System

Abstract

A system is provided.SOLUTION: The system includes a database means for storing the medical history and prescription information of patients, a receiving means for receiving the medical history and prescription information of patients and storing them in the database means, a AI model means for proposing the optimal medicine on the basis of the stored medical history and prescription information, and a display means for displaying the proposed medicine information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's medical field, there is a chronic shortage of pharmacists, and the excessive workload of pharmacists increases the risk of dispensing errors. It is also not easy to accurately understand each patient's medical history and current prescription information and provide the most appropriate medication. Furthermore, with the spread of online and home medical care, 24-hour support is required. However, solving all of these issues manually is difficult, and the situation is heavily dependent on the knowledge and experience of pharmacists. Therefore, it is necessary to resolve these issues and provide high-quality medical services while reducing the workload of pharmacists. [Means for solving the problem]

[0005] According to the present invention, a system is provided that includes a database means for storing patient medical history and prescription information, a receiving means for receiving patient medical history and prescription information and storing it in the database means, an AI model means for proposing optimal medications based on the stored medical history and prescription information, and a display means for displaying the proposed medication information. This system reduces dispensing errors without relying on the pharmacist's knowledge or experience and enables optimal medication suggestions based on the latest pharmaceutical information. Furthermore, the system includes an additional receiving means for receiving medical data entered by patients or doctors through online or home medical consultations and storing it in the database means, enabling integration with online or home medical consultations and realizing 24-hour support. Furthermore, the system includes an inquiry receiving means for accepting inquiries from patients or pharmacists and a support means for providing automated or manual support for received inquiries, enabling prompt and accurate support.

[0006] "Database Means" means a recording device that stores patient medical history and prescription information and allows the information to be retrieved as needed.

[0007] The "receiving means" is a communication device for acquiring patient medical history and prescription information and storing it in the database means.

[0008] The "AI model means" is an artificial intelligence algorithm that suggests optimal medications based on stored medical history and prescription information.

[0009] The "display means" is a display device that visually displays the suggested drug information to the user.

[0010] The "additional receiving means" is a communication device for receiving data on online medical treatment or home medical treatment and storing it in the database means.

[0011] The "inquiry receiving means" is a communication device for receiving inquiries from patients or pharmacists.

[0012] A "support means" is a system for providing automated responses or human support to received inquiries. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention provides a system for pharmacists to provide optimal medications to patients. This system includes a database means, a receiving means, an AI model means, and a display means. It may further include an additional receiving means, an inquiry receiving means, and a support means as needed. The following describes in detail the embodiment of each element of the system and the processing of the program.

[0035] System Configuration

[0036] Database means: This is a database management system for persistently storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[0037] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[0038] AI model means: Based on stored medical history and prescription information, it recommends the most appropriate medication, its dosage, and administration method. The AI ​​model uses machine learning algorithms to analyze multiple parameters.

[0039] Display: The suggestions generated by the AI ​​model are displayed on the device screen. This interface is designed to be intuitive and easy to understand, making it easy for pharmacists to use.

[0040] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[0041] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[0042] Program processing

[0043] 1. User Registration and Authentication

[0044] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[0045] Server: The received user information is saved in the database and the input information is validated. If authentication is required, authentication is performed using the user name and password, and if authentication is successful, a session ID is generated.

[0046] User: Once a user has successfully registered and authenticated, they can access various services.

[0047] 2. Enter patient information

[0048] Terminal: Pharmacists input and submit patient medical history and prescription information through an intuitive user interface.

[0049] Server: Stores the received patient information in a database, and simultaneously performs data preprocessing (format normalization, etc.).

[0050] 3. Prescription suggestions based on AI models

[0051] Server: Sends pre-processed patient information to the AI ​​model, which analyzes the received data and suggests the optimal medication, dosage, and dosing schedule.

[0052] Terminal: Suggested medication information is displayed. Pharmacists use this information to provide the most appropriate medication for the patient.

[0053] 4. Collaboration between online and home medical care

[0054] Terminal: Receives medical data entered online or through home medical care.

[0055] Server: Stores medical data in a database and sends it to the AI ​​model as needed to generate new suggestions.

[0056] Terminal: Displays medical results and new suggestions.

[0057] 5. 24-hour support

[0058] Terminal: Provides an inquiry form for patients or pharmacists and accepts submitted inquiries.

[0059] Server: Stores the inquiry and responds via an automated response system or support staff.

[0060] User: Check the support details and take any necessary action.

[0061] Specific examples

[0062] Example 1: Prescription optimization for diabetic patients

[0063] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[0064] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model.

[0065] AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[0066] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[0067] Example 2: Online medical consultation from a remote location

[0068] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[0069] Server: Receives medical data and stores it in a database.

[0070] AI model method: Recommend the most appropriate antibiotic based on clinical data.

[0071] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[0072] By implementing such a system and program, it is possible to reduce the workload of pharmacists, reduce dispensing errors, and provide high-quality medical services. The system of the present invention supports the provision of optimal medications according to individual patient needs, thereby helping to improve the quality of medical services and ensure safety.

[0073] The processing flow will be explained below.

[0074] Program processing steps

[0075] User Registration and Authentication

[0076] Step 1:

[0077] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[0078] Step 2:

[0079] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[0080] Step 3:

[0081] Terminal: Display a registration completion message to the user.

[0082] Step 4:

[0083] Device: The user enters their email address and password on the login screen and presses the login button.

[0084] Step 5:

[0085] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[0086] Step 6:

[0087] Terminal: Displays a login success message and redirects the user to the dashboard.

[0088] Entering patient information

[0089] Step 1:

[0090] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[0091] Step 2:

[0092] Server: Receives input patient information and stores it in a database means.

[0093] Step 3:

[0094] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[0095] Prescription suggestions based on AI models

[0096] Step 1:

[0097] Server: Sends pre-processed patient information to the AI ​​model means.

[0098] Step 2:

[0099] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing of administration.

[0100] Step 3:

[0101] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[0102] Step 4:

[0103] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[0104] Step 5:

[0105] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[0106] Collaboration between online and home medical care

[0107] Step 1:

[0108] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[0109] Step 2:

[0110] Server: Receives medical data and prescription information and stores them in a database.

[0111] Step 3:

[0112] Server: Sends the stored data to the AI ​​model means and generates new prescription suggestions.

[0113] Step 4:

[0114] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[0115] Step 5:

[0116] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[0117] 24-hour support

[0118] Step 1:

[0119] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[0120] Step 2:

[0121] Server: Receives the inquiry and stores it in a database.

[0122] Step 3:

[0123] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[0124] Step 4:

[0125] Terminal: The user sees an automated response or a response from the support staff.

[0126] Step 5:

[0127] User: Review the support provided and determine the next action required.

[0128] This system will not only help provide the most appropriate medication based on the patient's medical history and prescription, making pharmacists' work more efficient, but also provide reliable medical services through 24-hour support.

[0129] Example 1

[0130] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0131] Conventional medication management systems require manual input and management of patient medical history and prescription information, which can easily lead to input errors and inconsistencies, making it difficult to recommend optimal medications. Furthermore, data integration with online and home medical consultations was insufficient, resulting in delayed responses to patients in remote locations. Responses to inquiries from patients and pharmacists also lacked real-time response capabilities, making it difficult to provide satisfactory support. There is a need for a system that can solve these issues.

[0132] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0133] In this invention, the server includes a user management unit, a database unit, a receiving unit, an AI model generation unit, and a display unit. This allows for efficient and accurate management of patient medical history and prescription information, and the AI ​​model can be used to suggest optimal medications, their dosages, and administration schedules. Furthermore, by receiving data from online and home medical consultations and incorporating it into the AI ​​model in real time, it is possible to respond quickly to patients in remote locations. Furthermore, by receiving inquiries from patients and pharmacists and providing automated or manual support, 24-hour support can be provided.

[0134] "User management means" refers to the means for inputting, receiving, validating, and storing registration information and authentication information for new and existing users.

[0135] "Database Means" means a database management system for storing patient medical history and prescription information.

[0136] The "receiving means" is a means for receiving inputted patient medical history and prescription information in real time and storing it in the database means.

[0137] The "generative AI model means" is an artificial intelligence model that uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on stored medical history and prescription information.

[0138] The "display means" is an interface for displaying the suggested drug information on the user terminal.

[0139] The "additional receiving means" is a means for receiving medical data entered by a patient or a medical provider through online medical consultation or home medical consultation, storing it in the database means, and transmitting it to the generating AI model means as needed.

[0140] The "inquiry receiving means" is a means for receiving inquiries from patients or pharmacists.

[0141] "Support means" refers to means for providing automatic responses or manual support to received inquiries.

[0142] The present invention provides a system used by pharmacists to provide optimal medicines to patients, which includes a user management means, a database means, a receiving means, a generating AI model means, a display means, and optionally an additional receiving means, an inquiry receiving means, and a support means.

[0143] System configuration

[0144] 1. User Management Methods

[0145] Terminal: A new user (pharmacist or patient) enters information such as name, email address, and password into a registration form within the application.

[0146] Server: The received user information is saved in the database and validated. If authentication is required, the username and password are hashed and saved in the database.

[0147] 2. Database Means

[0148] Server: A database management system is used to permanently store patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[0149] 3. Receiving Method

[0150] Terminal: The pharmacist inputs the patient's medical history and prescription information and sends it to the server. This operation is performed using an intuitive user interface.

[0151] Server: Stores the received patient information in a database and performs data preprocessing (e.g., normalization of data format).

