above problems, reduce the burden on medical staff, and improve the quality of care for patients. 【Means for Solving the Problems】 【0005】 This invention solves the above problems by providing a system that collects patient information from a database and analyzes that information using AI generation. Based on the analysis results, the system has means for automatically generating a patient's medical treatment schedule and sending notifications to the terminals of healthcare professionals. Furthermore, the system can evaluate the need for follow-up and make suggestions at the appropriate time. This makes it easier for healthcare professionals to make timely decisions in medical treatment and to provide high-quality care to patients. 【0006】 "Patient information" refers to data held by a medical institution, such as a patient's medical history, medication data, and schedule information. 【0007】 A "database" is a system used in healthcare institutions to systematically collect, store, and manage patient information. 【0008】 "Generative AI" is artificial intelligence that uses machine learning algorithms to analyze data and automatically generate information tailored to specific purposes. 【0009】 "Data analysis methods" refer to the process by which a generated AI analyzes collected patient information to derive meaningful results from medical history and symptoms. 【0010】 A "medical schedule" is a plan that outlines the appropriate timing for a patient's medical treatment and medication. 【0011】 "Notification method" refers to a system function that automatically sends reminders to healthcare professionals based on the medical treatment schedule. 【0012】 "Follow-up" refers to the continuous observation or reassessment of a patient's condition and the provision of additional medical treatment or guidance as needed. 【0013】 A "terminal" refers to a computer or mobile device used by healthcare professionals to receive notifications from the patient management system. [Brief explanation of the drawing] 【0014】 [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 an 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 an emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a labeled 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. 【0020】 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). 【0021】 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." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 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. 【0025】 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). 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 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". 【0035】 This invention is a system for streamlining patient management in medical institutions, providing integrated functions for patient information collection, analysis, schedule generation, notification, and follow-up suggestions. The details and operation of each component are described below. 【0036】 Data collection and storage 【0037】 First, the server connects to the healthcare institution's database and collects patient medical history, medication data, and schedule information. The collected information is encrypted and stored in a secure database. 【0038】 Data Analysis 【0039】 Next, the server uses a generation AI to analyze the patient's past medical history and medication data to predict the appropriate timing for medical treatment and medication. This makes it possible to create an optimal care plan for each patient. 【0040】 Schedule generation 【0041】 The server automatically generates a patient's medical schedule based on the analysis results. This includes the next appointment date and the timing of necessary follow-up appointments. 【0042】 Notification function 【0043】 Based on the generated schedule, the server automatically sends reminders to healthcare workers' devices. Notifications are delivered as pop-ups on the device screen or via email. 【0044】 Follow-up proposal 【0045】 The server continuously analyzes the patient's condition and assesses the need for follow-up. If a worsening of symptoms is predicted or if changes in medication instructions are necessary, it provides the user with specific follow-up suggestions. 【0046】 Specific example 【0047】 When new medical data for patient A is recorded, the server immediately retrieves the data and analyzes it using a generating AI. Based on this analysis, it is determined that the optimal time for the next consultation is two weeks later. The server then creates a schedule based on this determination and sends it as a reminder to the doctor's terminal. If an abnormality is detected in patient A's medication history, the user receives a notification suggesting a follow-up in three days. 【0048】 Thus, the system of the present invention provides a practical and effective means to streamline the work of healthcare professionals and improve patient care. 【0049】 The following describes the processing flow. 【0050】 Step 1: 【0051】 The server accesses the healthcare institution's database regularly every day to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored in its own database. 【0052】 Step 2: 【0053】 The server first performs data preprocessing to analyze the collected data. This involves detecting and correcting outliers, imputing missing values, and converting the data into a format suitable for analysis. 【0054】 Step 3: 【0055】 The server supplies pre-processed data to the generating AI, which uses machine learning algorithms to analyze each patient's medical history and medication patterns. This analysis predicts the optimal medical schedule and follow-up timing for each patient. 【0056】 Step 4: 【0057】 The server automatically generates a treatment schedule for each patient based on the analysis results. This schedule includes appointment reminders and medication timings. 【0058】 Step 5: 【0059】 The server sends notifications to healthcare workers' devices based on the generated schedule. These notifications are delivered promptly to the devices via pop-ups or email, prompting them to acknowledge them. 【0060】 Step 6: 【0061】 The server continuously monitors and analyzes patient status data. If changes in the situation or signs of worsening symptoms are detected, it assesses the need for follow-up and makes appropriate recommendations. 【0062】 Step 7: 【0063】 Users receive notifications and suggestions displayed on their devices and take specific actions such as medical treatment or follow-up. They also provide feedback to the system as needed, contributing to its improvement. 【0064】 (Example 1) 【0065】 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." 【0066】 Patient management in healthcare institutions requires the collection and analysis of vast amounts of information, posing challenges such as the time and effort required to provide effective care. Furthermore, rapid and accurate information processing is essential for creating appropriate treatment schedules and determining the need for follow-up. 【0067】 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. 【0068】 In this invention, the server includes means for securely collecting and storing individual data from medical institutions, information processing means using generative AI to analyze history based on the collected individual data, and means for automatically generating schedules according to the information analysis results. This streamlines the work of medical professionals and enables the provision of accurate and prompt care to patients. 【0069】 A "medical institution" is an organization that provides medical services to maintain and improve people's health. 【0070】 "Individualized data" refers to information related to a specific individual, including medical records and treatment history. 【0071】 "Generative AI" is an artificial intelligence technology that generates new information based on input data and supports analysis and decision-making. 【0072】 "Information processing means" refers to methods and technologies for receiving data, processing and analyzing it, and obtaining useful information. 【0073】 A "notification function" is a function used to transmit specific information to a target audience, and generally includes push notifications, emails, and alerts. 【0074】 "Communication equipment" refers to devices used to send and receive data and information from remote locations. 【0075】 "Follow-up" is the process of continuously monitoring a patient's health status and providing additional medical care or guidance as needed. 【0076】 This invention is a system designed to improve the efficiency of patient management operations in medical institutions. The server first securely connects to the medical institution's database and collects individual patient data such as medical history, medication data, and schedule information. The collected data is securely stored using encryption technologies such as AES-256. 【0077】 Next, the server uses a generative AI model to analyze the collected patient data. This analysis employs natural language processing (NLP) and machine learning algorithms to predict the optimal timing for medical treatment and medication. Based on these results, the server automatically generates a personalized treatment schedule and builds a continuous care plan. 【0078】 Once a schedule is generated, the server notifies healthcare professionals of its contents on their terminals. The terminals display the received information as a pop-up on the screen and also send notifications via email. This ensures that healthcare professionals receive information quickly, enabling them to provide appropriate medical care. 【0079】 Furthermore, the server continuously analyzes the patient's condition and determines the need for follow-up. For example, if a worsening of symptoms is predicted, it will suggest specific follow-up options to the user, improving the quality of medical care. An example of a specific prompt message is: "Based on patient A's latest medical data, predict the next appointment date. Also, evaluate whether follow-up is necessary based on their medication history." 【0080】 Thus, the present invention enables efficient collection and analysis of medical information, thereby reducing the burden on healthcare professionals and providing prompt and appropriate care to patients. 【0081】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0082】 Step 1: 【0083】 The server connects to the healthcare institution's database and collects individual data. Inputs include patient medical history, medication data, and schedule information, which are retrieved using a secure communication protocol. As output, this information is encrypted and stored in a secure database. 【0084】 Step 2: 【0085】 The server inputs the collected data into a generating AI model for analysis. Specifically, it uses natural language processing and machine learning algorithms to predict the optimal timing for medical treatment and medication. This process outputs analysis results tailored to each individual patient. 【0086】 Step 3: 【0087】 The server generates a patient-specific treatment schedule based on the analysis results of the generated AI. It takes the analysis results as input and automatically calculates the next appointment date and necessary follow-up appointments. This allows it to output the optimal treatment schedule. 【0088】 Step 4: 【0089】 The server notifies healthcare workers' terminals of the generated schedule. Specifically, it sends reminders to terminals via a notification system. The input is the generated medical schedule, and the output is a pop-up notification on the terminal or an email. 【0090】 Step 5: 【0091】 The server continuously monitors the patient's condition and assesses the need for follow-up. Input data includes the patient's latest medical results and medication information. A generating AI determines the need for follow-up and outputs the results as a suggestion to the user. This step improves the quality of medical care. 【0092】 (Application Example 1) 【0093】 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." 【0094】 In healthcare settings, patient management is inefficient due to the time and effort required. This is particularly challenging in an aging society where the demand for home care is increasing, making it difficult to adequately manage patients' health in their home environment. Furthermore, there is a lack of means for patients and caregivers to understand treatment schedules and follow-up needs in real time. Therefore, there is a need for an efficient and effective patient management system to address these issues. 【0095】 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. 【0096】 In this invention, the server includes means for collecting patient information from a database, means for data analysis using generative AI to analyze medical history based on the collected patient information, and means for automatically generating a patient's medical schedule based on the analysis results. This enables efficient management of the patient's health status even in a home environment, and allows for the proposal of appropriate medical schedules and follow-up in cooperation with medical institutions. 【0097】 "Patient information" refers to detailed data held by a medical institution, such as a patient's medical history, medication data, and health status. 【0098】 A "database" is an information management system that organizes and stores large amounts of data, such as patient information and medical history, and allows for quick searching and retrieval when needed. 【0099】 "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict and propose desired results. 【0100】 "Data analysis methods" refer to the processes and technologies used to analyze collected patient information using generated AI to evaluate treatment schedules and the need for follow-up. 【0101】 "Automatic generation methods" refer to a system that uses the results of data analysis to automatically construct a patient's medical treatment schedule. 【0102】 "Means of sending notifications" refers to the function of sending generated schedules and important information to communication devices in real time. 【0103】 "Follow-up" refers to the activity of continuously observing and evaluating a patient's health status and proposing additional medical measures as needed. 【0104】 A "sensing device" is a device or sensor used to monitor a patient's health status in real time and to link data. 【0105】 "Means of presentation using visual and auditory means" refer to methods of conveying information to users visually and audibly through displays and speakers. 【0106】 The system for implementing this invention is a system that collects patient information from a database and analyzes it using a generated AI model, in order to streamline patient management in medical institutions and homes. 【0107】 The server connects to the healthcare institution's database and securely retrieves patient medical history and medication data. This data is encrypted and stored in a secure database. The software used by the server is a database management system and a generative AI model equipped with machine learning algorithms. 【0108】 The server inputs collected patient information into a generating AI model and performs data analysis to predict the appropriate timing for medical treatment and medication. Based on the analysis results, the server automatically generates the patient's medical schedule and transmits this information to a communication device. Communication devices include tablets, smartphones, or care robots used in the home. Notifications are made in real time and presented to the user visually and audibly. 【0109】 The server further continuously monitors the patient's health status using sensing devices and shares information with medical institutions as needed. This function allows for the assessment of the need for individualized follow-up for each patient and provides users with specific action suggestions. For example, based on the results of daily temperature measurements, the AI ​​might suggest, "Drink some water." 【0110】 Examples of prompts include, "Based on the patient's latest health data, please suggest the optimal appointment date," and "Considering the patient's recent medical history, please suggest follow-up actions until the next appointment." The generative AI model performs appropriate analysis of these prompts and presents the optimal medical plan. 【0111】 Thus, the system aims to support patients' health management in a practical and effective way. 【0112】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0113】 Step 1: 【0114】 The server connects to the healthcare institution's database and retrieves patient information, such as medical history and medication data. Input is a query to the database, and output is patient information encrypted in a secure format. This information is stored in a secure database for subsequent analysis processes. 【0115】 Step 2: 【0116】 The server inputs acquired patient information into a generating AI model for data analysis. The input consists of the patient's medical history and medication data, which the generating AI model analyzes to predict the appropriate timing for medical treatment and medication. The algorithm used in this process is machine learning, which learns data patterns to make appropriate predictions. 【0117】 Step 3: 【0118】 The server automatically generates a patient's medical schedule based on the analysis results obtained from the generated AI model. The input is the analysis results, and the output is a specific medical schedule. This schedule includes the next appointment date and the timing of necessary follow-up appointments. The server achieves this using schedule management software. 