[0152] 4. Generative AI Model Means

[0153] Server: Sends preprocessed patient information to the generative AI model, which uses machine learning frameworks such as TensorFlow or PyTorch to analyze multiple parameters and propose optimal medications, dosages, and dosing schedules.

[0154] 5. Display means

[0155] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the most appropriate medication based on this suggestion.

[0156] 6. Additional Receiving Methods (Optional)

[0157] Terminal: Patients or healthcare providers enter medical data through online or home consultations.

[0158] Server: Receives medical data in real time, stores it in a database, and sends it to the generative AI model as needed.

[0159] 7. Inquiry and support methods (optional)

[0160] Terminal: The patient or pharmacist enters a question through an inquiry form and sends it to the server.

[0161] Server: Receives the inquiry and stores it in a database. An automated response system or support staff analyzes the inquiry and responds.

[0162] Specific examples

[0163] Example 1: Prescription optimization for diabetic patients

[0164] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[0165] Server: Receives medical history and prescription information and stores it in a database.

[0166] Generative AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[0167] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[0168] Example 2: Online medical consultation from a remote location

[0169] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[0170] Server: Receives medical data and stores it in a database.

[0171] Generative AI model method: Recommends the most appropriate antibiotic based on clinical data.

[0172] Terminal: The proposal is displayed on the doctor's or patient's screen, and the healthcare provider makes the final prescription decision.

[0173] Prompt Sentence Examples

[0174] "The AI ​​model suggests optimal insulin dosages based on a diabetic patient's blood glucose history and dietary information."

[0175] By combining these components and processing procedures, the system ensures efficient and accurate medication provision for both pharmacists and patients. It also quickly updates data from online and home consultations, enabling appropriate medical services to be provided to patients in remote locations. Furthermore, a 24-hour support function resolves user questions and concerns, providing highly reliable medical services.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] Enter user registration information

[0179] Terminal: A new user fills in the registration form within the application with the required information, such as name, email address, and password. Once the information is complete, they click the "Register" button to send the information to the server. The input for this step is the user's basic information, and the output is sending the registration information to the server.

[0180] Step 2:

[0181] Storing and validating user information

[0182] Server: The received user information is saved in the database. Furthermore, the input information must be validated to check that the information is in the correct format and that there is no invalid or duplicate information. If necessary, a confirmation email is sent. The input for this step is the user information, and the output is the saved user information and the validation results.

[0183] Step 3:

[0184] Logging in and generating a session ID

[0185] User: An existing user enters their username and password into the login form and clicks the "Login" button.

[0186] Server: Checks the username and password, and if authentication is successful, generates a session ID and returns it to the user. The input to this step is the user's authentication information, and the output is a session ID.

[0187] Step 4:

[0188] Entering patient information

[0189] Terminal: The pharmacist enters the patient's basic information (name, age, sex, etc.), medical history, allergy information, medical history, etc. New prescription information (drug name, dosage, administration method) is also entered and sent. The input for this step is the patient's detailed information, and the output is sending the information to the server.

[0190] Step 5:

[0191] Patient information storage and preprocessing

[0192] Server: Stores the received patient information in a database and performs preprocessing, which includes normalizing the data format, filling in missing data, and checking for inconsistencies. The input of this step is the received patient information, and the output is the normalized patient information.

[0193] Step 6:

[0194] Prescription suggestions based on AI models

[0195] Server: Sends preprocessed patient information to the generative AI model. The generative AI model uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on the input data. The input is normalized patient information, and the output is suggested medication information.

[0196] Step 7:

[0197] Displaying suggested drug information

[0198] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the optimal medication based on this suggestion. The input to this step is the suggested information from the generative AI model, and the output is the information displayed to the user.

[0199] Step 8:

[0200] Receiving and storing online medical data (optional)

[0201] Device: Patients consult with doctors through an online medical consultation app, enter medical data, and send it.

[0202] Server: Receives medical data and stores it in a database. If necessary, it sends it to the generative AI model to generate new medication recommendations. The input for this step is the medical data, and the output is updated recommendation information.

[0203] Step 9:

[0204] Inquiry reception and support (optional)

[0205] Terminal: The patient or pharmacist enters a question into the inquiry form and submits it.

[0206] Server: Receives the inquiry and stores it in a database. An automated response system or support staff responds to the inquiry. The input to this step is the inquiry, and the output is the response.

[0207] Through these processing steps, the system will ensure efficient and accurate drug delivery, enable rapid response to patients in remote locations, and increase user confidence by providing 24 / 7 support.

[0208] (Application example 1)

[0209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] Although conventional drug recommendation systems manage patients' medical history and prescription information and recommend optimal medications, they lack the ability to manage user authentication, link with online medical consultations, and provide 24-hour support. Furthermore, it is difficult for patients to efficiently receive medical services from home, and they are also incomplete tools for pharmacists to provide optimal medications to patients.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0212] In this invention, the server includes a data management unit that stores patient medical history and prescription information, a communication unit that receives the patient's medical history and prescription information and stores it in the data management unit, a machine learning model unit that recommends optimal medications based on the stored medical history and prescription information, a display unit that displays the recommended medication information, and an authentication unit that performs user registration and manages authentication. This allows users to easily receive medical services from home, and pharmacists can efficiently recommend optimal medications for patients. Furthermore, by providing online medical consultations and 24-hour support, more advanced and comprehensive medical support can be realized.

[0213] "Data management means" refers to a database system that permanently stores medical data such as patient medical history and prescription information, and allows efficient search and reference as needed.

[0214] "Communication Means" refers to a data communication module for receiving patient history and prescription information and storing it appropriately in the Data Management Means.

[0215] A "machine learning model" is an AI algorithm that analyzes stored medical history and prescription information to suggest the most appropriate medication and its administration method.

[0216] The "display means" refers to an interface for visually displaying to the user the medication suggestions generated by the AI ​​model means.

[0217] "Authentication means" refers to an authentication system for registering users, managing authentication information, and providing secure access.

[0218] The "additional communication means" refers to a data communication module for receiving medical data entered through telemedicine or home medical care and for adding and storing the data in the data management means.

[0219] The "inquiry communication means" refers to a data communication module for accepting inquiries from patients and pharmacists.

[0220] "Support means" refers to a system for providing automated response or human support to received inquiries.

[0221] System Overview

[0222] The present invention is a system that recommends optimal medications based on a patient's medical history and prescription information. The system includes data management means, communication means, machine learning model means, display means, and authentication means. This allows users to conveniently receive medical services from home. It also provides remote medical care and 24-hour support.

[0223] Specific program description

[0224] The core of the system is the server, which processes data and performs calculations using the following means:

[0225] Data Management Measures

[0226] The server contains a database that persistently stores patient history and prescription information, often using a standard database management system such as MySQL or PostgreSQL. Each patient is assigned a unique ID, and information is stored based on that ID.

[0227] communication means

[0228] The patient's medical history and prescription information sent from the device is received by the server's communications module, which in most cases uses the HTTP / HTTPS protocol over the Internet to send and receive data, using the Python Flask or Django framework.

[0229] Machine Learning Model Means

[0230] Based on the stored medical history and prescription information, AI algorithms are used to suggest the optimal medication, dosage, and administration method. This part utilizes machine learning frameworks such as TensorFlow and PyTorch. Multiple parameters (e.g., patient age, medical history, allergy information) are analyzed to suggest the optimal medication.

[0231] Display means

[0232] The medication suggestions generated by the server are displayed to the user through a display module on the terminal. The user interface is intuitively designed using modern web frameworks such as React and Vue.js.

[0233] Authentication Method

[0234] User registration and authentication are important to provide secure access. When a user registers, the information they enter is validated and stored in a database. When authentication is required, a session ID is issued for the username and password combination.

[0235] Examples and prompts

[0236] Example: User registration

[0237] For example: "In the user registration form, I entered the following: username: 'test_user', password: 'password123', email: 'test@example.com'."

[0238] Prompt Sentence Examples

[0239] Please enter your username, password, and email address to register. If an existing username or email address is in use, an error message will appear.

[0240] In this way, by managing patients' medical history and prescription information, displaying optimal medication suggestions based on machine learning models, and integrating online medical consultation and 24-hour support functions, patients can receive medical services efficiently from home.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] A user accesses the application from a smartphone and enters new registration information (user name, password, email address). This information is the input data.

[0244] Step 2:

[0245] The terminal sends the entered user registration information to the server. The information is sent via the HTTP / HTTPS protocol by the communication means. This data becomes the input to the server.

[0246] Step 3:

[0247] The server validates the received user registration information before saving it in the data management means. Validation checks whether the username already exists and whether the email address format is correct. After verification, the user data is saved in the database. This is the data processing stage. If successful, a message indicating registration completion is output.

[0248] Step 4:

[0249] If user registration is successful, the server generates authentication information and issues a session ID. This session ID is used for subsequent authentication. The session ID is output.

[0250] Step 5:

[0251] When a user logs in, they enter authentication information (user name, password), which is the login input data.

[0252] Step 6:

[0253] The terminal sends the login information to the server. It is sent again via the communication means via the HTTP / HTTPS protocol. This data becomes the input to the server.

[0254] Step 7:

[0255] The server checks the received login information against its database to see if the hash of the entered password matches. If authentication is successful, a new session ID is issued. This is the output data, which is sent back to the user's device.

[0256] Step 8:

[0257] Once the authentication is successful, the user can use the application functions. Enter patient information (medical history, current health condition, medications being taken). This is the input data for entering patient information.

[0258] Step 9:

[0259] The terminal sends the entered patient information to the server. It is sent again via the communication means using the HTTP / HTTPS protocol. The patient information becomes the input data for the server.

[0260] Step 10:

[0261] The server stores the received patient information in a database and sends the data to the machine learning modeling means. The machine learning algorithm analyzes the data and suggests the optimal medication and its administration method. This is the data calculation step. The analysis results become the output data.

[0262] Step 11:

[0263] The server receives the proposed information from the machine learning model and sends it to the user's device via a display means. The results are displayed as output data to the user.

[0264] Step 12:

[0265] When a user uses the online medical consultation function, they consult with a medical professional through a video call or chat and input medical data, which is the input data for online medical consultation.

[0266] Step 13:

[0267] The terminal transmits the medical data to the server, and the server stores the received medical data in a database. Additional communication means are used here, and the data is stored.

[0268] Step 14:

[0269] The server retransmits the stored medical data to the machine learning model means and re-proposes the updated optimal medication and its administration method. This is the data calculation update step. The new proposal information becomes the output data.

[0270] Step 15:

[0271] The server receives the new proposal information and transmits it again to the user's terminal via the display means, where the latest medical results and proposals are displayed.

[0272] Step 16:

[0273] When a user has a question or wants to ask for advice, he or she sends the inquiry to the server through an inquiry form, which is the input data for the inquiry.

[0274] Step 17:

[0275] The server receives the inquiry sent from the terminal and provides an automated response system or human support through the support means. This is the support step, and the answer to the inquiry becomes the output data.

[0276] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0277] The present invention provides a system that enables pharmacists to provide optimal medications to patients. This system includes a database, a receiving unit, an AI model, and a display unit, and by combining it with an emotion engine, it is possible to recognize the user's emotions and provide medical suggestions and support content based on those emotions. The following describes in detail the implementation of each element of the system and the program processing.

[0278] System Configuration

[0279] Database means: A database for storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are stored.

[0280] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[0281] AI model means: Based on stored medical history and prescription information, it recommends the optimal type of medication, dosage, and timing of administration. This AI model uses machine learning algorithms to analyze multiple parameters.

[0282] Display means: The suggestions generated by the AI ​​model means are displayed on the device screen. The user interface is designed to be intuitive and easy to understand.

[0283] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[0284] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[0285] Emotion Engine: Recognizes the user's emotions in real time and adjusts medical suggestions and support content. This emotion engine uses voice and facial recognition technology to analyze the user's emotions.

[0286] Program processing

[0287] 1. User Registration and Authentication

[0288] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[0289] Server: Save the received user information in the database and validate the input information. If authentication is required, authenticate with the username and password. If authentication is successful, generate a session ID.

[0290] User: Once a user has successfully registered and authenticated, they can access various services.

[0291] 2. Enter patient information

[0292] Terminal: Pharmacists input and submit patient medical history and prescription information. The input form can be operated through an intuitive user interface.

[0293] Server: Stores the received patient information in a database and performs data preprocessing (format conversion, normalization).

[0294] 3. Emotional Recognition

[0295] Terminal: When a user inputs information, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data in real time.

[0296] Server: Stores the emotion data generated by the emotion engine in a database and sends it to the AI ​​model.

[0297] 4. Prescription suggestions based on AI models

[0298] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[0299] AI model means: Analyzes the transmitted data and suggests the optimal type of medication, dosage, and timing of administration. It can also adjust the suggestions based on the user's emotional data.

[0300] Server: Stores the generated prescription suggestions in a database and prepares them to be returned to the terminal.

[0301] Terminal: The suggested medication information is displayed on the pharmacist's screen. The pharmacist uses this information to provide the most appropriate medication for the patient.

[0302] 5. Collaboration between online and home medical care

[0303] Device: The patient or doctor enters and sends new medical data and prescription information through the online medical consultation app.

[0304] Server: Receives medical data and prescription information and stores them in a database.

[0305] AI model means: Generate new prescription suggestions based on stored data.

[0306] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[0307] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[0308] 6. 24-hour support

[0309] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[0310] Server: Receives the inquiry and stores it in a database.

[0311] Server: An automated response system responds immediately to inquiries that can be handled, and complex inquiries are forwarded to support staff.

[0312] Terminal: The user sees an automated response or a response from the support staff.

[0313] User: Review the support provided and determine the next action required.

[0314] Specific examples

[0315] Example 1: Prescription optimization for diabetic patients

[0316] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[0317] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model and emotion engine.

[0318] Emotion engine: Analyzes the patient's emotions and generates emotion data.

[0319] AI model means: Analyzes data and suggests appropriate insulin dosage and administration schedule, and suggests additional support if the patient shows emotional instability.

[0320] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[0321] Example 2: Online medical consultation from a remote location

[0322] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[0323] Server: Receives medical data and stores it in a database.

[0324] Emotion engine: Analyzes patient emotions during medical consultations and generates emotional data.

[0325] AI model method: Recommend the most suitable antibiotic based on clinical and emotional data.

[0326] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[0327] The system of the present invention, which incorporates an emotion engine, can provide more personalized medical services by recognizing patients' emotions in real time and making medical recommendations based on those emotions. This reduces the workload of pharmacists, reduces the risk of dispensing errors, and realizes high-quality medical services.

[0328] The processing flow will be explained below.

[0329] Program processing steps (system combining emotion engines)

[0330] User Registration and Authentication

[0331] Step 1:

[0332] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[0333] Step 2:

[0334] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[0335] Step 3:

[0336] Terminal: Display a registration completion message to the user.

[0337] Step 4:

[0338] Device: The user enters their email address and password on the login screen and presses the login button.

[0339] Step 5:

[0340] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[0341] Step 6:

[0342] Terminal: Displays a login success message and redirects the user to the dashboard.

[0343] Entering patient information

[0344] Step 1:

[0345] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[0346] Step 2:

[0347] Server: Receives input patient information and stores it in a database means.

[0348] Step 3:

[0349] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[0350] Emotion recognition

[0351] Step 1:

[0352] Terminal: While the user is entering information, the emotion engine analyzes the user's tone of voice and facial expressions in real time.

[0353] Step 2:

[0354] Server: Receives emotion data generated by the emotion engine and stores it in a database means.

[0355] Prescription suggestions based on AI models

[0356] Step 1:

[0357] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[0358] Step 2:

[0359] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing, taking into account the user's emotional data.

[0360] Step 3:

[0361] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[0362] Step 4:

[0363] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[0364] Step 5:

[0365] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[0366] Collaboration between online and home medical care

[0367] Step 1:

[0368] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[0369] Step 2:

[0370] Server: Receives medical data and prescription information and stores them in a database.

[0371] Step 3:

[0372] Emotion engine: Analyzes the patient's emotions (e.g., anxiety, relief) in real time during medical treatment and generates emotional data.

[0373] Step 4:

[0374] Server: Sends medical data and emotion data to the AI ​​model to generate new prescription suggestions.

[0375] Step 5:

[0376] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[0377] Step 6:

[0378] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[0379] 24-hour support

[0380] Step 1:

[0381] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[0382] Step 2:

[0383] Server: Receives the inquiry and stores it in a database.

[0384] Step 3:

[0385] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[0386] Step 4:

[0387] Terminal: The user sees an automated response or a response from the support staff.

[0388] Step 5:

[0389] User: Review the support provided and determine the next action required.

[0390] Such systems will help provide the most appropriate medication based on the patient's medical history, prescription, and emotional data, not only making pharmacists' work more efficient but also providing personalized, emotionally-driven medical services.

[0391] Example 2

[0392] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0393] While conventional systems offer medical suggestions based on a patient's medical history and prescription information, they are unable to consider the patient's feelings, making it difficult to provide personalized medical care. Furthermore, while there is a need for data integration for remote and home medical care, and for prompt responses to inquiries from patients and medical professionals, the current system is inadequate. This makes it difficult to improve patient satisfaction or reduce the workload of pharmacists.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0395] In this invention, the server includes a data storage means for storing the patient's medical history and prescription information, an information receiving means for receiving the patient's medical history and prescription information and storing it in the data storage means, an artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, an information display means for displaying the proposed medication information, and an emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. This enables personalized medical suggestions and support that take the patient's emotions into consideration, strengthens cooperation with remote medical care and home medical care, and enables quicker response to inquiries.

[0396] 1. "Data Storage Means" means a database device for storing patient medical history and prescription information.

[0397] 2. "Information receiving means" means a communication device for receiving patient medical history and prescription information and storing it in a data storage means.

[0398] 3. "Artificial intelligence model means" is a system that implements a machine learning algorithm to suggest optimal medications based on stored medical history, prescription information, and emotional data.

[0399] 4. "Information display means" means a display device or software for displaying suggested drug information in a form visible to the user.

[0400] 5. "Emotion analysis means" refers to a system that combines voice recognition and facial recognition technologies to recognize a user's emotions in real time and adjust medical suggestions and support content based on the results.

[0401] 6. "Additional information receiving means" means a communication device for receiving medical data entered by a patient or a medical professional through remote medical consultation or home medical consultation and storing the data in a data storage means.

[0402] 7. "Inquiry information receiving means" means a communication device for receiving and processing inquiries from patients or medical professionals.

[0403] 8. "Support means" means a system for providing automated responses or manual support to received inquiries.

[0404] This invention is a system for enabling pharmacists to provide patients with optimal medications, and includes the following elements: data storage means for storing the patient's medical history and prescription information, information receiving means for receiving the patient's medical history and prescription information, artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, information display means for displaying the proposed medication information, and emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. Embodiments of each of these elements are described in detail below.