【0119】 Step 4: 【0120】 The server notifies the communication device of the generated schedule. The input is the automatically generated medical schedule, and the output is a reminder-style notification displayed on the terminal. The terminal presents the schedule to the user visually or audibly, allowing the end user to check it in real time. 【0121】 Step 5: 【0122】 The server continuously monitors the patient's health status through sensing devices and shares information with healthcare institutions as needed. Input is the latest health data transmitted from the sensing devices, and output is the latest health status analysis and necessary follow-up suggestions. A generating AI model uses this data to determine the need for follow-up and recommends specific actions to the user. 【0123】 As a concrete example, if the server receives elevated body temperature data from a robot's sensor one morning, the generating AI model analyzes this data and, if it determines that early medical attention is necessary, it notifies the user's terminal accordingly. It also simultaneously outputs a voice prompt to the user, such as "Drink some water." 【0124】 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. 【0125】 This invention incorporates an emotion engine that analyzes user emotions into a system designed to streamline patient management in medical institutions. In addition to collecting patient information, analyzing it using AI, automatically generating treatment schedules, sending notifications, and suggesting follow-up care, this system aims to reduce the burden on healthcare professionals and improve the quality of patient care by performing user emotion evaluations using the emotion engine. 【0126】 Data collection and storage 【0127】 First, the server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored securely. 【0128】 Data Analysis 【0129】 The server uses generative AI to analyze the collected data and determine the optimal treatment schedule for each patient. The AI ​​uses machine learning algorithms to predict effective treatment intervals and follow-up timings based on past data. 【0130】 Application of the emotion engine 【0131】 The server uses an emotion engine to analyze text data, such as comments entered by the user in natural language, and determine their emotional state. The emotion engine utilizes natural language processing technology to identify the user's stress level and emotional response. 【0132】 Schedule and notification generation 【0133】 Next, the server combines the analysis results and sentiment assessment to automatically generate the most effective consultation schedule and reminders. The generated notifications are adjusted based on the user's emotional state and sent to the healthcare professional's terminal. 【0134】 Receiving notifications and taking action 【0135】 The device presents the user with generated reminders and follow-up suggestions. The notification content is adjusted according to the user's emotions, ensuring that information is delivered in the appropriate tone and at the right time. 【0136】 Follow-up proposal 【0137】 Based on the analysis, the server suggests more frequent follow-ups if the patient's symptoms may be worsening or their emotional state is unstable. This allows healthcare professionals to better understand the patient's overall health status. 【0138】 Specific example 【0139】 For example, if patient B logs into the medical system and enters a comment about their condition, the server's emotion engine analyzes the text. If the analysis determines that patient B is experiencing stress, the server takes this information into consideration and sets an earlier-than-usual appointment reminder. This earlier reminder is notified to the doctor's terminal, and follow-up care, including emotional support for patient B, is suggested. 【0140】 This system allows healthcare professionals to visualize patient-centered care, further improving the quality of patient care. 【0141】 The following describes the processing flow. 【0142】 Step 1: 【0143】 The server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted and securely stored in the database. 【0144】 Step 2: 【0145】 The server processes the collected data using a generating AI for analysis. Here, the AI ​​uses machine learning algorithms based on past treatment patterns to optimize the treatment schedule. 【0146】 Step 3: 【0147】 The server automatically generates a medical schedule to determine the optimal next appointment date and follow-up period for each patient, based on the data analyzed by the generating AI. 【0148】 Step 4: 【0149】 The server sends user-entered comments and feedback to the emotion engine, which uses natural language processing technology to determine the emotional state. The emotion engine identifies stress levels and emotional changes. 【0150】 Step 5: 【0151】 The server integrates the analysis results from the emotion engine with the medical schedule and adjusts the notification content to match the user's emotional state. This creates an emotionally sensitive reminder. 【0152】 Step 6: 【0153】 The server sends coordinated reminders in real time to the devices used by healthcare professionals. Notifications appear on the devices as pop-ups or emails. 【0154】 Step 7: 【0155】 Users review emotionally sensitive reminders and follow-up suggestions displayed on their devices and take appropriate action. User feedback helps to further improve the system. 【0156】 (Example 2) 【0157】 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 will be referred to as the "terminal." 【0158】 Patient management in healthcare settings involves many time-consuming tasks, such as managing medical history, coordinating schedules, and planning follow-up appointments. Furthermore, appropriately understanding and reflecting patients' emotional states in their treatment is a significant burden for healthcare professionals. To address these challenges, there is a need for methods to automate patient emotional management and the creation of efficient treatment schedules. 【0159】 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. 【0160】 In this invention, the server includes means for collecting patient information from a storage medium, data analysis means using generative AI to analyze medical history based on the collected patient information, and means for evaluating comments entered by the user in natural language and determining the emotional state. This enables more efficient patient management and the provision of high-quality medical care that takes emotional states into consideration. 【0161】 "Patient information" refers to all data related to a patient, such as medical history, medication data, and schedule information. 【0162】 "Storage medium" refers to means of storing information, including databases and other data storage systems. 【0163】 "Generative AI" refers to artificial intelligence technologies used to analyze collected data and predict or generate specific results. 【0164】 "Data analysis methods" refer to the methods and techniques used to process and analyze collected data to derive insights and conclusions. 【0165】 "Clinic schedule" refers to the patient's appointment times and follow-up schedule. 【0166】 "Automatic generation" refers to the creation of information and schedules by artificial intelligence or algorithms without manual intervention. 【0167】 "Natural language" refers to the words and sentences that humans use in everyday life, and also to the technology that allows computers to understand or process them. 【0168】 "Emotional state" refers to the psychological or emotional reactions or conditions exhibited by the user, and this means determining these states through text analysis or other methods. 【0169】 "Notifications" refer to messages or alerts used to convey specific information to users or healthcare professionals. 【0170】 "Providing in real time" means that some kind of information or result is processed immediately and provided in an available state. 【0171】 The embodiments for carrying out the present invention will now be described. The present invention is a system for streamlining patient management in medical institutions, and aims to reduce the burden on healthcare professionals and improve the quality of patient care by comprehensively considering even the emotional state of patients. 【0172】 This system first has a server access the healthcare institution's database to collect patient information. This patient information includes medical history, medication data, and schedule information. The data is encrypted using technologies such as SSL / TLS and stored securely. 【0173】 The server then analyzes the collected patient information using a generative AI model. This model utilizes machine learning libraries such as Scikit-learn to predict treatment schedules and follow-up timings based on historical data. 【0174】 Furthermore, the server utilizes a natural language processing-based emotion engine to analyze natural language comments collected from users. The emotion engine uses a framework such as TENSORFLOW® to classify emotional states and determine the patient's stress level. 【0175】 Based on the analysis results, the server generates a medical schedule and notifications. These generated schedules and notifications are then adjusted using a Python script according to the patient's emotional state. The adjusted notifications are then sent to the healthcare professional's terminal. 【0176】 For example, if a patient enters a comment into the system saying, "I haven't been able to sleep well lately and I'm feeling a bit tired," the server's emotion engine analyzes the text and determines that the patient is experiencing stress. Based on this, an earlier-than-usual appointment reminder is set and notified to healthcare professionals. 【0177】 An example of a prompt to be input into the generating AI model is, "Based on past medical history, please suggest the optimal date for your next appointment." 【0178】 In this way, the system can provide medical management that takes the patient's emotional state into consideration. 【0179】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0180】 Step 1: 【0181】 The server accesses the healthcare institution's database to collect patient information such as medical history, medication data, and schedule information. Input requires a database query, which sends encrypted data to the server. Specifically, the server periodically accesses the database to retrieve the latest patient information. 【0182】 Step 2: 【0183】 The server passes the collected patient information to a generating AI model for data analysis. The patient's medical history is sent to the AI ​​model as input. Using the Scikit-learn library, the AI ​​predicts the medical schedule and outputs the optimal interval between appointments. Specifically, the AI ​​applies machine learning algorithms based on past data. 【0184】 Step 3: 【0185】 The server passes comments entered by users in natural language to an emotion engine for analysis. Text data is required as input. TensorFlow is used to determine the emotional state, and emotional information such as stress levels is obtained as output. Specifically, the user's comments are analyzed, and their emotional response is quantified. 【0186】 Step 4: 【0187】 The server generates treatment schedules and reminders by combining data analysis results and emotion assessments. The input requires AI analysis results and emotion assessments. A Python script automatically creates reminders based on this information. Specifically, the generated schedule is adjusted to reflect the patient's emotional state. 【0188】 Step 5: 【0189】 The server sends the generated reminders and follow-up suggestions to the healthcare professional's device. The input requires coordinated reminder information. The output is that the device receives the notification and provides the information to the user. Specifically, the server delivers reminders via Secure Email or a dedicated app. 【0190】 (Application Example 2) 【0191】 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". 【0192】 Providing appropriate care based on residents' emotions and health conditions in nursing care facilities is a significant burden for care staff. There is a need to alleviate this burden while improving the quality of care for residents. Traditional systems have had the problem of making it difficult to provide individualized care that is sensitive to residents' emotions, often resulting in uniform care being provided. 【0193】 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. 【0194】 In this invention, the server includes means for collecting user information from an information management system, means for analyzing information using a generative AI that analyzes health records based on the collected user information, and means including an emotion analysis engine that analyzes the user's emotional state. This makes it possible to propose appropriate care according to the user's individual emotional state and health condition. 【0195】 "User information" refers to data about residents of care facilities, including health records and emotional status. 【0196】 An "information management system" refers to a system that centrally manages user information and efficiently collects and provides it. 【0197】 "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze data and generate optimal care schedules. 【0198】 "Information analysis methods" refer to the process of analyzing health records and care requirements based on collected user information. 【0199】 An "emotion analysis engine" refers to a system that uses natural language processing technology to evaluate a user's emotional state and determine the need for care. 【0200】 "Notification methods" refer to means of informing care staff of automatically generated schedules and care suggestions. 【0201】 "Care proposal" refers to the process of suggesting appropriate responses and follow-ups based on the user's health and emotional state. 【0202】 The system implementing this invention is operated primarily using a server and terminals. The server collects user information from an information management system and acquires health records and emotional states. Next, it uses a generative AI model to analyze this data and automatically generate a care schedule tailored to each user's health state and emotional state. 【0203】 The server also uses an emotion analysis engine to analyze natural language data entered by the user and evaluate the user's emotional state. This involves using natural language processing techniques, such as combining space keys and morphological analysis libraries. 【0204】 The generated schedules and care suggestions are sent to the terminal in real time. The terminal notifies care staff of necessary care information. This notification method includes alerts and pop-up messages to enable care staff to respond quickly. 【0205】 For example, if a user enters a comment into the app saying, "I've been having trouble sleeping lately and I'm feeling anxious," the server analyzes the text, and the emotion analysis engine detects the user's stress level. Based on the results, the server notifies the user's device with a "suggestion for relaxation time." 【0206】 An example of a prompt message is, "Please enter a comment from the resident. We will analyze their emotions and provide appropriate care suggestions." This allows the system to efficiently analyze user input and propose the optimal care plan to the staff. 【0207】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0208】 Step 1: 【0209】 The server collects user information from the information management system. The input consists of health records and emotional state-related data stored in the facility's database. This data is extracted using a secure protocol, encrypted, and sent to the server's analysis system. 【0210】 Step 2: 【0211】 The server analyzes user information collected using a generative AI model. The input is encrypted data obtained in step 1, which is decrypted, and machine learning algorithms are used to analyze the user's past health status and emotional patterns. Based on this analysis, a personalized care schedule is automatically generated. 【0212】 Step 3: 【0213】 The server receives natural language comments entered by the user on their device. User input is direct natural language data, which is sent to the sentiment analysis engine. The server uses natural language processing to analyze the user's emotional state from the text data and determine their stress level and emotional tendencies. 【0214】 Step 4: 【0215】 The server constructs appropriate care suggestions based on the generated care schedule and emotion analysis results. The input is the results from steps 2 and 3, and this information is combined to determine the care plan and the need for follow-up. The server then prepares to send the suggestions to the care staff. 【0216】 Step 5: 【0217】 The terminal receives care suggestions sent from the server. Input is notification data from the server. The terminal provides real-time information to care staff using pop-up alerts and message notifications. This is a crucial step in facilitating prompt responses tailored to the care situation. 【0218】 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. 【0219】 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. 【0220】 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. 【0221】 [Second Embodiment] 【0222】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0223】 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. 【0224】 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). 【0225】 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. 【0226】 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. 【0227】 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). 【0228】 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. 【0229】 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. 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 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". 【0234】 This invention is a system for streamlining patient management in medical institutions, providing integrated functions for patient information collection, analysis, schedule generation, notification, and follow-up suggestions. The details and operation of each component are described below. 【0235】 Data collection and storage 【0236】 First, the server connects to the healthcare institution's database and collects patient medical history, medication data, and schedule information. The collected information is encrypted and stored in a secure database. 【0237】 Data Analysis 【0238】 Next, the server uses a generation AI to analyze the patient's past medical history and medication data to predict the appropriate timing for medical treatment and medication. This makes it possible to create an optimal care plan for each patient. 【0239】 Schedule generation 【0240】 The server automatically generates a patient's medical schedule based on the analysis results. This includes the next appointment date and the timing of necessary follow-up appointments. 【0241】 Notification function 【0242】 Based on the generated schedule, the server automatically sends reminders to healthcare workers' devices. Notifications are delivered as pop-ups on the device screen or via email. 【0243】 Follow-up proposal 【0244】 The server continuously analyzes the patient's condition and assesses the need for follow-up. If a worsening of symptoms is predicted or if changes in medication instructions are necessary, it provides the user with specific follow-up suggestions. 【0245】 Specific example 【0246】 When new medical data for patient A is recorded, the server immediately retrieves the data and analyzes it using a generating AI. Based on this analysis, it is determined that the optimal time for the next consultation is two weeks later. The server then creates a schedule based on this determination and sends it as a reminder to the doctor's terminal. If an abnormality is detected in patient A's medication history, the user receives a notification suggesting a follow-up in three days. 【0247】 Thus, the system of the present invention provides a practical and effective means to streamline the work of healthcare professionals and improve patient care. 【0248】 The following describes the processing flow. 【0249】 Step 1: 【0250】 The server accesses the healthcare institution's database regularly every day to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored in its own database. 【0251】 Step 2: 【0252】 The server first performs data preprocessing to analyze the collected data. This involves detecting and correcting outliers, imputing missing values, and converting the data into a format suitable for analysis. 【0253】 Step 3: 【0254】 The server supplies pre-processed data to the generating AI, which uses machine learning algorithms to analyze each patient's medical history and medication patterns. This analysis predicts the optimal medical schedule and follow-up timing for each patient. 【0255】 Step 4: 【0256】 The server automatically generates a treatment schedule for each patient based on the analysis results. This schedule includes appointment reminders and medication timings. 【0257】 Step 5: 【0258】 The server sends notifications to healthcare workers' devices based on the generated schedule. These notifications are delivered promptly to the devices via pop-ups or email, prompting them to acknowledge them. 【0259】 Step 6: 【0260】 The server continuously monitors and analyzes patient status data. If changes in the situation or signs of worsening symptoms are detected, it assesses the need for follow-up and makes appropriate recommendations. 【0261】 Step 7: 【0262】 Users receive notifications and suggestions displayed on their devices and take specific actions such as medical treatment or follow-up. They also provide feedback to the system as needed, contributing to its improvement. 【0263】 (Example 1) 【0264】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0265】 Patient management in healthcare institutions requires the collection and analysis of vast amounts of information, posing challenges such as the time and effort required to provide effective care. Furthermore, rapid and accurate information processing is essential for creating appropriate treatment schedules and determining the need for follow-up. 【0266】 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. 【0267】 In this invention, the server includes means for securely collecting and storing individual data from medical institutions, information processing means using generative AI to analyze history based on the collected individual data, and means for automatically generating schedules according to the information analysis results. This streamlines the work of medical professionals and enables the provision of accurate and prompt care to patients. 【0268】 A "medical institution" is an organization that provides medical services to maintain and improve people's health. 【0269】 "Individualized data" refers to information related to a specific individual, including medical records and treatment history. 【0270】 "Generative AI" is an artificial intelligence technology that generates new information based on input data and supports analysis and decision-making. 【0271】 "Information processing means" refers to methods and technologies for receiving data, processing and analyzing it, and obtaining useful information. 【0272】 A "notification function" is a function used to transmit specific information to a target audience, and generally includes push notifications, emails, and alerts. 【0273】 "Communication equipment" refers to devices used to send and receive data and information from remote locations. 【0274】 "Follow-up" is the process of continuously monitoring a patient's health status and providing additional medical care or guidance as needed. 【0275】 This invention is a system designed to improve the efficiency of patient management operations in medical institutions. The server first securely connects to the medical institution's database and collects individual patient data such as medical history, medication data, and schedule information. The collected data is securely stored using encryption technologies such as AES-256. 【0276】 Next, the server uses a generative AI model to analyze the collected patient data. This analysis employs natural language processing (NLP) and machine learning algorithms to predict the optimal timing for medical treatment and medication. Based on these results, the server automatically generates a personalized treatment schedule and builds a continuous care plan. 【0277】 Once a schedule is generated, the server notifies healthcare professionals of its contents on their terminals. The terminals display the received information as a pop-up on the screen and also send notifications via email. This ensures that healthcare professionals receive information quickly, enabling them to provide appropriate medical care. 【0278】 Furthermore, the server continuously analyzes the patient's condition and determines the need for follow-up. For example, if a deterioration of symptoms is predicted, specific follow-up proposals are made to the user to improve the quality of medical care. An example of a specific prompt sentence is "Based on the latest medical data of Patient A, please predict the next medical appointment date. Also, please evaluate whether follow-up is necessary based on the medication history." 【0279】 In this way, the present invention realizes the reduction of the burden on medical staff and the provision of prompt and appropriate care to patients by efficiently collecting and analyzing medical information. 【0280】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0281】 Step 1: 【0282】 The server connects to the database of the medical institution and collects individual data. The inputs are the patient's medical history, medication data, and schedule information, which are obtained using a secure communication protocol. As output, this information is encrypted and stored in a secure database. 【0283】 Step 2: 【0284】 The server inputs the collected data into the generative AI model for analysis. Specifically, natural language processing and machine learning algorithms are used to predict the optimal timing for medical treatment and medication. Through this process, analysis results tailored to individual patients are output. 【0285】 Step 3: 【0286】 Based on the analysis results of the generative AI, the server generates a medical schedule for each patient. Taking the analysis results as input, it automatically calculates the next medical appointment date and the schedule for necessary follow-up from them. Thereby, an optimal medical schedule is output. 【0287】 Step 4: 【0288】 The server notifies healthcare workers' terminals of the generated schedule. Specifically, it sends reminders to terminals via a notification system. The input is the generated medical schedule, and the output is a pop-up notification on the terminal or an email. 【0289】 Step 5: 【0290】 The server continuously monitors the patient's condition and assesses the need for follow-up. Input data includes the patient's latest medical results and medication information. A generating AI determines the need for follow-up and outputs the results as a suggestion to the user. This step improves the quality of medical care. 【0291】 (Application Example 1) 【0292】 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." 【0293】 In healthcare settings, patient management is inefficient due to the time and effort required. This is particularly challenging in an aging society where the demand for home care is increasing, making it difficult to adequately manage patients' health in their home environment. Furthermore, there is a lack of means for patients and caregivers to understand treatment schedules and follow-up needs in real time. Therefore, there is a need for an efficient and effective patient management system to address these issues. 【0294】 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. 【0295】 In this invention, the server includes means for collecting patient information from a database, means for data analysis using generative AI to analyze medical history based on the collected patient information, and means for automatically generating a patient's medical schedule based on the analysis results. This enables efficient management of the patient's health status even in a home environment, and allows for the proposal of appropriate medical schedules and follow-up in cooperation with medical institutions. 【0296】 "Patient information" refers to detailed data held by a medical institution, such as a patient's medical history, medication data, and health status. 【0297】 A "database" is an information management system that organizes and stores large amounts of data, such as patient information and medical history, and allows for quick searching and retrieval when needed. 【0298】 "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict and propose desired results. 【0299】 "Data analysis methods" refer to the processes and technologies used to analyze collected patient information using generated AI to evaluate treatment schedules and the need for follow-up. 【0300】 "Automatic generation methods" refer to a system that uses the results of data analysis to automatically construct a patient's medical treatment schedule. 【0301】 "Means of sending notifications" refers to the function of sending generated schedules and important information to communication devices in real time. 【0302】 "Follow-up" refers to the activity of continuously observing and evaluating a patient's health status and proposing additional medical measures as needed. 【0303】 A "sensing device" is a device or sensor used to monitor a patient's health status in real time and to link data. 【0304】 "The means of presenting visually or audibly" refers to a method for transmitting information to the user visually and audibly by means of a display or a speaker. 【0305】 The system for implementing this invention is a system that collects patient information from a database and performs analysis using a generated AI model in order to streamline patient management in medical institutions and homes. 【0306】 The server connects to the database of the medical institution and securely obtains the patient's medical history and medication data. These data are encrypted and stored in a secure database. The software used by the server is a generated AI model equipped with a database management system and machine learning algorithms. 【0307】 The server inputs the collected patient information into the generated AI model and performs data analysis to predict the appropriate timing for medical treatment and medication. Based on the analysis results, the server automatically generates the patient's medical schedule and transmits the information to the communication device. The communication device is a tablet, smartphone, or care robot used within the home. The notification is made in real time and presented to the user visually or audibly. 【0308】 The server further continuously monitors the patient's health status using a sensing device and shares information with the medical institution as needed. This function evaluates the need for individual patient follow-up and provides specific action proposals to the user. As a specific example, based on the morning body temperature measurement results, the generated AI may propose "drink more water". 【0309】 Examples of prompt sentences include "Please propose the optimal medical treatment date based on the patient's latest health data." and "Please propose follow-up actions until the next medical treatment considering the recent medical history." The generated AI model performs appropriate analysis on these prompts and presents an optimal medical plan. 【0310】 Thus, the system aims to support patients' health management in a practical and effective way. 【0311】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0312】 Step 1: 【0313】 The server connects to the healthcare institution's database and retrieves patient information, such as medical history and medication data. Input is a query to the database, and output is patient information encrypted in a secure format. This information is stored in a secure database for subsequent analysis processes. 【0314】 Step 2: 【0315】 The server inputs acquired patient information into a generating AI model for data analysis. The input consists of the patient's medical history and medication data, which the generating AI model analyzes to predict the appropriate timing for medical treatment and medication. The algorithm used in this process is machine learning, which learns data patterns to make appropriate predictions. 【0316】 Step 3: 【0317】 The server automatically generates a patient's medical schedule based on the analysis results obtained from the generated AI model. The input is the analysis results, and the output is a specific medical schedule. This schedule includes the next appointment date and the timing of necessary follow-up appointments. The server achieves this using schedule management software. 【0318】 Step 4: 【0319】 The server notifies the communication device of the generated schedule. The input is the automatically generated medical schedule, and the output is a reminder-style notification displayed on the terminal. The terminal presents the schedule to the user visually or audibly, allowing the end user to check it in real time. 【0320】 Step 5: 【0321】 The server continuously monitors the patient's health status through sensing devices and shares information with healthcare institutions as needed. Input is the latest health data transmitted from the sensing devices, and output is the latest health status analysis and necessary follow-up suggestions. A generating AI model uses this data to determine the need for follow-up and recommends specific actions to the user. 【0322】 As a concrete example, if the server receives elevated body temperature data from a robot's sensor one morning, the generating AI model analyzes this data and, if it determines that early medical attention is necessary, it notifies the user's terminal accordingly. It also simultaneously outputs a voice prompt to the user, such as "Drink some water." 【0323】 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. 【0324】 This invention incorporates an emotion engine that analyzes user emotions into a system designed to streamline patient management in medical institutions. In addition to collecting patient information, analyzing it using AI, automatically generating treatment schedules, sending notifications, and suggesting follow-up care, this system aims to reduce the burden on healthcare professionals and improve the quality of patient care by performing user emotion evaluations using the emotion engine. 【0325】 Data collection and storage 【0326】 First, the server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored securely. 【0327】 Data Analysis 【0328】 The server uses generative AI to analyze the collected data and determine the optimal treatment schedule for each patient. The AI ​​uses machine learning algorithms to predict effective treatment intervals and follow-up timings based on past data. 