[0405] The data storage means is used to store patient medical history and prescription information. Specific examples include relational database management systems (RDBMS) such as PostgreSQL and MySQL. This allows a unique ID to be assigned to each patient, and data such as medical history, allergy information, and past prescription information to be securely stored.

[0406] The information receiving means receives information entered by the patient or pharmacist using a terminal. This can be achieved by data communication via a web browser or mobile application. For example, some modules are built with React Native, and information is received in real time via the internet.

[0407] The AI ​​model is a machine learning algorithm that uses stored medical history, prescription information, and emotional data to provide the optimal medication type, dosage, and timing. It can analyze multiple parameters using TensorFlow and Google Cloud AI Platform, resulting in more accurate medication recommendations.

[0408] The information display means displays the suggested medication information on the device screen. Specifically, the user interface (UI) is built with React Native, allowing for intuitive and easy operation. This allows pharmacists to easily check the suggested medication information and provide the most appropriate medication for the patient.

[0409] Emotion analysis is a technology that recognizes a user's emotions in real time and adjusts medical suggestions and support content. For example, it uses Google Cloud Vision API and Amazon Polly, and combines voice and facial recognition technologies to analyze a user's emotions. This makes it possible to provide personalized medical suggestions that take into account the patient's emotional state.

[0410] Combining these technologies will create a system that suggests the most appropriate medication for a patient. As a concrete example, consider optimizing prescriptions for a diabetic patient. A pharmacist inputs the diabetic patient's medical history (e.g., blood glucose level history, dietary information) and new prescription (e.g., insulin), and this information is saved in a data storage means. An emotion analysis means analyzes the patient's emotions, and an artificial intelligence model means suggests the appropriate insulin dosage and administration schedule. This makes it possible to suggest the most appropriate medical treatment that takes into account the patient's emotional state.

[0411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0412] Step 1: User Registration and Authentication

[0413] Device: A new user (pharmacist or patient) uses a device (e.g., tablet or smartphone) to enter their information (name, email address, password, etc.) into the registration form within the application and submit it.

[0414] Input: User information (name, email address, password, etc.)

[0415] Server: The received user information is stored in a data storage medium (e.g., AWS RDS) and the information is validated using the Django framework. If the information is invalid or incomplete, an error message is generated and sent back to the terminal.

[0416] Input: Received user information

[0417] Data processing: Validation using the Django framework

[0418] Output: Validation result (success / failure)

[0419] Server: If validation is successful, authenticate the user, generate a session ID and save it in the Redis session store.

[0420] Output: Session ID

[0421] User: If authentication is successful, the dashboard screen will be displayed. From this screen, various services can be accessed.

[0422] Step 2: Enter patient information

[0423] Terminal: The pharmacist inputs and transmits the patient's medical history and prescription information. The patient's medical history includes past illnesses, allergies, and past prescription information.

[0424] Input: Patient medical history and prescription information

[0425] Server: Stores the received patient information in a data storage medium (e.g., PostgreSQL) and performs data preprocessing (data format conversion and normalization) using Python scripts.

[0426] Input: Received patient information

[0427] Data processing: data format conversion, normalization

[0428] Output: Preprocessed patient information

[0429] Server: Stores the preprocessed data in a database so that it can be used in the next step.

[0430] Step 3: Recognize emotions

[0431] Terminal: As the patient enters information, the emotion analysis unit analyzes their voice tone and facial expressions to generate emotion data in real time. For example, emotion analysis can be performed by combining voice recognition and facial recognition technology.

[0432] Input: User's voice and facial expressions

[0433] Server: Stores the generated emotion data in a database (e.g., MongoDB) and sends it to the AI ​​model.

[0434] Input: Generated emotion data

[0435] Data processing: Emotion recognition through voice and facial expression analysis

[0436] Output: Emotion data

[0437] Step 4: Prescription suggestions using AI models

[0438] Server: Sends preprocessed patient information and emotion data to an AI model (e.g., a TensorFlow model hosted on Google Cloud AI Platform).

[0439] Input: Preprocessed patient information, emotion data

[0440] Data Computation: Data Analysis with AI Models

[0441] AI model means: Analyzes the transmitted data and suggests the most appropriate type of medication, dosage, and timing. It also takes into account emotional data and makes suggestions based on the patient's condition.

[0442] Output: Optimal prescription suggestions

[0443] Server: Stores the generated prescription suggestions in a database and returns them to the user's terminal.

[0444] Step 5: View prescription suggestions

[0445] Terminal: The pharmacist's screen displays suggested medication information, allowing them to dispense the most appropriate medication. The user interface is designed to be intuitive and easy to operate.

[0446] Input: Prescription suggestions sent from the server

[0447] Output: Prescription suggestions displayed in the user interface

[0448] Step 6: Collaboration between online and home medical care

[0449] Device: The patient or doctor uses the telemedicine app to enter and send new medical data and prescription information.

[0450] Input: medical data, prescription information

[0451] Server: Receives medical data and stores it in a database.

[0452] Input: Received medical data

[0453] Output: Saved medical data

[0454] AI model means: Make new prescription suggestions based on received data.

[0455] Output: New recipe suggestions

[0456] Terminal: The suggestions are displayed on the doctor's or pharmacist's screen, who then decides on the final prescription and provides it to the patient.

[0457] Step 7: 24-hour support

[0458] Terminal: The patient or pharmacist enters the question into the inquiry form and submits it.

[0459] Input: Inquiry details

[0460] Server: Receives the query and stores it in a database (e.g., Elasticsearch).

[0461] Input: Received inquiry

[0462] Output: Saved inquiry details

[0463] Server: An automated response system using Dialogflow responds immediately to simple inquiries and transfers complex inquiries to support staff.

[0464] Output: Response, support ticket

[0465] Terminal: The user sees an automated response or a response from the support staff.

[0466] Input: Response from the server

[0467] Output: The response displayed in the user interface

[0468] (Application example 2)

[0469] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0470] Conventional medical suggestion systems have limitations in making suggestions based on a patient's medical history and prescription information, and do not adequately provide personalized medical services that take into account the patient's emotions and mental state. Furthermore, factory production lines are not optimized to take into account the emotional state of workers, and there is a need for methods to improve production efficiency and reduce worker stress. A system that solves these problems is needed.

[0471] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0472] In this invention, the server includes database means for storing patient medical history and prescription information, receiving means for receiving patient medical history and prescription information and saving it in the database means, AI model means for proposing optimal medications based on the saved medical history and prescription information, display means for displaying the suggested medication information, and emotion recognition means for recognizing worker emotions and optimizing production plans and lines based on that data. This enables personalized medical recommendations that take patient emotions into account, and further allows factory production lines to be optimized taking into account the emotional states of workers, thereby improving production efficiency and reducing worker stress.

[0473] "Patient medical history and prescription information" refers to the patient's medical history and drug use instructions issued by a doctor.

[0474] "Database means" refers to a system that has the function of accumulating and storing information and searching and retrieving it as needed.

[0475] The "receiving means" is a device or function that has the function of receiving data from the outside and transmitting it to the internal system.

[0476] "AI model means" is a function that uses a machine learning algorithm to realize an artificial intelligence model that analyzes input data and generates results.

[0477] "Display means" refers to a device or function for visually displaying output information from the system.

[0478] "Worker emotions" refers to the emotional state of workers engaged in their daily work, and is data that requires analysis.

[0479] "Emotion recognition means" refers to a function for analyzing and recognizing emotions using voice recognition, facial expression recognition, and other sensor technologies.

[0480] A "production plan" is a plan for setting the schedule and methods of the production process.

[0481] "Line optimization" refers to the coordination and management of a production line to maximize its efficiency.

[0482] The present invention provides a system that suggests optimal medications based on a patient's medical history and prescription information, and a system that recognizes the emotional state of workers on a factory production line and performs production planning and line optimization. The following describes in detail the implementation of each element of the system and the program processing.

[0483] System Configuration

[0484] The system includes the following means:

[0485] 1. Database Means

[0486] The server's database stores patient medical history and prescription information along with a unique ID. This database also stores past prescription information, allergy information, and medical history, and is used as input data for analysis by the AI ​​model.

[0487] 2. Receiving Method

[0488] The server receives patient medical history and prescription information over the Internet and stores it in a database means in real time.

[0489] 3. AI Model Means

[0490] This method suggests the optimal type of medication, dosage, and timing based on the patient's medical history and prescription information. It uses machine learning algorithms to analyze multiple parameters to make optimal suggestions. It also includes integration with emotion recognition methods, which adjust the suggestions based on the user's emotional data.

[0491] 4. Display means

[0492] The suggested medication information is intuitively displayed on the device's display or smartphone, allowing pharmacists to provide the most appropriate medication to patients.

[0493] 5. Emotion recognition means

[0494] The server uses cameras, voice recognition, and facial expression recognition software to analyze workers' emotions in real time, and can use this emotional data to adjust production line speeds and optimize work assignments.

[0495] Program processing explanation

[0496] Hardware

[0497] Camera: Captures worker facial expressions in real time.

[0498] Server: Stores data, analyzes it, and generates suggestions.

[0499] Device (smartphone, tablet, PC): Enters and displays information.

[0500] software

[0501] OpenCV: Image processing and face recognition.

[0502] TensorFlow (Keras): Running an emotion recognition model.

[0503] Database system (e.g., SQLite): patient and worker data storage.