【0329】 Application of the emotion engine 【0330】 The server uses an emotion engine to analyze text data, such as comments entered by the user in natural language, and determine their emotional state. The emotion engine utilizes natural language processing technology to identify the user's stress level and emotional response. 【0331】 Schedule and notification generation 【0332】 Next, the server combines the analysis results and sentiment assessment to automatically generate the most effective consultation schedule and reminders. The generated notifications are adjusted based on the user's emotional state and sent to the healthcare professional's terminal. 【0333】 Receiving notifications and taking action 【0334】 The device presents the user with generated reminders and follow-up suggestions. The notification content is adjusted according to the user's emotions, ensuring that information is delivered in the appropriate tone and at the right time. 【0335】 Follow-up proposal 【0336】 Based on the analysis, the server suggests more frequent follow-ups if the patient's symptoms may be worsening or their emotional state is unstable. This allows healthcare professionals to better understand the patient's overall health status. 【0337】 Specific example 【0338】 For example, if patient B logs into the medical system and enters a comment about their condition, the server's emotion engine analyzes the text. If the analysis determines that patient B is experiencing stress, the server takes this information into consideration and sets an earlier-than-usual appointment reminder. This earlier reminder is notified to the doctor's terminal, and follow-up care, including emotional support for patient B, is suggested. 【0339】 This system allows healthcare professionals to visualize patient-centered care, further improving the quality of patient care. 【0340】 The following describes the processing flow. 【0341】 Step 1: 【0342】 The server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted and securely stored in the database. 【0343】 Step 2: 【0344】 The server processes the collected data using a generating AI for analysis. Here, the AI ​​uses machine learning algorithms based on past treatment patterns to optimize the treatment schedule. 【0345】 Step 3: 【0346】 The server automatically generates a medical schedule to determine the optimal next appointment date and follow-up period for each patient, based on the data analyzed by the generating AI. 【0347】 Step 4: 【0348】 The server sends user-entered comments and feedback to the emotion engine, which uses natural language processing technology to determine the emotional state. The emotion engine identifies stress levels and emotional changes. 【0349】 Step 5: 【0350】 The server integrates the analysis results from the emotion engine with the medical schedule and adjusts the notification content to match the user's emotional state. This creates an emotionally sensitive reminder. 【0351】 Step 6: 【0352】 The server sends coordinated reminders in real time to the devices used by healthcare professionals. Notifications appear on the devices as pop-ups or emails. 【0353】 Step 7: 【0354】 Users review emotionally sensitive reminders and follow-up suggestions displayed on their devices and take appropriate action. User feedback helps to further improve the system. 【0355】 (Example 2) 【0356】 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". 【0357】 Patient management in healthcare settings involves many time-consuming tasks, such as managing medical history, coordinating schedules, and planning follow-up appointments. Furthermore, appropriately understanding and reflecting patients' emotional states in their treatment is a significant burden for healthcare professionals. To address these challenges, there is a need for methods to automate patient emotional management and the creation of efficient treatment schedules. 【0358】 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. 【0359】 In this invention, the server includes means for collecting patient information from a storage medium, data analysis means using generative AI to analyze medical history based on the collected patient information, and means for evaluating comments entered by the user in natural language and determining the emotional state. This enables more efficient patient management and the provision of high-quality medical care that takes emotional states into consideration. 【0360】 "Patient information" refers to all data related to a patient, such as medical history, medication data, and schedule information. 【0361】 "Storage medium" refers to means of storing information, including databases and other data storage systems. 【0362】 "Generative AI" refers to artificial intelligence technologies used to analyze collected data and predict or generate specific results. 【0363】 "Data analysis methods" refer to the methods and techniques used to process and analyze collected data to derive insights and conclusions. 【0364】 "Clinic schedule" refers to the patient's appointment times and follow-up schedule. 【0365】 "Automatic generation" refers to the creation of information and schedules by artificial intelligence or algorithms without manual intervention. 【0366】 "Natural language" refers to the words and sentences that humans use in everyday life, and also to the technology that allows computers to understand or process them. 【0367】 "Emotional state" refers to the psychological or emotional reactions or conditions exhibited by the user, and this means determining these states through text analysis or other methods. 【0368】 "Notifications" refer to messages or alerts used to convey specific information to users or healthcare professionals. 【0369】 "Providing in real time" means that some kind of information or result is processed immediately and provided in an available state. 【0370】 The embodiments for carrying out the present invention will now be described. The present invention is a system for streamlining patient management in medical institutions, and aims to reduce the burden on healthcare professionals and improve the quality of patient care by comprehensively considering even the emotional state of patients. 【0371】 This system first has a server access the healthcare institution's database to collect patient information. This patient information includes medical history, medication data, and schedule information. The data is encrypted using technologies such as SSL / TLS and stored securely. 【0372】 The server then analyzes the collected patient information using a generative AI model. This model utilizes machine learning libraries such as Scikit-learn to predict treatment schedules and follow-up timings based on historical data. 【0373】 Furthermore, the server utilizes a natural language processing-based emotion engine to analyze natural language comments collected from users. The emotion engine uses a framework like TensorFlow to classify emotional states and determine the patient's stress level. 【0374】 Based on the analysis results, the server generates a medical schedule and notifications. These generated schedules and notifications are then adjusted using a Python script according to the patient's emotional state. The adjusted notifications are then sent to the healthcare professional's terminal. 【0375】 For example, if a patient enters a comment into the system saying, "I haven't been able to sleep well lately and I'm feeling a bit tired," the server's emotion engine analyzes the text and determines that the patient is experiencing stress. Based on this, an earlier-than-usual appointment reminder is set and notified to healthcare professionals. 【0376】 An example of a prompt to be input into the generating AI model is, "Based on past medical history, please suggest the optimal date for your next appointment." 【0377】 In this way, the system can provide medical management that takes the patient's emotional state into consideration. 【0378】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0379】 Step 1: 【0380】 The server accesses the healthcare institution's database to collect patient information such as medical history, medication data, and schedule information. Input requires a database query, which sends encrypted data to the server. Specifically, the server periodically accesses the database to retrieve the latest patient information. 【0381】 Step 2: 【0382】 The server passes the collected patient information to a generating AI model for data analysis. The patient's medical history is sent to the AI ​​model as input. Using the Scikit-learn library, the AI ​​predicts the medical schedule and outputs the optimal interval between appointments. Specifically, the AI ​​applies machine learning algorithms based on past data. 【0383】 Step 3: 【0384】 The server passes comments entered by users in natural language to an emotion engine for analysis. Text data is required as input. TensorFlow is used to determine the emotional state, and emotional information such as stress levels is obtained as output. Specifically, the user's comments are analyzed, and their emotional response is quantified. 【0385】 Step 4: 【0386】 The server generates treatment schedules and reminders by combining data analysis results and emotion assessments. The input requires AI analysis results and emotion assessments. A Python script automatically creates reminders based on this information. Specifically, the generated schedule is adjusted to reflect the patient's emotional state. 【0387】 Step 5: 【0388】 The server sends the generated reminders and follow-up suggestions to the healthcare professional's device. The input requires coordinated reminder information. The output is that the device receives the notification and provides the information to the user. Specifically, the server delivers reminders via Secure Email or a dedicated app. 【0389】 (Application Example 2) 【0390】 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." 【0391】 Providing appropriate care based on residents' emotions and health conditions in nursing care facilities is a significant burden for care staff. There is a need to alleviate this burden while improving the quality of care for residents. Traditional systems have had the problem of making it difficult to provide individualized care that is sensitive to residents' emotions, often resulting in uniform care being provided. 【0392】 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. 【0393】 In this invention, the server includes means for collecting user information from an information management system, means for analyzing information using a generative AI that analyzes health records based on the collected user information, and means including an emotion analysis engine that analyzes the user's emotional state. This makes it possible to propose appropriate care according to the user's individual emotional state and health condition. 【0394】 "User information" refers to data about residents of care facilities, including health records and emotional status. 【0395】 An "information management system" refers to a system that centrally manages user information and efficiently collects and provides it. 【0396】 "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze data and generate optimal care schedules. 【0397】 "Information analysis methods" refer to the process of analyzing health records and care requirements based on collected user information. 【0398】 An "emotion analysis engine" refers to a system that uses natural language processing technology to evaluate a user's emotional state and determine the need for care. 【0399】 "Notification methods" refer to means of informing care staff of automatically generated schedules and care suggestions. 【0400】 "Care proposal" refers to the process of suggesting appropriate responses and follow-ups based on the user's health and emotional state. 【0401】 The system implementing this invention is operated primarily using a server and terminals. The server collects user information from an information management system and acquires health records and emotional states. Next, it uses a generative AI model to analyze this data and automatically generate a care schedule tailored to each user's health state and emotional state. 【0402】 The server also uses an emotion analysis engine to analyze natural language data entered by the user and evaluate the user's emotional state. This involves using natural language processing techniques, such as combining space keys and morphological analysis libraries. 【0403】 The generated schedules and care suggestions are sent to the terminal in real time. The terminal notifies care staff of necessary care information. This notification method includes alerts and pop-up messages to enable care staff to respond quickly. 【0404】 For example, if a user enters a comment into the app saying, "I've been having trouble sleeping lately and I'm feeling anxious," the server analyzes the text, and the emotion analysis engine detects the user's stress level. Based on the results, the server notifies the user's device with a "suggestion for relaxation time." 【0405】 An example of a prompt message is, "Please enter a comment from the resident. We will analyze their emotions and provide appropriate care suggestions." This allows the system to efficiently analyze user input and propose the optimal care plan to the staff. 【0406】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0407】 Step 1: 【0408】 The server collects user information from the information management system. The input consists of health records and emotional state-related data stored in the facility's database. This data is extracted using a secure protocol, encrypted, and sent to the server's analysis system. 【0409】 Step 2: 【0410】 The server analyzes user information collected using a generative AI model. The input is encrypted data obtained in step 1, which is decrypted, and machine learning algorithms are used to analyze the user's past health status and emotional patterns. Based on this analysis, a personalized care schedule is automatically generated. 【0411】 Step 3: 【0412】 The server receives natural language comments entered by the user on their device. User input is direct natural language data, which is sent to the sentiment analysis engine. The server uses natural language processing to analyze the user's emotional state from the text data and determine their stress level and emotional tendencies. 【0413】 Step 4: 【0414】 The server constructs appropriate care suggestions based on the generated care schedule and emotion analysis results. The input is the results from steps 2 and 3, and this information is combined to determine the care plan and the need for follow-up. The server then prepares to send the suggestions to the care staff. 【0415】 Step 5: 【0416】 The terminal receives care suggestions sent from the server. Input is notification data from the server. The terminal provides real-time information to care staff using pop-up alerts and message notifications. This is a crucial step in facilitating prompt responses tailored to the care situation. 【0417】 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. 【0418】 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. 【0419】 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. 【0420】 [Third Embodiment] 【0421】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0422】 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. 【0423】 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). 【0424】 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. 【0425】 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. 【0426】 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). 【0427】 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. 【0428】 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. 【0429】 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. 【0430】 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. 【0431】 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. 【0432】 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". 【0433】 This invention is a system for streamlining patient management in medical institutions, providing integrated functions for patient information collection, analysis, schedule generation, notification, and follow-up suggestions. The details and operation of each component are described below. 【0434】 Data collection and storage 【0435】 First, the server connects to the healthcare institution's database and collects patient medical history, medication data, and schedule information. The collected information is encrypted and stored in a secure database. 【0436】 Data Analysis 【0437】 Next, the server uses a generation AI to analyze the patient's past medical history and medication data to predict the appropriate timing for medical treatment and medication. This makes it possible to create an optimal care plan for each patient. 【0438】 Schedule generation 【0439】 The server automatically generates a patient's medical schedule based on the analysis results. This includes the next appointment date and the timing of necessary follow-up appointments. 【0440】 Notification function 【0441】 Based on the generated schedule, the server automatically sends reminders to healthcare workers' devices. Notifications are delivered as pop-ups on the device screen or via email. 【0442】 Follow-up proposal 【0443】 The server continuously analyzes the patient's condition and assesses the need for follow-up. If a worsening of symptoms is predicted or if changes in medication instructions are necessary, it provides the user with specific follow-up suggestions. 【0444】 Specific example 【0445】 When new medical data for patient A is recorded, the server immediately retrieves the data and analyzes it using a generating AI. Based on this analysis, it is determined that the optimal time for the next consultation is two weeks later. The server then creates a schedule based on this determination and sends it as a reminder to the doctor's terminal. If an abnormality is detected in patient A's medication history, the user receives a notification suggesting a follow-up in three days. 【0446】 Thus, the system of the present invention provides a practical and effective means to streamline the work of healthcare professionals and improve patient care. 【0447】 The following describes the processing flow. 【0448】 Step 1: 【0449】 The server accesses the healthcare institution's database regularly every day to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored in its own database. 【0450】 Step 2: 【0451】 The server first performs data preprocessing to analyze the collected data. This involves detecting and correcting outliers, imputing missing values, and converting the data into a format suitable for analysis. 【0452】 Step 3: 【0453】 The server supplies pre-processed data to the generating AI, which uses machine learning algorithms to analyze each patient's medical history and medication patterns. This analysis predicts the optimal medical schedule and follow-up timing for each patient. 【0454】 Step 4: 【0455】 The server automatically generates a treatment schedule for each patient based on the analysis results. This schedule includes appointment reminders and medication timings. 【0456】 Step 5: 【0457】 The server sends notifications to healthcare workers' devices based on the generated schedule. These notifications are delivered promptly to the devices via pop-ups or email, prompting them to acknowledge them. 【0458】 Step 6: 【0459】 The server continuously monitors and analyzes patient status data. If changes in the situation or signs of worsening symptoms are detected, it assesses the need for follow-up and makes appropriate recommendations. 【0460】 Step 7: 【0461】 Users receive notifications and suggestions displayed on their devices and take specific actions such as medical treatment or follow-up. They also provide feedback to the system as needed, contributing to its improvement. 【0462】 (Example 1) 【0463】 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." 【0464】 Patient management in healthcare institutions requires the collection and analysis of vast amounts of information, posing challenges such as the time and effort required to provide effective care. Furthermore, rapid and accurate information processing is essential for creating appropriate treatment schedules and determining the need for follow-up. 【0465】 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. 【0466】 In this invention, the server includes means for securely collecting and storing individual data from medical institutions, information processing means using generative AI to analyze history based on the collected individual data, and means for automatically generating schedules according to the information analysis results. This streamlines the work of medical professionals and enables the provision of accurate and prompt care to patients. 【0467】 A "medical institution" is an organization that provides medical services to maintain and improve people's health. 【0468】 "Individualized data" refers to information related to a specific individual, including medical records and treatment history. 【0469】 "Generative AI" is an artificial intelligence technology that generates new information based on input data and supports analysis and decision-making. 【0470】 "Information processing means" refers to methods and technologies for receiving data, processing and analyzing it, and obtaining useful information. 【0471】 A "notification function" is a function used to transmit specific information to a target audience, and generally includes push notifications, emails, and alerts. 【0472】 "Communication equipment" refers to devices used to send and receive data and information from remote locations. 【0473】 "Follow-up" is the process of continuously monitoring a patient's health status and providing additional medical care or guidance as needed. 【0474】 This invention is a system designed to improve the efficiency of patient management operations in medical institutions. The server first securely connects to the medical institution's database and collects individual patient data such as medical history, medication data, and schedule information. The collected data is securely stored using encryption technologies such as AES-256. 【0475】 Next, the server uses a generative AI model to analyze the collected patient data. This analysis employs natural language processing (NLP) and machine learning algorithms to predict the optimal timing for medical treatment and medication. Based on these results, the server automatically generates a personalized treatment schedule and builds a continuous care plan. 【0476】 Once a schedule is generated, the server notifies healthcare professionals of its contents on their terminals. The terminals display the received information as a pop-up on the screen and also send notifications via email. This ensures that healthcare professionals receive information quickly, enabling them to provide appropriate medical care. 【0477】 Furthermore, the server continuously analyzes the patient's condition and determines the need for follow-up. For example, if a worsening of symptoms is predicted, it will suggest specific follow-up options to the user, improving the quality of medical care. An example of a specific prompt message is: "Based on patient A's latest medical data, predict the next appointment date. Also, evaluate whether follow-up is necessary based on their medication history." 【0478】 Thus, the present invention enables efficient collection and analysis of medical information, thereby reducing the burden on healthcare professionals and providing prompt and appropriate care to patients. 【0479】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0480】 Step 1: 【0481】 The server connects to the healthcare institution's database and collects individual data. Inputs include patient medical history, medication data, and schedule information, which are retrieved using a secure communication protocol. As output, this information is encrypted and stored in a secure database. 【0482】 Step 2: 【0483】 The server inputs the collected data into a generating AI model for analysis. Specifically, it uses natural language processing and machine learning algorithms to predict the optimal timing for medical treatment and medication. This process outputs analysis results tailored to each individual patient. 【0484】 Step 3: 【0485】 The server generates a patient-specific treatment schedule based on the analysis results of the generated AI. It takes the analysis results as input and automatically calculates the next appointment date and necessary follow-up appointments. This allows it to output the optimal treatment schedule. 【0486】 Step 4: 【0487】 The server notifies healthcare workers' terminals of the generated schedule. Specifically, it sends reminders to terminals via a notification system. The input is the generated medical schedule, and the output is a pop-up notification on the terminal or an email. 【0488】 Step 5: 【0489】 The server continuously monitors the patient's condition and assesses the need for follow-up. Input data includes the patient's latest medical results and medication information. A generating AI determines the need for follow-up and outputs the results as a suggestion to the user. This step improves the quality of medical care. 【0490】 (Application Example 1) 【0491】 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." 【0492】 In healthcare settings, patient management is inefficient due to the time and effort required. This is particularly challenging in an aging society where the demand for home care is increasing, making it difficult to adequately manage patients' health in their home environment. Furthermore, there is a lack of means for patients and caregivers to understand treatment schedules and follow-up needs in real time. Therefore, there is a need for an efficient and effective patient management system to address these issues. 【0493】 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. 【0494】 In this invention, the server includes means for collecting patient information from a database, means for data analysis using generative AI to analyze medical history based on the collected patient information, and means for automatically generating a patient's medical schedule based on the analysis results. This enables efficient management of the patient's health status even in a home environment, and allows for the proposal of appropriate medical schedules and follow-up in cooperation with medical institutions. 【0495】 "Patient information" refers to detailed data held by a medical institution, such as a patient's medical history, medication data, and health status. 【0496】 A "database" is an information management system that organizes and stores large amounts of data, such as patient information and medical history, and allows for quick searching and retrieval when needed. 【0497】 "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict and propose desired results. 【0498】 "Data analysis methods" refer to the processes and technologies used to analyze collected patient information using generated AI to evaluate treatment schedules and the need for follow-up. 【0499】 "Automatic generation methods" refer to a system that uses the results of data analysis to automatically construct a patient's medical treatment schedule. 【0500】 "Means of sending notifications" refers to the function of sending generated schedules and important information to communication devices in real time. 【0501】 "Follow-up" refers to the activity of continuously observing and evaluating a patient's health status and proposing additional medical measures as needed. 【0502】 A "sensing device" is a device or sensor used to monitor a patient's health status in real time and to link data. 【0503】 "Means of presentation using visual and auditory means" refer to methods of conveying information to users visually and audibly through displays and speakers. 【0504】 The system for implementing this invention is a system that collects patient information from a database and analyzes it using a generated AI model, in order to streamline patient management in medical institutions and homes. 【0505】 The server connects to the healthcare institution's database and securely retrieves patient medical history and medication data. This data is encrypted and stored in a secure database. The software used by the server is a database management system and a generative AI model equipped with machine learning algorithms. 【0506】 The server inputs collected patient information into a generating AI model and performs data analysis to predict the appropriate timing for medical treatment and medication. Based on the analysis results, the server automatically generates the patient's medical schedule and transmits this information to a communication device. Communication devices include tablets, smartphones, or care robots used in the home. Notifications are made in real time and presented to the user visually and audibly. 【0507】 The server further continuously monitors the patient's health status using sensing devices and shares information with medical institutions as needed. This function allows for the assessment of the need for individualized follow-up for each patient and provides users with specific action suggestions. For example, based on the results of daily temperature measurements, the AI ​​might suggest, "Drink some water." 【0508】 Examples of prompts include, "Based on the patient's latest health data, please suggest the optimal appointment date," and "Considering the patient's recent medical history, please suggest follow-up actions until the next appointment." The generative AI model performs appropriate analysis of these prompts and presents the optimal medical plan. 【0509】 Thus, the system aims to support patients' health management in a practical and effective way. 【0510】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0511】 Step 1: 【0512】 The server connects to the healthcare institution's database and retrieves patient information, such as medical history and medication data. Input is a query to the database, and output is patient information encrypted in a secure format. This information is stored in a secure database for subsequent analysis processes. 【0513】 Step 2: 【0514】 The server inputs acquired patient information into a generating AI model for data analysis. The input consists of the patient's medical history and medication data, which the generating AI model analyzes to predict the appropriate timing for medical treatment and medication. The algorithm used in this process is machine learning, which learns data patterns to make appropriate predictions. 【0515】 Step 3: 【0516】 The server automatically generates a patient's medical schedule based on the analysis results obtained from the generated AI model. The input is the analysis results, and the output is a specific medical schedule. This schedule includes the next appointment date and the timing of necessary follow-up appointments. The server achieves this using schedule management software. 【0517】 Step 4: 【0518】 The server notifies the communication device of the generated schedule. The input is the automatically generated medical schedule, and the output is a reminder-style notification displayed on the terminal. The terminal presents the schedule to the user visually or audibly, allowing the end user to check it in real time. 【0519】 Step 5: 【0520】 The server continuously monitors the patient's health status through sensing devices and shares information with healthcare institutions as needed. Input is the latest health data transmitted from the sensing devices, and output is the latest health status analysis and necessary follow-up suggestions. A generating AI model uses this data to determine the need for follow-up and recommends specific actions to the user. 【0521】 As a concrete example, if the server receives elevated body temperature data from a robot's sensor one morning, the generating AI model analyzes this data and, if it determines that early medical attention is necessary, it notifies the user's terminal accordingly. It also simultaneously outputs a voice prompt to the user, such as "Drink some water." 【0522】 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. 【0523】 This invention incorporates an emotion engine that analyzes user emotions into a system designed to streamline patient management in medical institutions. In addition to collecting patient information, analyzing it using AI, automatically generating treatment schedules, sending notifications, and suggesting follow-up care, this system aims to reduce the burden on healthcare professionals and improve the quality of patient care by performing user emotion evaluations using the emotion engine. 【0524】 Data collection and storage 【0525】 First, the server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored securely. 【0526】 Data Analysis 【0527】 The server uses generative AI to analyze the collected data and determine the optimal treatment schedule for each patient. The AI ​​uses machine learning algorithms to predict effective treatment intervals and follow-up timings based on past data. 【0528】 Application of the emotion engine 【0529】 The server uses an emotion engine to analyze text data, such as comments entered by the user in natural language, and determine their emotional state. The emotion engine utilizes natural language processing technology to identify the user's stress level and emotional response. 【0530】 Schedule and notification generation 【0531】 Next, the server combines the analysis results and sentiment assessment to automatically generate the most effective consultation schedule and reminders. The generated notifications are adjusted based on the user's emotional state and sent to the healthcare professional's terminal. 【0532】 Receiving notifications and taking action 【0533】 The device presents the user with generated reminders and follow-up suggestions. The notification content is adjusted according to the user's emotions, ensuring that information is delivered in the appropriate tone and at the right time. 【0534】 Follow-up proposal 【0535】 Based on the analysis, the server suggests more frequent follow-ups if the patient's symptoms may be worsening or their emotional state is unstable. This allows healthcare professionals to better understand the patient's overall health status. 【0536】 Specific example 【0537】 For example, if patient B logs into the medical system and enters a comment about their condition, the server's emotion engine analyzes the text. If the analysis determines that patient B is experiencing stress, the server takes this information into consideration and sets an earlier-than-usual appointment reminder. This earlier reminder is notified to the doctor's terminal, and follow-up care, including emotional support for patient B, is suggested. 【0538】 This system allows healthcare professionals to visualize patient-centered care, further improving the quality of patient care. 【0539】 The following describes the processing flow. 