[0504] Machine learning algorithms: Building and analyzing AI models.

[0505] Specific examples

[0506] 1. Optimizing prescriptions for diabetes patients

[0507] The pharmacist enters the patient's medical history and new prescription information from the terminal, which is then sent to the server and stored in a database.

[0508] The AI ​​modeling tool analyzes this data and suggests optimal insulin dosages and schedules, and if the emotion recognition tool determines that additional support is needed, it will provide more detailed support suggestions.

[0509] The suggestions are displayed on the terminal screen, and the pharmacist makes the final decision.

[0510] 2. Factory production line optimization

[0511] The emotion recognition means collects and analyzes the emotional data of workers. If the worker's stress level is high, the server adjusts and optimizes the speed of the production line.

[0512] Emotional data is analyzed at regular intervals, and production plans are revised based on the emotional state.

[0513] Prompt Sentence Examples

[0514] - "Write a program that analyzes the facial expressions of factory workers and optimizes the speed of the production line based on their emotional state."

[0515] This system will enable personalized medical recommendations that take into account the patient's emotions, and will also enable optimization of factory production lines that take into account the emotional state of workers, thereby improving production efficiency and reducing worker stress.

[0516] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0517] Step 1:

[0518] The server receives the patient's medical history and prescription information from the terminal. The data entered on the terminal (input: patient's medical history and prescription information) is sent to the server via the Internet. The server converts and normalizes the received data and stores it in a database (output: normalized patient information).

[0519] Step 2:

[0520] The server passes the stored data to an AI model means to generate optimal medication suggestions. The server retrieves patient information from a database (input: patient information from database), analyzes the data using machine learning algorithms, and suggests medication types, dosages, and timing (output: optimal medication suggestions).

[0521] Step 3:

[0522] The terminal receives the drug suggestion information from the server and presents it to the pharmacist through the display means. The terminal receives and displays the suggested drug information (input: suggested information from the server) and displays it on the screen in a format that is easy for the user to understand (output: presented drug information).

[0523] Step 4:

[0524] The emotion recognition means captures the facial expressions of workers in real time through a camera. The emotion recognition means analyzes the captured video data (input: video data from the camera) and extracts facial expression features (output: facial expression feature data).

[0525] Step 5:

[0526] The server recognizes emotions based on the facial expression feature data sent from the emotion recognition means. The server receives the feature data (input: facial expression feature data), analyzes it using an emotion recognition model such as TensorFlow, and estimates the worker's emotional state (output: emotion data).

[0527] Step 6:

[0528] The server optimizes the speed of the production line and the allocation of work based on the emotional data. The server retrieves emotional data from a database over a certain period of time (input: past emotional data) and determines the optimal production plan based on the results of an analysis of the data with the current emotional data (output: optimized production plan).

[0529] Step 7:

[0530] The terminal receives the optimization results of the production plan from the server and transmits them to the production line control device. The terminal receives the optimization data from the server (input: production plan data from the server) and sends it to the production line control device (output: instructions to the control device).

[0531] Step 8:

[0532] The production line control device adjusts the speed of the production line and the allocation of work in accordance with instructions from the terminal. The control device adjusts the speed of the production line based on the input optimization instructions (input: instructions from the terminal) to improve production efficiency (output: optimized production line).

[0533] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0534] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0535] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0536] [Second embodiment]

[0537] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0538] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0539] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0540] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0541] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0542] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0543] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0544] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0545] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0546] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0547] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0548] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0549] The present invention provides a system for pharmacists to provide optimal medications to patients. This system includes a database means, a receiving means, an AI model means, and a display means. It may further include an additional receiving means, an inquiry receiving means, and a support means as needed. The following describes in detail the embodiment of each element of the system and the processing of the program.

[0550] System Configuration

[0551] Database means: This is a database management system for persistently storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[0552] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[0553] AI model means: Based on stored medical history and prescription information, it recommends the most appropriate medication, its dosage, and administration method. The AI ​​model uses machine learning algorithms to analyze multiple parameters.

[0554] Display: The suggestions generated by the AI ​​model are displayed on the device screen. This interface is designed to be intuitive and easy to understand, making it easy for pharmacists to use.

[0555] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[0556] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[0557] Program processing

[0558] 1. User Registration and Authentication

[0559] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[0560] Server: The received user information is saved in the database and the input information is validated. If authentication is required, authentication is performed using the user name and password, and if authentication is successful, a session ID is generated.

[0561] User: Once a user has successfully registered and authenticated, they can access various services.

[0562] 2. Enter patient information

[0563] Terminal: Pharmacists input and submit patient medical history and prescription information through an intuitive user interface.

[0564] Server: Stores the received patient information in a database, and simultaneously performs data preprocessing (format normalization, etc.).

[0565] 3. Prescription suggestions based on AI models

[0566] Server: Sends pre-processed patient information to the AI ​​model, which analyzes the received data and suggests the optimal medication, dosage, and dosing schedule.

[0567] Terminal: Suggested medication information is displayed. Pharmacists use this information to provide the most appropriate medication for the patient.

[0568] 4. Collaboration between online and home medical care

[0569] Terminal: Receives medical data entered online or through home medical care.

[0570] Server: Stores medical data in a database and sends it to the AI ​​model as needed to generate new suggestions.

[0571] Terminal: Displays medical results and new suggestions.

[0572] 5. 24-hour support

[0573] Terminal: Provides an inquiry form for patients or pharmacists and accepts submitted inquiries.

[0574] Server: Stores the inquiry and responds via an automated response system or support staff.

[0575] User: Check the support details and take any necessary action.

[0576] Specific examples

[0577] Example 1: Prescription optimization for diabetic patients

[0578] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[0579] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model.

[0580] AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[0581] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[0582] Example 2: Online medical consultation from a remote location

[0583] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[0584] Server: Receives medical data and stores it in a database.

[0585] AI model method: Recommend the most appropriate antibiotic based on clinical data.

[0586] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[0587] By implementing such a system and program, it is possible to reduce the workload of pharmacists, reduce dispensing errors, and provide high-quality medical services. The system of the present invention supports the provision of optimal medications according to individual patient needs, thereby helping to improve the quality of medical services and ensure safety.

[0588] The processing flow will be explained below.

[0589] Program processing steps

[0590] User Registration and Authentication

[0591] Step 1:

[0592] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[0593] Step 2:

[0594] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[0595] Step 3:

[0596] Terminal: Display a registration completion message to the user.

[0597] Step 4:

[0598] Device: The user enters their email address and password on the login screen and presses the login button.

[0599] Step 5:

[0600] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[0601] Step 6:

[0602] Terminal: Displays a login success message and redirects the user to the dashboard.

[0603] Entering patient information

[0604] Step 1:

[0605] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[0606] Step 2:

[0607] Server: Receives input patient information and stores it in a database means.

[0608] Step 3:

[0609] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[0610] Prescription suggestions based on AI models

[0611] Step 1:

[0612] Server: Sends pre-processed patient information to the AI ​​model means.

[0613] Step 2:

[0614] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing of administration.

[0615] Step 3:

[0616] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[0617] Step 4:

[0618] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[0619] Step 5:

[0620] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[0621] Collaboration between online and home medical care

[0622] Step 1:

[0623] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[0624] Step 2:

[0625] Server: Receives medical data and prescription information and stores them in a database.

[0626] Step 3:

[0627] Server: Sends the stored data to the AI ​​model means and generates new prescription suggestions.

[0628] Step 4:

[0629] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[0630] Step 5:

[0631] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[0632] 24-hour support

[0633] Step 1:

[0634] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[0635] Step 2:

[0636] Server: Receives the inquiry and stores it in a database.

[0637] Step 3:

[0638] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[0639] Step 4:

[0640] Terminal: The user sees an automated response or a response from the support staff.

[0641] Step 5:

[0642] User: Review the support provided and determine the next action required.

[0643] This system will not only help provide the most appropriate medication based on the patient's medical history and prescription, making pharmacists' work more efficient, but also provide reliable medical services through 24-hour support.

[0644] Example 1

[0645] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0646] Conventional medication management systems require manual input and management of patient medical history and prescription information, which can easily lead to input errors and inconsistencies, making it difficult to recommend optimal medications. Furthermore, data integration with online and home medical consultations was insufficient, resulting in delayed responses to patients in remote locations. Responses to inquiries from patients and pharmacists also lacked real-time response capabilities, making it difficult to provide satisfactory support. There is a need for a system that can solve these issues.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0648] In this invention, the server includes a user management unit, a database unit, a receiving unit, an AI model generation unit, and a display unit. This allows for efficient and accurate management of patient medical history and prescription information, and the AI ​​model can be used to suggest optimal medications, their dosages, and administration schedules. Furthermore, by receiving data from online and home medical consultations and incorporating it into the AI ​​model in real time, it is possible to respond quickly to patients in remote locations. Furthermore, by receiving inquiries from patients and pharmacists and providing automated or manual support, 24-hour support can be provided.

[0649] "User management means" refers to the means for inputting, receiving, validating, and storing registration information and authentication information for new and existing users.

[0650] "Database Means" means a database management system for storing patient medical history and prescription information.

[0651] The "receiving means" is a means for receiving inputted patient medical history and prescription information in real time and storing it in the database means.

[0652] The "generative AI model means" is an artificial intelligence model that uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on stored medical history and prescription information.

[0653] The "display means" is an interface for displaying the suggested drug information on the user terminal.

[0654] The "additional receiving means" is a means for receiving medical data entered by a patient or a medical provider through online medical consultation or home medical consultation, storing it in the database means, and transmitting it to the generating AI model means as needed.

[0655] The "inquiry receiving means" is a means for receiving inquiries from patients or pharmacists.

[0656] "Support means" refers to means for providing automatic responses or manual support to received inquiries.

[0657] The present invention provides a system used by pharmacists to provide optimal medicines to patients, which includes a user management means, a database means, a receiving means, a generating AI model means, a display means, and optionally an additional receiving means, an inquiry receiving means, and a support means.

[0658] System configuration

[0659] 1. User Management Methods

[0660] Terminal: A new user (pharmacist or patient) enters information such as name, email address, and password into a registration form within the application.

[0661] Server: The received user information is saved in the database and validated. If authentication is required, the username and password are hashed and saved in the database.

[0662] 2. Database Means

[0663] Server: A database management system is used to permanently store patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[0664] 3. Receiving Method

[0665] Terminal: The pharmacist inputs the patient's medical history and prescription information and sends it to the server. This operation is performed using an intuitive user interface.

[0666] Server: Stores the received patient information in a database and performs data preprocessing (e.g., normalization of data format).

[0667] 4. Generative AI Model Means

[0668] Server: Sends preprocessed patient information to the generative AI model, which uses machine learning frameworks such as TensorFlow or PyTorch to analyze multiple parameters and propose optimal medications, dosages, and dosing schedules.

[0669] 5. Display means

[0670] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the most appropriate medication based on this suggestion.

[0671] 6. Additional Receiving Methods (Optional)

[0672] Terminal: Patients or healthcare providers enter medical data through online or home consultations.

[0673] Server: Receives medical data in real time, stores it in a database, and sends it to the generative AI model as needed.

[0674] 7. Inquiry and support methods (optional)

[0675] Terminal: The patient or pharmacist enters a question through an inquiry form and sends it to the server.

[0676] Server: Receives the inquiry and stores it in a database. An automated response system or support staff analyzes the inquiry and responds.

[0677] Specific examples

[0678] Example 1: Prescription optimization for diabetic patients

[0679] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[0680] Server: Receives medical history and prescription information and stores it in a database.

[0681] Generative AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[0682] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[0683] Example 2: Online medical consultation from a remote location

[0684] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[0685] Server: Receives medical data and stores it in a database.

[0686] Generative AI model method: Recommends the most appropriate antibiotic based on clinical data.

[0687] Terminal: The proposal is displayed on the doctor's or patient's screen, and the healthcare provider makes the final prescription decision.

[0688] Prompt Sentence Examples

[0689] "The AI ​​model suggests optimal insulin dosages based on a diabetic patient's blood glucose history and dietary information."

[0690] By combining these components and processing procedures, the system ensures efficient and accurate medication provision for both pharmacists and patients. It also quickly updates data from online and home consultations, enabling appropriate medical services to be provided to patients in remote locations. Furthermore, a 24-hour support function resolves user questions and concerns, providing highly reliable medical services.

[0691] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0692] Step 1:

[0693] Enter user registration information

[0694] Terminal: A new user fills in the registration form within the application with the required information, such as name, email address, and password. Once the information is complete, they click the "Register" button to send the information to the server. The input for this step is the user's basic information, and the output is sending the registration information to the server.

[0695] Step 2:

[0696] Storing and validating user information

[0697] Server: The received user information is saved in the database. Furthermore, the input information must be validated to check that the information is in the correct format and that there is no invalid or duplicate information. If necessary, a confirmation email is sent. The input for this step is the user information, and the output is the saved user information and the validation results.

[0698] Step 3:

[0699] Logging in and generating a session ID

[0700] User: An existing user enters their username and password into the login form and clicks the "Login" button.

[0701] Server: Checks the username and password, and if authentication is successful, generates a session ID and returns it to the user. The input to this step is the user's authentication information, and the output is a session ID.

[0702] Step 4:

[0703] Entering patient information

[0704] Terminal: The pharmacist enters the patient's basic information (name, age, sex, etc.), medical history, allergy information, medical history, etc. New prescription information (drug name, dosage, administration method) is also entered and sent. The input for this step is the patient's detailed information, and the output is sending the information to the server.

[0705] Step 5:

[0706] Patient information storage and preprocessing

[0707] Server: Stores the received patient information in a database and performs preprocessing, which includes normalizing the data format, filling in missing data, and checking for inconsistencies. The input of this step is the received patient information, and the output is the normalized patient information.

[0708] Step 6:

[0709] Prescription suggestions based on AI models

[0710] Server: Sends preprocessed patient information to the generative AI model. The generative AI model uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on the input data. The input is normalized patient information, and the output is suggested medication information.

[0711] Step 7:

[0712] Displaying suggested drug information

[0713] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the optimal medication based on this suggestion. The input to this step is the suggested information from the generative AI model, and the output is the information displayed to the user.

[0714] Step 8:

[0715] Receiving and storing online medical data (optional)

[0716] Device: Patients consult with doctors through an online medical consultation app, enter medical data, and send it.

[0717] Server: Receives medical data and stores it in a database. If necessary, it sends it to the generative AI model to generate new medication recommendations. The input for this step is the medical data, and the output is updated recommendation information.

[0718] Step 9:

[0719] Inquiry reception and support (optional)

[0720] Terminal: The patient or pharmacist enters a question into the inquiry form and submits it.

[0721] Server: Receives the inquiry and stores it in a database. An automated response system or support staff responds to the inquiry. The input to this step is the inquiry, and the output is the response.

[0722] Through these processing steps, the system will ensure efficient and accurate drug delivery, enable rapid response to patients in remote locations, and increase user confidence by providing 24 / 7 support.

[0723] (Application example 1)

[0724] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0725] Although conventional drug recommendation systems manage patients' medical history and prescription information and recommend optimal medications, they lack the ability to manage user authentication, link with online medical consultations, and provide 24-hour support. Furthermore, it is difficult for patients to efficiently receive medical services from home, and they are also incomplete tools for pharmacists to provide optimal medications to patients.

[0726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0727] In this invention, the server includes a data management unit that stores patient medical history and prescription information, a communication unit that receives the patient's medical history and prescription information and stores it in the data management unit, a machine learning model unit that recommends optimal medications based on the stored medical history and prescription information, a display unit that displays the recommended medication information, and an authentication unit that performs user registration and manages authentication. This allows users to easily receive medical services from home, and pharmacists can efficiently recommend optimal medications for patients. Furthermore, by providing online medical consultations and 24-hour support, more advanced and comprehensive medical support can be realized.

[0728] "Data management means" refers to a database system that permanently stores medical data such as patient medical history and prescription information, and allows efficient search and reference as needed.

[0729] "Communication Means" refers to a data communication module for receiving patient history and prescription information and storing it appropriately in the Data Management Means.

[0730] A "machine learning model" is an AI algorithm that analyzes stored medical history and prescription information to suggest the most appropriate medication and its administration method.

[0731] The "display means" refers to an interface for visually displaying to the user the medication suggestions generated by the AI ​​model means.

[0732] "Authentication means" refers to an authentication system for registering users, managing authentication information, and providing secure access.

[0733] The "additional communication means" refers to a data communication module for receiving medical data entered through telemedicine or home medical care and for adding and storing the data in the data management means.

[0734] The "inquiry communication means" refers to a data communication module for accepting inquiries from patients and pharmacists.

[0735] "Support means" refers to a system for providing automated response or human support to received inquiries.

[0736] System Overview

[0737] The present invention is a system that recommends optimal medications based on a patient's medical history and prescription information. The system includes data management means, communication means, machine learning model means, display means, and authentication means. This allows users to conveniently receive medical services from home. It also provides remote medical care and 24-hour support.

[0738] Specific program description

[0739] The core of the system is the server, which processes data and performs calculations using the following means:

[0740] Data Management Measures

[0741] The server contains a database that persistently stores patient history and prescription information, often using a standard database management system such as MySQL or PostgreSQL. Each patient is assigned a unique ID, and information is stored based on that ID.

[0742] communication means

[0743] The patient's medical history and prescription information sent from the device is received by the server's communications module, which in most cases uses the HTTP / HTTPS protocol over the Internet to send and receive data, using the Python Flask or Django framework.

[0744] Machine Learning Model Means

[0745] Based on the stored medical history and prescription information, AI algorithms are used to suggest the optimal medication, dosage, and administration method. This part utilizes machine learning frameworks such as TensorFlow and PyTorch. Multiple parameters (e.g., patient age, medical history, allergy information) are analyzed to suggest the optimal medication.

[0746] Display means

[0747] The medication suggestions generated by the server are displayed to the user through a display module on the terminal. The user interface is intuitively designed using modern web frameworks such as React and Vue.js.

[0748] Authentication Method

[0749] User registration and authentication are important to provide secure access. When a user registers, the information they enter is validated and stored in a database. When authentication is required, a session ID is issued for the username and password combination.

[0750] Examples and prompts

[0751] Example: User registration

[0752] For example: "In the user registration form, I entered the following: username: 'test_user', password: 'password123', email: 'test@example.com'."

[0753] Prompt Sentence Examples

[0754] Please enter your username, password, and email address to register. If an existing username or email address is in use, an error message will appear.

[0755] In this way, by managing patients' medical history and prescription information, displaying optimal medication suggestions based on machine learning models, and integrating online medical consultation and 24-hour support functions, patients can receive medical services efficiently from home.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] A user accesses the application from a smartphone and enters new registration information (user name, password, email address). This information is the input data.