【0540】 Step 1: 【0541】 The server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted and securely stored in the database. 【0542】 Step 2: 【0543】 The server processes the collected data using a generating AI for analysis. Here, the AI ​​uses machine learning algorithms based on past treatment patterns to optimize the treatment schedule. 【0544】 Step 3: 【0545】 The server automatically generates a medical schedule to determine the optimal next appointment date and follow-up period for each patient, based on the data analyzed by the generating AI. 【0546】 Step 4: 【0547】 The server sends user-entered comments and feedback to the emotion engine, which uses natural language processing technology to determine the emotional state. The emotion engine identifies stress levels and emotional changes. 【0548】 Step 5: 【0549】 The server integrates the analysis results from the emotion engine with the medical schedule and adjusts the notification content to match the user's emotional state. This creates an emotionally sensitive reminder. 【0550】 Step 6: 【0551】 The server sends coordinated reminders in real time to the devices used by healthcare professionals. Notifications appear on the devices as pop-ups or emails. 【0552】 Step 7: 【0553】 Users review emotionally sensitive reminders and follow-up suggestions displayed on their devices and take appropriate action. User feedback helps to further improve the system. 【0554】 (Example 2) 【0555】 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." 【0556】 Patient management in healthcare settings involves many time-consuming tasks, such as managing medical history, coordinating schedules, and planning follow-up appointments. Furthermore, appropriately understanding and reflecting patients' emotional states in their treatment is a significant burden for healthcare professionals. To address these challenges, there is a need for methods to automate patient emotional management and the creation of efficient treatment schedules. 【0557】 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. 【0558】 In this invention, the server includes means for collecting patient information from a storage medium, data analysis means using generative AI to analyze medical history based on the collected patient information, and means for evaluating comments entered by the user in natural language and determining the emotional state. This enables more efficient patient management and the provision of high-quality medical care that takes emotional states into consideration. 【0559】 "Patient information" refers to all data related to a patient, such as medical history, medication data, and schedule information. 【0560】 "Storage medium" refers to means of storing information, including databases and other data storage systems. 【0561】 "Generative AI" refers to artificial intelligence technologies used to analyze collected data and predict or generate specific results. 【0562】 "Data analysis methods" refer to the methods and techniques used to process and analyze collected data to derive insights and conclusions. 【0563】 "Clinic schedule" refers to the patient's appointment times and follow-up schedule. 【0564】 "Automatic generation" refers to the creation of information and schedules by artificial intelligence or algorithms without manual intervention. 【0565】 "Natural language" refers to the words and sentences that humans use in everyday life, and also to the technology that allows computers to understand or process them. 【0566】 "Emotional state" refers to the psychological or emotional reactions or conditions exhibited by the user, and this means determining these states through text analysis or other methods. 【0567】 "Notifications" refer to messages or alerts used to convey specific information to users or healthcare professionals. 【0568】 "Providing in real time" means that some kind of information or result is processed immediately and provided in an available state. 【0569】 The embodiments for carrying out the present invention will now be described. The present invention is a system for streamlining patient management in medical institutions, and aims to reduce the burden on healthcare professionals and improve the quality of patient care by comprehensively considering even the emotional state of patients. 【0570】 This system first has a server access the healthcare institution's database to collect patient information. This patient information includes medical history, medication data, and schedule information. The data is encrypted using technologies such as SSL / TLS and stored securely. 【0571】 The server then analyzes the collected patient information using a generative AI model. This model utilizes machine learning libraries such as Scikit-learn to predict treatment schedules and follow-up timings based on historical data. 【0572】 Furthermore, the server utilizes a natural language processing-based emotion engine to analyze natural language comments collected from users. The emotion engine uses a framework like TensorFlow to classify emotional states and determine the patient's stress level. 【0573】 Based on the analysis results, the server generates a medical schedule and notifications. These generated schedules and notifications are then adjusted using a Python script according to the patient's emotional state. The adjusted notifications are then sent to the healthcare professional's terminal. 【0574】 For example, if a patient enters a comment into the system saying, "I haven't been able to sleep well lately and I'm feeling a bit tired," the server's emotion engine analyzes the text and determines that the patient is experiencing stress. Based on this, an earlier-than-usual appointment reminder is set and notified to healthcare professionals. 【0575】 An example of a prompt to be input into the generating AI model is, "Based on past medical history, please suggest the optimal date for your next appointment." 【0576】 In this way, the system can provide medical management that takes the patient's emotional state into consideration. 【0577】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0578】 Step 1: 【0579】 The server accesses the healthcare institution's database to collect patient information such as medical history, medication data, and schedule information. Input requires a database query, which sends encrypted data to the server. Specifically, the server periodically accesses the database to retrieve the latest patient information. 【0580】 Step 2: 【0581】 The server passes the collected patient information to a generating AI model for data analysis. The patient's medical history is sent to the AI ​​model as input. Using the Scikit-learn library, the AI ​​predicts the medical schedule and outputs the optimal interval between appointments. Specifically, the AI ​​applies machine learning algorithms based on past data. 【0582】 Step 3: 【0583】 The server passes comments entered by users in natural language to an emotion engine for analysis. Text data is required as input. TensorFlow is used to determine the emotional state, and emotional information such as stress levels is obtained as output. Specifically, the user's comments are analyzed, and their emotional response is quantified. 【0584】 Step 4: 【0585】 The server generates treatment schedules and reminders by combining data analysis results and emotion assessments. The input requires AI analysis results and emotion assessments. A Python script automatically creates reminders based on this information. Specifically, the generated schedule is adjusted to reflect the patient's emotional state. 【0586】 Step 5: 【0587】 The server sends the generated reminders and follow-up suggestions to the healthcare professional's device. The input requires coordinated reminder information. The output is that the device receives the notification and provides the information to the user. Specifically, the server delivers reminders via Secure Email or a dedicated app. 【0588】 (Application Example 2) 【0589】 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." 【0590】 Providing appropriate care based on residents' emotions and health conditions in nursing care facilities is a significant burden for care staff. There is a need to alleviate this burden while improving the quality of care for residents. Traditional systems have had the problem of making it difficult to provide individualized care that is sensitive to residents' emotions, often resulting in uniform care being provided. 【0591】 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. 【0592】 In this invention, the server includes means for collecting user information from an information management system, means for analyzing information using a generative AI that analyzes health records based on the collected user information, and means including an emotion analysis engine that analyzes the user's emotional state. This makes it possible to propose appropriate care according to the user's individual emotional state and health condition. 【0593】 "User information" refers to data about residents of care facilities, including health records and emotional status. 【0594】 An "information management system" refers to a system that centrally manages user information and efficiently collects and provides it. 【0595】 "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze data and generate optimal care schedules. 【0596】 "Information analysis methods" refer to the process of analyzing health records and care requirements based on collected user information. 【0597】 An "emotion analysis engine" refers to a system that uses natural language processing technology to evaluate a user's emotional state and determine the need for care. 【0598】 "Notification methods" refer to means of informing care staff of automatically generated schedules and care suggestions. 【0599】 "Care proposal" refers to the process of suggesting appropriate responses and follow-ups based on the user's health and emotional state. 【0600】 The system implementing this invention is operated primarily using a server and terminals. The server collects user information from an information management system and acquires health records and emotional states. Next, it uses a generative AI model to analyze this data and automatically generate a care schedule tailored to each user's health state and emotional state. 【0601】 The server also uses an emotion analysis engine to analyze natural language data entered by the user and evaluate the user's emotional state. This involves using natural language processing techniques, such as combining space keys and morphological analysis libraries. 【0602】 The generated schedules and care suggestions are sent to the terminal in real time. The terminal notifies care staff of necessary care information. This notification method includes alerts and pop-up messages to enable care staff to respond quickly. 【0603】 For example, if a user enters a comment into the app saying, "I've been having trouble sleeping lately and I'm feeling anxious," the server analyzes the text, and the emotion analysis engine detects the user's stress level. Based on the results, the server notifies the user's device with a "suggestion for relaxation time." 【0604】 An example of a prompt message is, "Please enter a comment from the resident. We will analyze their emotions and provide appropriate care suggestions." This allows the system to efficiently analyze user input and propose the optimal care plan to the staff. 【0605】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0606】 Step 1: 【0607】 The server collects user information from the information management system. The input consists of health records and emotional state-related data stored in the facility's database. This data is extracted using a secure protocol, encrypted, and sent to the server's analysis system. 【0608】 Step 2: 【0609】 The server analyzes user information collected using a generative AI model. The input is encrypted data obtained in step 1, which is decrypted, and machine learning algorithms are used to analyze the user's past health status and emotional patterns. Based on this analysis, a personalized care schedule is automatically generated. 【0610】 Step 3: 【0611】 The server receives natural language comments entered by the user on their device. User input is direct natural language data, which is sent to the sentiment analysis engine. The server uses natural language processing to analyze the user's emotional state from the text data and determine their stress level and emotional tendencies. 【0612】 Step 4: 【0613】 The server constructs appropriate care suggestions based on the generated care schedule and emotion analysis results. The input is the results from steps 2 and 3, and this information is combined to determine the care plan and the need for follow-up. The server then prepares to send the suggestions to the care staff. 【0614】 Step 5: 【0615】 The terminal receives care suggestions sent from the server. Input is notification data from the server. The terminal provides real-time information to care staff using pop-up alerts and message notifications. This is a crucial step in facilitating prompt responses tailored to the care situation. 【0616】 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. 【0617】 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. 【0618】 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. 【0619】 [Fourth Embodiment] 【0620】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0621】 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. 【0622】 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). 【0623】 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. 【0624】 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. 【0625】 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). 【0626】 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. 【0627】 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. 【0628】 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. 【0629】 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. 【0630】 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. 【0631】 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. 【0632】 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". 【0633】 This invention is a system for streamlining patient management in medical institutions, providing integrated functions for patient information collection, analysis, schedule generation, notification, and follow-up suggestions. The details and operation of each component are described below. 【0634】 Data collection and storage 【0635】 First, the server connects to the healthcare institution's database and collects patient medical history, medication data, and schedule information. The collected information is encrypted and stored in a secure database. 【0636】 Data Analysis 【0637】 Next, the server uses a generation AI to analyze the patient's past medical history and medication data to predict the appropriate timing for medical treatment and medication. This makes it possible to create an optimal care plan for each patient. 【0638】 Schedule generation 【0639】 The server automatically generates a patient's medical schedule based on the analysis results. This includes the next appointment date and the timing of necessary follow-up appointments. 【0640】 Notification function 【0641】 Based on the generated schedule, the server automatically sends reminders to healthcare workers' devices. Notifications are delivered as pop-ups on the device screen or via email. 【0642】 Follow-up proposal 【0643】 The server continuously analyzes the patient's condition and assesses the need for follow-up. If a worsening of symptoms is predicted or if changes in medication instructions are necessary, it provides the user with specific follow-up suggestions. 【0644】 Specific example 【0645】 When new medical data for patient A is recorded, the server immediately retrieves the data and analyzes it using a generating AI. Based on this analysis, it is determined that the optimal time for the next consultation is two weeks later. The server then creates a schedule based on this determination and sends it as a reminder to the doctor's terminal. If an abnormality is detected in patient A's medication history, the user receives a notification suggesting a follow-up in three days. 【0646】 Thus, the system of the present invention provides a practical and effective means to streamline the work of healthcare professionals and improve patient care. 【0647】 The following describes the processing flow. 【0648】 Step 1: 【0649】 The server accesses the healthcare institution's database regularly every day to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored in its own database. 【0650】 Step 2: 【0651】 The server first performs data preprocessing to analyze the collected data. This involves detecting and correcting outliers, imputing missing values, and converting the data into a format suitable for analysis. 【0652】 Step 3: 【0653】 The server supplies pre-processed data to the generating AI, which uses machine learning algorithms to analyze each patient's medical history and medication patterns. This analysis predicts the optimal medical schedule and follow-up timing for each patient. 【0654】 Step 4: 【0655】 The server automatically generates a treatment schedule for each patient based on the analysis results. This schedule includes appointment reminders and medication timings. 【0656】 Step 5: 【0657】 The server sends notifications to healthcare workers' devices based on the generated schedule. These notifications are delivered promptly to the devices via pop-ups or email, prompting them to acknowledge them. 