[0759] Step 2:

[0760] The terminal sends the entered user registration information to the server. The information is sent via the HTTP / HTTPS protocol by the communication means. This data becomes the input to the server.

[0761] Step 3:

[0762] The server validates the received user registration information before saving it in the data management means. Validation checks whether the username already exists and whether the email address format is correct. After verification, the user data is saved in the database. This is the data processing stage. If successful, a message indicating registration completion is output.

[0763] Step 4:

[0764] If user registration is successful, the server generates authentication information and issues a session ID. This session ID is used for subsequent authentication. The session ID is output.

[0765] Step 5:

[0766] When a user logs in, they enter authentication information (user name, password), which is the login input data.

[0767] Step 6:

[0768] The terminal sends the login information to the server. It is sent again via the communication means via the HTTP / HTTPS protocol. This data becomes the input to the server.

[0769] Step 7:

[0770] The server checks the received login information against its database to see if the hash of the entered password matches. If authentication is successful, a new session ID is issued. This is the output data, which is sent back to the user's device.

[0771] Step 8:

[0772] Once the authentication is successful, the user can use the application functions. Enter patient information (medical history, current health condition, medications being taken). This is the input data for entering patient information.

[0773] Step 9:

[0774] The terminal sends the entered patient information to the server. It is sent again via the communication means using the HTTP / HTTPS protocol. The patient information becomes the input data for the server.

[0775] Step 10:

[0776] The server stores the received patient information in a database and sends the data to the machine learning modeling means. The machine learning algorithm analyzes the data and suggests the optimal medication and its administration method. This is the data calculation step. The analysis results become the output data.

[0777] Step 11:

[0778] The server receives the proposed information from the machine learning model and sends it to the user's device via a display means. The results are displayed as output data to the user.

[0779] Step 12:

[0780] When a user uses the online medical consultation function, they consult with a medical professional through a video call or chat and input medical data, which is the input data for online medical consultation.

[0781] Step 13:

[0782] The terminal transmits the medical data to the server, and the server stores the received medical data in a database. Additional communication means are used here, and the data is stored.

[0783] Step 14:

[0784] The server retransmits the stored medical data to the machine learning model means and re-proposes the updated optimal medication and its administration method. This is the data calculation update step. The new proposal information becomes the output data.

[0785] Step 15:

[0786] The server receives the new proposal information and transmits it again to the user's terminal via the display means, where the latest medical results and proposals are displayed.

[0787] Step 16:

[0788] When a user has a question or wants to ask for advice, he or she sends the inquiry to the server through an inquiry form, which is the input data for the inquiry.

[0789] Step 17:

[0790] The server receives the inquiry sent from the terminal and provides an automated response system or human support through the support means. This is the support step, and the answer to the inquiry becomes the output data.

[0791] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0792] The present invention provides a system that enables pharmacists to provide optimal medications to patients. This system includes a database, a receiving unit, an AI model, and a display unit, and by combining it with an emotion engine, it is possible to recognize the user's emotions and provide medical suggestions and support content based on those emotions. The following describes in detail the implementation of each element of the system and the program processing.

[0793] System Configuration

[0794] Database means: A database for storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are stored.

[0795] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[0796] AI model means: Based on stored medical history and prescription information, it recommends the optimal type of medication, dosage, and timing of administration. This AI model uses machine learning algorithms to analyze multiple parameters.

[0797] Display means: The suggestions generated by the AI ​​model means are displayed on the device screen. The user interface is designed to be intuitive and easy to understand.

[0798] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[0799] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[0800] Emotion Engine: Recognizes the user's emotions in real time and adjusts medical suggestions and support content. This emotion engine uses voice and facial recognition technology to analyze the user's emotions.

[0801] Program processing

[0802] 1. User Registration and Authentication

[0803] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[0804] Server: Save the received user information in the database and validate the input information. If authentication is required, authenticate with the username and password. If authentication is successful, generate a session ID.

[0805] User: Once a user has successfully registered and authenticated, they can access various services.

[0806] 2. Enter patient information

[0807] Terminal: Pharmacists input and submit patient medical history and prescription information. The input form can be operated through an intuitive user interface.

[0808] Server: Stores the received patient information in a database and performs data preprocessing (format conversion, normalization).

[0809] 3. Emotional Recognition

[0810] Terminal: When a user inputs information, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data in real time.

[0811] Server: Stores the emotion data generated by the emotion engine in a database and sends it to the AI ​​model.

[0812] 4. Prescription suggestions based on AI models

[0813] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[0814] AI model means: Analyzes the transmitted data and suggests the optimal type of medication, dosage, and timing of administration. It can also adjust the suggestions based on the user's emotional data.

[0815] Server: Stores the generated prescription suggestions in a database and prepares them to be returned to the terminal.

[0816] Terminal: The suggested medication information is displayed on the pharmacist's screen. The pharmacist uses this information to provide the most appropriate medication for the patient.

[0817] 5. Collaboration between online and home medical care

[0818] Device: The patient or doctor enters and sends new medical data and prescription information through the online medical consultation app.

[0819] Server: Receives medical data and prescription information and stores them in a database.

[0820] AI model means: Generate new prescription suggestions based on stored data.

[0821] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[0822] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[0823] 6. 24-hour support

[0824] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[0825] Server: Receives the inquiry and stores it in a database.

[0826] Server: An automated response system responds immediately to inquiries that can be handled, and complex inquiries are forwarded to support staff.

[0827] Terminal: The user sees an automated response or a response from the support staff.

[0828] User: Review the support provided and determine the next action required.

[0829] Specific examples

[0830] Example 1: Prescription optimization for diabetic patients

[0831] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[0832] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model and emotion engine.

[0833] Emotion engine: Analyzes the patient's emotions and generates emotion data.

[0834] AI model means: Analyzes data and suggests appropriate insulin dosage and administration schedule, and suggests additional support if the patient shows emotional instability.

[0835] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[0836] Example 2: Online medical consultation from a remote location

[0837] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[0838] Server: Receives medical data and stores it in a database.

[0839] Emotion engine: Analyzes patient emotions during medical consultations and generates emotional data.

[0840] AI model method: Recommend the most suitable antibiotic based on clinical and emotional data.

[0841] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[0842] The system of the present invention, which incorporates an emotion engine, can provide more personalized medical services by recognizing patients' emotions in real time and making medical recommendations based on those emotions. This reduces the workload of pharmacists, reduces the risk of dispensing errors, and realizes high-quality medical services.

[0843] The processing flow will be explained below.

[0844] Program processing steps (system combining emotion engines)

[0845] User Registration and Authentication

[0846] Step 1:

[0847] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[0848] Step 2:

[0849] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[0850] Step 3:

[0851] Terminal: Display a registration completion message to the user.

[0852] Step 4:

[0853] Device: The user enters their email address and password on the login screen and presses the login button.

[0854] Step 5:

[0855] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[0856] Step 6:

[0857] Terminal: Displays a login success message and redirects the user to the dashboard.

[0858] Entering patient information

[0859] Step 1:

[0860] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[0861] Step 2:

[0862] Server: Receives input patient information and stores it in a database means.

[0863] Step 3:

[0864] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[0865] Emotion recognition

[0866] Step 1:

[0867] Terminal: While the user is entering information, the emotion engine analyzes the user's tone of voice and facial expressions in real time.

[0868] Step 2:

[0869] Server: Receives emotion data generated by the emotion engine and stores it in a database means.

[0870] Prescription suggestions based on AI models

[0871] Step 1:

[0872] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[0873] Step 2:

[0874] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing, taking into account the user's emotional data.

[0875] Step 3:

[0876] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[0877] Step 4:

[0878] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[0879] Step 5:

[0880] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[0881] Collaboration between online and home medical care

[0882] Step 1:

[0883] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[0884] Step 2:

[0885] Server: Receives medical data and prescription information and stores them in a database.

[0886] Step 3:

[0887] Emotion engine: Analyzes the patient's emotions (e.g., anxiety, relief) in real time during medical treatment and generates emotional data.

[0888] Step 4:

[0889] Server: Sends medical data and emotion data to the AI ​​model to generate new prescription suggestions.

[0890] Step 5:

[0891] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[0892] Step 6:

[0893] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[0894] 24-hour support

[0895] Step 1:

[0896] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[0897] Step 2:

[0898] Server: Receives the inquiry and stores it in a database.

[0899] Step 3:

[0900] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[0901] Step 4:

[0902] Terminal: The user sees an automated response or a response from the support staff.

[0903] Step 5:

[0904] User: Review the support provided and determine the next action required.

[0905] Such systems will help provide the most appropriate medication based on the patient's medical history, prescription, and emotional data, not only making pharmacists' work more efficient but also providing personalized, emotionally-driven medical services.

[0906] Example 2

[0907] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0908] While conventional systems offer medical suggestions based on a patient's medical history and prescription information, they are unable to consider the patient's feelings, making it difficult to provide personalized medical care. Furthermore, while there is a need for data integration for remote and home medical care, and for prompt responses to inquiries from patients and medical professionals, the current system is inadequate. This makes it difficult to improve patient satisfaction or reduce the workload of pharmacists.

[0909] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0910] In this invention, the server includes a data storage means for storing the patient's medical history and prescription information, an information receiving means for receiving the patient's medical history and prescription information and storing it in the data storage means, an artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, an information display means for displaying the proposed medication information, and an emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. This enables personalized medical suggestions and support that take the patient's emotions into consideration, strengthens cooperation with remote medical care and home medical care, and enables quicker response to inquiries.