【0658】 Step 6: 【0659】 The server continuously monitors and analyzes patient status data. If changes in the situation or signs of worsening symptoms are detected, it assesses the need for follow-up and makes appropriate recommendations. 【0660】 Step 7: 【0661】 Users receive notifications and suggestions displayed on their devices and take specific actions such as medical treatment or follow-up. They also provide feedback to the system as needed, contributing to its improvement. 【0662】 (Example 1) 【0663】 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". 【0664】 Patient management in healthcare institutions requires the collection and analysis of vast amounts of information, posing challenges such as the time and effort required to provide effective care. Furthermore, rapid and accurate information processing is essential for creating appropriate treatment schedules and determining the need for follow-up. 【0665】 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. 【0666】 In this invention, the server includes means for securely collecting and storing individual data from medical institutions, information processing means using generative AI to analyze history based on the collected individual data, and means for automatically generating schedules according to the information analysis results. This streamlines the work of medical professionals and enables the provision of accurate and prompt care to patients. 【0667】 A "medical institution" is an organization that provides medical services to maintain and improve people's health. 【0668】 "Individualized data" refers to information related to a specific individual, including medical records and treatment history. 【0669】 "Generative AI" is an artificial intelligence technology that generates new information based on input data and supports analysis and decision-making. 【0670】 "Information processing means" refers to methods and technologies for receiving data, processing and analyzing it, and obtaining useful information. 【0671】 A "notification function" is a function used to transmit specific information to a target audience, and generally includes push notifications, emails, and alerts. 【0672】 "Communication equipment" refers to devices used to send and receive data and information from remote locations. 【0673】 "Follow-up" is the process of continuously monitoring a patient's health status and providing additional medical care or guidance as needed. 【0674】 This invention is a system designed to improve the efficiency of patient management operations in medical institutions. The server first securely connects to the medical institution's database and collects individual patient data such as medical history, medication data, and schedule information. The collected data is securely stored using encryption technologies such as AES-256. 【0675】 Next, the server uses a generative AI model to analyze the collected patient data. This analysis employs natural language processing (NLP) and machine learning algorithms to predict the optimal timing for medical treatment and medication. Based on these results, the server automatically generates a personalized treatment schedule and builds a continuous care plan. 【0676】 Once a schedule is generated, the server notifies healthcare professionals of its contents on their terminals. The terminals display the received information as a pop-up on the screen and also send notifications via email. This ensures that healthcare professionals receive information quickly, enabling them to provide appropriate medical care. 【0677】 Furthermore, the server continuously analyzes the patient's condition and determines the need for follow-up. For example, if a worsening of symptoms is predicted, it will suggest specific follow-up options to the user, improving the quality of medical care. An example of a specific prompt message is: "Based on patient A's latest medical data, predict the next appointment date. Also, evaluate whether follow-up is necessary based on their medication history." 【0678】 Thus, the present invention enables efficient collection and analysis of medical information, thereby reducing the burden on healthcare professionals and providing prompt and appropriate care to patients. 【0679】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0680】 Step 1: 【0681】 The server connects to the healthcare institution's database and collects individual data. Inputs include patient medical history, medication data, and schedule information, which are retrieved using a secure communication protocol. As output, this information is encrypted and stored in a secure database. 【0682】 Step 2: 【0683】 The server inputs the collected data into a generating AI model for analysis. Specifically, it uses natural language processing and machine learning algorithms to predict the optimal timing for medical treatment and medication. This process outputs analysis results tailored to each individual patient. 【0684】 Step 3: 【0685】 The server generates a patient-specific treatment schedule based on the analysis results of the generated AI. It takes the analysis results as input and automatically calculates the next appointment date and necessary follow-up appointments. This allows it to output the optimal treatment schedule. 【0686】 Step 4: 【0687】 The server notifies healthcare workers' terminals of the generated schedule. Specifically, it sends reminders to terminals via a notification system. The input is the generated medical schedule, and the output is a pop-up notification on the terminal or an email. 【0688】 Step 5: 【0689】 The server continuously monitors the patient's condition and assesses the need for follow-up. Input data includes the patient's latest medical results and medication information. A generating AI determines the need for follow-up and outputs the results as a suggestion to the user. This step improves the quality of medical care. 【0690】 (Application Example 1) 【0691】 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". 【0692】 In healthcare settings, patient management is inefficient due to the time and effort required. This is particularly challenging in an aging society where the demand for home care is increasing, making it difficult to adequately manage patients' health in their home environment. Furthermore, there is a lack of means for patients and caregivers to understand treatment schedules and follow-up needs in real time. Therefore, there is a need for an efficient and effective patient management system to address these issues. 【0693】 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. 【0694】 In this invention, the server includes means for collecting patient information from a database, means for data analysis using generative AI to analyze medical history based on the collected patient information, and means for automatically generating a patient's medical schedule based on the analysis results. This enables efficient management of the patient's health status even in a home environment, and allows for the proposal of appropriate medical schedules and follow-up in cooperation with medical institutions. 【0695】 "Patient information" refers to detailed data held by a medical institution, such as a patient's medical history, medication data, and health status. 【0696】 A "database" is an information management system that organizes and stores large amounts of data, such as patient information and medical history, and allows for quick searching and retrieval when needed. 【0697】 "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict and propose desired results. 【0698】 "Data analysis methods" refer to the processes and technologies used to analyze collected patient information using generated AI to evaluate treatment schedules and the need for follow-up. 【0699】 "Automatic generation methods" refer to a system that uses the results of data analysis to automatically construct a patient's medical treatment schedule. 【0700】 "Means of sending notifications" refers to the function of sending generated schedules and important information to communication devices in real time. 【0701】 "Follow-up" refers to the activity of continuously observing and evaluating a patient's health status and proposing additional medical measures as needed. 【0702】 A "sensing device" is a device or sensor used to monitor a patient's health status in real time and to link data. 【0703】 "Means of presentation using visual and auditory means" refer to methods of conveying information to users visually and audibly through displays and speakers. 【0704】 The system for implementing this invention is a system that collects patient information from a database and analyzes it using a generated AI model, in order to streamline patient management in medical institutions and homes. 【0705】 The server connects to the healthcare institution's database and securely retrieves patient medical history and medication data. This data is encrypted and stored in a secure database. The software used by the server is a database management system and a generative AI model equipped with machine learning algorithms. 【0706】 The server inputs collected patient information into a generating AI model and performs data analysis to predict the appropriate timing for medical treatment and medication. Based on the analysis results, the server automatically generates the patient's medical schedule and transmits this information to a communication device. Communication devices include tablets, smartphones, or care robots used in the home. Notifications are made in real time and presented to the user visually and audibly. 【0707】 The server further continuously monitors the patient's health status using sensing devices and shares information with medical institutions as needed. This function allows for the assessment of the need for individualized follow-up for each patient and provides users with specific action suggestions. For example, based on the results of daily temperature measurements, the AI ​​might suggest, "Drink some water." 【0708】 Examples of prompts include, "Based on the patient's latest health data, please suggest the optimal appointment date," and "Considering the patient's recent medical history, please suggest follow-up actions until the next appointment." The generative AI model performs appropriate analysis of these prompts and presents the optimal medical plan. 【0709】 Thus, the system aims to support patients' health management in a practical and effective way. 【0710】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0711】 Step 1: 【0712】 The server connects to the healthcare institution's database and retrieves patient information, such as medical history and medication data. Input is a query to the database, and output is patient information encrypted in a secure format. This information is stored in a secure database for subsequent analysis processes. 【0713】 Step 2: 【0714】 The server inputs acquired patient information into a generating AI model for data analysis. The input consists of the patient's medical history and medication data, which the generating AI model analyzes to predict the appropriate timing for medical treatment and medication. The algorithm used in this process is machine learning, which learns data patterns to make appropriate predictions. 【0715】 Step 3: 【0716】 The server automatically generates a patient's medical schedule based on the analysis results obtained from the generated AI model. The input is the analysis results, and the output is a specific medical schedule. This schedule includes the next appointment date and the timing of necessary follow-up appointments. The server achieves this using schedule management software. 【0717】 Step 4: 【0718】 The server notifies the communication device of the generated schedule. The input is the automatically generated medical schedule, and the output is a reminder-style notification displayed on the terminal. The terminal presents the schedule to the user visually or audibly, allowing the end user to check it in real time. 【0719】 Step 5: 【0720】 The server continuously monitors the patient's health status through sensing devices and shares information with healthcare institutions as needed. Input is the latest health data transmitted from the sensing devices, and output is the latest health status analysis and necessary follow-up suggestions. A generating AI model uses this data to determine the need for follow-up and recommends specific actions to the user. 【0721】 As a concrete example, if the server receives elevated body temperature data from a robot's sensor one morning, the generating AI model analyzes this data and, if it determines that early medical attention is necessary, it notifies the user's terminal accordingly. It also simultaneously outputs a voice prompt to the user, such as "Drink some water." 【0722】 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. 【0723】 This invention incorporates an emotion engine that analyzes user emotions into a system designed to streamline patient management in medical institutions. In addition to collecting patient information, analyzing it using AI, automatically generating treatment schedules, sending notifications, and suggesting follow-up care, this system aims to reduce the burden on healthcare professionals and improve the quality of patient care by performing user emotion evaluations using the emotion engine. 【0724】 Data collection and storage 【0725】 First, the server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted for security purposes and stored securely. 【0726】 Data Analysis 【0727】 The server uses generative AI to analyze the collected data and determine the optimal treatment schedule for each patient. The AI ​​uses machine learning algorithms to predict effective treatment intervals and follow-up timings based on past data. 【0728】 Application of the emotion engine 【0729】 The server uses an emotion engine to analyze text data, such as comments entered by the user in natural language, and determine their emotional state. The emotion engine utilizes natural language processing technology to identify the user's stress level and emotional response. 【0730】 Schedule and notification generation 【0731】 Next, the server combines the analysis results and sentiment assessment to automatically generate the most effective consultation schedule and reminders. The generated notifications are adjusted based on the user's emotional state and sent to the healthcare professional's terminal. 【0732】 Receiving notifications and taking action 【0733】 The device presents the user with generated reminders and follow-up suggestions. The notification content is adjusted according to the user's emotions, ensuring that information is delivered in the appropriate tone and at the right time. 【0734】 Follow-up proposal 【0735】 Based on the analysis, the server suggests more frequent follow-ups if the patient's symptoms may be worsening or their emotional state is unstable. This allows healthcare professionals to better understand the patient's overall health status. 【0736】 Specific example 【0737】 For example, if patient B logs into the medical system and enters a comment about their condition, the server's emotion engine analyzes the text. If the analysis determines that patient B is experiencing stress, the server takes this information into consideration and sets an earlier-than-usual appointment reminder. This earlier reminder is notified to the doctor's terminal, and follow-up care, including emotional support for patient B, is suggested. 【0738】 This system allows healthcare professionals to visualize patient-centered care, further improving the quality of patient care. 【0739】 The following describes the processing flow. 【0740】 Step 1: 【0741】 The server connects to the healthcare institution's database to collect patient medical history, medication data, and schedule information. This information is encrypted and securely stored in the database. 【0742】 Step 2: 【0743】 The server processes the collected data using a generating AI for analysis. Here, the AI ​​uses machine learning algorithms based on past treatment patterns to optimize the treatment schedule. 【0744】 Step 3: 【0745】 The server automatically generates a medical schedule to determine the optimal next appointment date and follow-up period for each patient, based on the data analyzed by the generating AI. 【0746】 Step 4: 【0747】 The server sends user-entered comments and feedback to the emotion engine, which uses natural language processing technology to determine the emotional state. The emotion engine identifies stress levels and emotional changes. 【0748】 Step 5: 【0749】 The server integrates the analysis results from the emotion engine with the medical schedule and adjusts the notification content to match the user's emotional state. This creates an emotionally sensitive reminder. 【0750】 Step 6: 【0751】 The server sends coordinated reminders in real time to the devices used by healthcare professionals. Notifications appear on the devices as pop-ups or emails. 【0752】 Step 7: 【0753】 Users review emotionally sensitive reminders and follow-up suggestions displayed on their devices and take appropriate action. User feedback helps to further improve the system. 【0754】 (Example 2) 【0755】 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". 【0756】 Patient management in healthcare settings involves many time-consuming tasks, such as managing medical history, coordinating schedules, and planning follow-up appointments. Furthermore, appropriately understanding and reflecting patients' emotional states in their treatment is a significant burden for healthcare professionals. To address these challenges, there is a need for methods to automate patient emotional management and the creation of efficient treatment schedules. 【0757】 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. 【0758】 In this invention, the server includes means for collecting patient information from a storage medium, data analysis means using generative AI to analyze medical history based on the collected patient information, and means for evaluating comments entered by the user in natural language and determining the emotional state. This enables more efficient patient management and the provision of high-quality medical care that takes emotional states into consideration. 【0759】 "Patient information" refers to all data related to a patient, such as medical history, medication data, and schedule information. 【0760】 "Storage medium" refers to means of storing information, including databases and other data storage systems. 【0761】 "Generative AI" refers to artificial intelligence technologies used to analyze collected data and predict or generate specific results. 【0762】 "Data analysis methods" refer to the methods and techniques used to process and analyze collected data to derive insights and conclusions. 【0763】 "Clinic schedule" refers to the patient's appointment times and follow-up schedule. 【0764】 "Automatic generation" refers to the creation of information and schedules by artificial intelligence or algorithms without manual intervention. 【0765】 "Natural language" refers to the words and sentences that humans use in everyday life, and also to the technology that allows computers to understand or process them. 【0766】 "Emotional state" refers to the psychological or emotional reactions or conditions exhibited by the user, and this means determining these states through text analysis or other methods. 【0767】 "Notifications" refer to messages or alerts used to convey specific information to users or healthcare professionals. 【0768】 "Providing in real time" means that some kind of information or result is processed immediately and provided in an available state. 【0769】 The embodiments for carrying out the present invention will now be described. The present invention is a system for streamlining patient management in medical institutions, and aims to reduce the burden on healthcare professionals and improve the quality of patient care by comprehensively considering even the emotional state of patients. 【0770】 This system first has a server access the healthcare institution's database to collect patient information. This patient information includes medical history, medication data, and schedule information. The data is encrypted using technologies such as SSL / TLS and stored securely. 【0771】 The server then analyzes the collected patient information using a generative AI model. This model utilizes machine learning libraries such as Scikit-learn to predict treatment schedules and follow-up timings based on historical data. 【0772】 Furthermore, the server utilizes a natural language processing-based emotion engine to analyze natural language comments collected from users. The emotion engine uses a framework like TensorFlow to classify emotional states and determine the patient's stress level. 【0773】 Based on the analysis results, the server generates a medical schedule and notifications. These generated schedules and notifications are then adjusted using a Python script according to the patient's emotional state. The adjusted notifications are then sent to the healthcare professional's terminal. 【0774】 For example, if a patient enters a comment into the system saying, "I haven't been able to sleep well lately and I'm feeling a bit tired," the server's emotion engine analyzes the text and determines that the patient is experiencing stress. Based on this, an earlier-than-usual appointment reminder is set and notified to healthcare professionals. 【0775】 An example of a prompt to be input into the generating AI model is, "Based on past medical history, please suggest the optimal date for your next appointment." 【0776】 In this way, the system can provide medical management that takes the patient's emotional state into consideration. 【0777】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0778】 Step 1: 【0779】 The server accesses the healthcare institution's database to collect patient information such as medical history, medication data, and schedule information. Input requires a database query, which sends encrypted data to the server. Specifically, the server periodically accesses the database to retrieve the latest patient information. 【0780】 Step 2: 【0781】 The server passes the collected patient information to a generating AI model for data analysis. The patient's medical history is sent to the AI ​​model as input. Using the Scikit-learn library, the AI ​​predicts the medical schedule and outputs the optimal interval between appointments. Specifically, the AI ​​applies machine learning algorithms based on past data. 【0782】 Step 3: 【0783】 The server passes comments entered by users in natural language to an emotion engine for analysis. Text data is required as input. TensorFlow is used to determine the emotional state, and emotional information such as stress levels is obtained as output. Specifically, the user's comments are analyzed, and their emotional response is quantified. 【0784】 Step 4: 【0785】 The server generates treatment schedules and reminders by combining data analysis results and emotion assessments. The input requires AI analysis results and emotion assessments. A Python script automatically creates reminders based on this information. Specifically, the generated schedule is adjusted to reflect the patient's emotional state. 【0786】 Step 5: 【0787】 The server sends the generated reminders and follow-up suggestions to the healthcare professional's device. The input requires coordinated reminder information. The output is that the device receives the notification and provides the information to the user. Specifically, the server delivers reminders via Secure Email or a dedicated app. 【0788】 (Application Example 2) 【0789】 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". 【0790】 Providing appropriate care based on residents' emotions and health conditions in nursing care facilities is a significant burden for care staff. There is a need to alleviate this burden while improving the quality of care for residents. Traditional systems have had the problem of making it difficult to provide individualized care that is sensitive to residents' emotions, often resulting in uniform care being provided. 【0791】 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. 【0792】 In this invention, the server includes means for collecting user information from an information management system, means for analyzing information using a generative AI that analyzes health records based on the collected user information, and means including an emotion analysis engine that analyzes the user's emotional state. This makes it possible to propose appropriate care according to the user's individual emotional state and health condition. 【0793】 "User information" refers to data about residents of care facilities, including health records and emotional status. 【0794】 An "information management system" refers to a system that centrally manages user information and efficiently collects and provides it. 【0795】 "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze data and generate optimal care schedules. 【0796】 "Information analysis methods" refer to the process of analyzing health records and care requirements based on collected user information. 【0797】 An "emotion analysis engine" refers to a system that uses natural language processing technology to evaluate a user's emotional state and determine the need for care. 【0798】 "Notification methods" refer to means of informing care staff of automatically generated schedules and care suggestions. 【0799】 "Care proposal" refers to the process of suggesting appropriate responses and follow-ups based on the user's health and emotional state. 【0800】 The system implementing this invention is operated primarily using a server and terminals. The server collects user information from an information management system and acquires health records and emotional states. Next, it uses a generative AI model to analyze this data and automatically generate a care schedule tailored to each user's health state and emotional state. 【0801】 The server also uses an emotion analysis engine to analyze natural language data entered by the user and evaluate the user's emotional state. This involves using natural language processing techniques, such as combining space keys and morphological analysis libraries. 【0802】 The generated schedules and care suggestions are sent to the terminal in real time. The terminal notifies care staff of necessary care information. This notification method includes alerts and pop-up messages to enable care staff to respond quickly. 【0803】 For example, if a user enters a comment into the app saying, "I've been having trouble sleeping lately and I'm feeling anxious," the server analyzes the text, and the emotion analysis engine detects the user's stress level. Based on the results, the server notifies the user's device with a "suggestion for relaxation time." 【0804】 An example of a prompt message is, "Please enter a comment from the resident. We will analyze their emotions and provide appropriate care suggestions." This allows the system to efficiently analyze user input and propose the optimal care plan to the staff. 【0805】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0806】 Step 1: 【0807】 The server collects user information from the information management system. The input consists of health records and emotional state-related data stored in the facility's database. This data is extracted using a secure protocol, encrypted, and sent to the server's analysis system. 【0808】 Step 2: 【0809】 The server analyzes user information collected using a generative AI model. The input is encrypted data obtained in step 1, which is decrypted, and machine learning algorithms are used to analyze the user's past health status and emotional patterns. Based on this analysis, a personalized care schedule is automatically generated. 【0810】 Step 3: 【0811】 The server receives natural language comments entered by the user on their device. User input is direct natural language data, which is sent to the sentiment analysis engine. The server uses natural language processing to analyze the user's emotional state from the text data and determine their stress level and emotional tendencies. 【0812】 Step 4: 【0813】 The server constructs appropriate care suggestions based on the generated care schedule and emotion analysis results. The input is the results from steps 2 and 3, and this information is combined to determine the care plan and the need for follow-up. The server then prepares to send the suggestions to the care staff. 【0814】 Step 5: 【0815】 The terminal receives care suggestions sent from the server. Input is notification data from the server. The terminal provides real-time information to care staff using pop-up alerts and message notifications. This is a crucial step in facilitating prompt responses tailored to the care situation. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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." 【0825】 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. 【0826】 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. 【0827】 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. 【0828】 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. 【0829】 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. 【0830】 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. 【0831】 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. 【0832】 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. 【0833】 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. 【0834】 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. 【0835】 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. 【0836】 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. 【0837】 The following is further disclosed regarding the embodiments described above. 【0838】 (Claim 1) 【0839】 A means of collecting patient information from a database, 【0840】 A data analysis method using generative AI to analyze medical history based on collected patient information, 【0841】 A means for automatically generating a patient's medical treatment schedule based on the analysis results, 【0842】 A means of sending notifications to healthcare workers' terminals according to an automatically generated schedule, 【0843】 A means of evaluating and proposing the need for follow-up, 【0844】 A system that includes this. 【0845】 (Claim 2) 【0846】 The system according to claim 1, wherein the generating AI uses a machine learning algorithm. 【0847】 (Claim 3) 【0848】 The system according to claim 1, wherein the notification means provides reminders in real time. 【0849】 "Example 1" 【0850】 (Claim 1) 【0851】 A means of securely collecting and storing individual data from medical institutions, 【0852】 An information processing method using generative AI that analyzes history based on collected individual data, 【0853】 A means of automatically generating a schedule based on the results of information analysis, 【0854】 A means of sending a notification to a communication device using the generated schedule, 【0855】 A means of continuously evaluating individual conditions and making suggestions, 【0856】 A system that includes this. 【0857】 (Claim 2) 【0858】 The system according to claim 1, wherein the generating AI uses a specific algorithm. 【0859】 (Claim 3) 【0860】 The system according to claim 1, wherein the notification function provides a reminder immediately. 【0861】 "Application Example 1" 【0862】 (Claim 1) 【0863】 A means of collecting patient information from a database, 【0864】 A data analysis method using generative AI to analyze medical history based on collected patient information, 【0865】 A means for automatically generating a patient's medical treatment schedule based on the analysis results, 【0866】 A means of sending notifications to communication devices according to an automatically generated schedule, 【0867】 A means of evaluating and proposing the need for follow-up, 【0868】 A means of monitoring the patient's health status using a sensing device and sharing the data with medical institutions as needed, 【0869】 Means of presenting this information to patients and caregivers visually and audibly, 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, wherein the generating AI uses a machine learning algorithm. 【0873】 (Claim 3) 【0874】 The system according to claim 1, wherein the notification means provides reminders in real time. 【0875】 "Example 2 of combining an emotion engine" 【0876】 (Claim 1) 【0877】 A means of collecting patient information from a storage medium, 【0878】 A data analysis method using generative AI to analyze medical history based on collected patient information, 【0879】 A means for automatically generating a patient's medical treatment schedule based on the analysis results, 【0880】 A means of evaluating comments entered by users in natural language and determining their emotional state, 【0881】 A means of sending notifications according to an automatically generated schedule that takes into account emotional state, 【0882】 A means of evaluating and proposing the need for follow-up, 【0883】 A system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, wherein the generative AI uses machine learning techniques for data analysis. 【0886】 (Claim 3) 【0887】 The system according to claim 1, wherein the notification means adjusts and provides reminders in real time based on emotional state. 【0888】 "Application example 2 when combining with an emotional engine" 【0889】 (Claim 1) 【0890】 Means of collecting user information from an information management system, 【0891】 An information analysis method using generative AI that analyzes health records based on collected user information, 【0892】 A means for automatically generating a user's care schedule based on the analysis results, 【0893】 A means for sending notifications to the worker's information processing device according to an automatically generated schedule, 【0894】 A means of evaluating and proposing the need for follow-up, 【0895】 A means including an emotion analysis engine for analyzing the emotional state of the user, 【0896】 A means of providing care suggestions tailored to emotional states, 【0897】 A system that includes this. 【0898】 (Claim 2) 【0899】 The system according to claim 1, wherein the generating AI uses a machine learning algorithm. 【0900】 (Claim 3) 【0901】 The system according to claim 1, wherein the notification means provides information in real time to a device that holds a memory. [Explanation of Symbols] 【0902】 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

[Claim 1] A means of collecting patient information from a database, A data analysis method using generative AI to analyze medical history based on collected patient information, A means for automatically generating a patient's medical treatment schedule based on the analysis results, A means of sending notifications to healthcare workers' terminals according to an automatically generated schedule, A means of evaluating and proposing the need for follow-up, A system that includes this. [Claim 2] The system according to claim 1, wherein the generating AI uses a machine learning algorithm. [Claim 3] The system according to claim 1, wherein the notification means provides reminders in real time.

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