[0911] 1. "Data Storage Means" means a database device for storing patient medical history and prescription information.

[0912] 2. "Information receiving means" means a communication device for receiving patient medical history and prescription information and storing it in a data storage means.

[0913] 3. "Artificial intelligence model means" is a system that implements a machine learning algorithm to suggest optimal medications based on stored medical history, prescription information, and emotional data.

[0914] 4. "Information display means" means a display device or software for displaying suggested drug information in a form visible to the user.

[0915] 5. "Emotion analysis means" refers to a system that combines voice recognition and facial recognition technologies to recognize a user's emotions in real time and adjust medical suggestions and support content based on the results.

[0916] 6. "Additional information receiving means" means a communication device for receiving medical data entered by a patient or a medical professional through remote medical consultation or home medical consultation and storing the data in a data storage means.

[0917] 7. "Inquiry information receiving means" means a communication device for receiving and processing inquiries from patients or medical professionals.

[0918] 8. "Support means" means a system for providing automated responses or manual support to received inquiries.

[0919] This invention is a system for enabling pharmacists to provide patients with optimal medications, and includes the following elements: data storage means for storing the patient's medical history and prescription information, information receiving means for receiving the patient's medical history and prescription information, artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, information display means for displaying the proposed medication information, and emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. Embodiments of each of these elements are described in detail below.

[0920] The data storage means is used to store patient medical history and prescription information. Specific examples include relational database management systems (RDBMS) such as PostgreSQL and MySQL. This allows a unique ID to be assigned to each patient, and data such as medical history, allergy information, and past prescription information to be securely stored.

[0921] The information receiving means receives information entered by the patient or pharmacist using a terminal. This can be achieved by data communication via a web browser or mobile application. For example, some modules are built with React Native, and information is received in real time via the internet.

[0922] The AI ​​model is a machine learning algorithm that uses stored medical history, prescription information, and emotional data to provide the optimal medication type, dosage, and timing. It can analyze multiple parameters using TensorFlow and Google Cloud AI Platform, resulting in more accurate medication recommendations.

[0923] The information display means displays the suggested medication information on the device screen. Specifically, the user interface (UI) is built with React Native, allowing for intuitive and easy operation. This allows pharmacists to easily check the suggested medication information and provide the most appropriate medication for the patient.

[0924] Emotion analysis is a technology that recognizes a user's emotions in real time and adjusts medical suggestions and support content. For example, it uses Google Cloud Vision API and Amazon Polly, and combines voice and facial recognition technologies to analyze a user's emotions. This makes it possible to provide personalized medical suggestions that take into account the patient's emotional state.

[0925] Combining these technologies will create a system that suggests the most appropriate medication for a patient. As a concrete example, consider optimizing prescriptions for a diabetic patient. A pharmacist inputs the diabetic patient's medical history (e.g., blood glucose level history, dietary information) and new prescription (e.g., insulin), and this information is saved in a data storage means. An emotion analysis means analyzes the patient's emotions, and an artificial intelligence model means suggests the appropriate insulin dosage and administration schedule. This makes it possible to suggest the most appropriate medical treatment that takes into account the patient's emotional state.

[0926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0927] Step 1: User Registration and Authentication

[0928] Device: A new user (pharmacist or patient) uses a device (e.g., tablet or smartphone) to enter their information (name, email address, password, etc.) into the registration form within the application and submit it.

[0929] Input: User information (name, email address, password, etc.)

[0930] Server: The received user information is stored in a data storage medium (e.g., AWS RDS) and the information is validated using the Django framework. If the information is invalid or incomplete, an error message is generated and sent back to the terminal.

[0931] Input: Received user information

[0932] Data processing: Validation using the Django framework

[0933] Output: Validation result (success / failure)

[0934] Server: If validation is successful, authenticate the user, generate a session ID and save it in the Redis session store.

[0935] Output: Session ID

[0936] User: If authentication is successful, the dashboard screen will be displayed. From this screen, various services can be accessed.

[0937] Step 2: Enter patient information

[0938] Terminal: The pharmacist inputs and transmits the patient's medical history and prescription information. The patient's medical history includes past illnesses, allergies, and past prescription information.

[0939] Input: Patient medical history and prescription information

[0940] Server: Stores the received patient information in a data storage medium (e.g., PostgreSQL) and performs data preprocessing (data format conversion and normalization) using Python scripts.

[0941] Input: Received patient information

[0942] Data processing: data format conversion, normalization

[0943] Output: Preprocessed patient information

[0944] Server: Stores the preprocessed data in a database so that it can be used in the next step.

[0945] Step 3: Recognize emotions

[0946] Terminal: As the patient enters information, the emotion analysis unit analyzes their voice tone and facial expressions to generate emotion data in real time. For example, emotion analysis can be performed by combining voice recognition and facial recognition technology.

[0947] Input: User's voice and facial expressions

[0948] Server: Stores the generated emotion data in a database (e.g., MongoDB) and sends it to the AI ​​model.

[0949] Input: Generated emotion data

[0950] Data processing: Emotion recognition through voice and facial expression analysis

[0951] Output: Emotion data

[0952] Step 4: Prescription suggestions using AI models

[0953] Server: Sends preprocessed patient information and emotion data to an AI model (e.g., a TensorFlow model hosted on Google Cloud AI Platform).

[0954] Input: Preprocessed patient information, emotion data

[0955] Data Computation: Data Analysis with AI Models

[0956] AI model means: Analyzes the transmitted data and suggests the most appropriate type of medication, dosage, and timing. It also takes into account emotional data and makes suggestions based on the patient's condition.

[0957] Output: Optimal prescription suggestions

[0958] Server: Stores the generated prescription suggestions in a database and returns them to the user's terminal.

[0959] Step 5: View prescription suggestions

[0960] Terminal: The pharmacist's screen displays suggested medication information, allowing them to dispense the most appropriate medication. The user interface is designed to be intuitive and easy to operate.

[0961] Input: Prescription suggestions sent from the server

[0962] Output: Prescription suggestions displayed in the user interface

[0963] Step 6: Collaboration between online and home medical care

[0964] Device: The patient or doctor uses the telemedicine app to enter and send new medical data and prescription information.

[0965] Input: medical data, prescription information

[0966] Server: Receives medical data and stores it in a database.

[0967] Input: Received medical data

[0968] Output: Saved medical data

[0969] AI model means: Make new prescription suggestions based on received data.

[0970] Output: New recipe suggestions

[0971] Terminal: The suggestions are displayed on the doctor's or pharmacist's screen, who then decides on the final prescription and provides it to the patient.

[0972] Step 7: 24-hour support

[0973] Terminal: The patient or pharmacist enters the question into the inquiry form and submits it.

[0974] Input: Inquiry details

[0975] Server: Receives the query and stores it in a database (e.g., Elasticsearch).

[0976] Input: Received inquiry

[0977] Output: Saved inquiry details

[0978] Server: An automated response system using Dialogflow responds immediately to simple inquiries and transfers complex inquiries to support staff.

[0979] Output: Response, support ticket

[0980] Terminal: The user sees an automated response or a response from the support staff.

[0981] Input: Response from the server

[0982] Output: The response displayed in the user interface

[0983] (Application example 2)

[0984] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0985] Conventional medical suggestion systems have limitations in making suggestions based on a patient's medical history and prescription information, and do not adequately provide personalized medical services that take into account the patient's emotions and mental state. Furthermore, factory production lines are not optimized to take into account the emotional state of workers, and there is a need for methods to improve production efficiency and reduce worker stress. A system that solves these problems is needed.

[0986] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0987] In this invention, the server includes database means for storing patient medical history and prescription information, receiving means for receiving patient medical history and prescription information and saving it in the database means, AI model means for proposing optimal medications based on the saved medical history and prescription information, display means for displaying the suggested medication information, and emotion recognition means for recognizing worker emotions and optimizing production plans and lines based on that data. This enables personalized medical recommendations that take patient emotions into account, and further allows factory production lines to be optimized taking into account the emotional states of workers, thereby improving production efficiency and reducing worker stress.

[0988] "Patient medical history and prescription information" refers to the patient's medical history and drug use instructions issued by a doctor.

[0989] "Database means" refers to a system that has the function of accumulating and storing information and searching and retrieving it as needed.

[0990] The "receiving means" is a device or function that has the function of receiving data from the outside and transmitting it to the internal system.

[0991] "AI model means" is a function that uses a machine learning algorithm to realize an artificial intelligence model that analyzes input data and generates results.

[0992] "Display means" refers to a device or function for visually displaying output information from the system.

[0993] "Worker emotions" refers to the emotional state of workers engaged in their daily work, and is data that requires analysis.

[0994] "Emotion recognition means" refers to a function for analyzing and recognizing emotions using voice recognition, facial expression recognition, and other sensor technologies.

[0995] A "production plan" is a plan for setting the schedule and methods of the production process.

[0996] "Line optimization" refers to the coordination and management of a production line to maximize its efficiency.

[0997] The present invention provides a system that suggests optimal medications based on a patient's medical history and prescription information, and a system that recognizes the emotional state of workers on a factory production line and performs production planning and line optimization. The following describes in detail the implementation of each element of the system and the program processing.

[0998] System Configuration

[0999] The system includes the following means:

[1000] 1. Database Means

[1001] The server's database stores patient medical history and prescription information along with a unique ID. This database also stores past prescription information, allergy information, and medical history, and is used as input data for analysis by the AI ​​model.

[1002] 2. Receiving Method

[1003] The server receives patient medical history and prescription information over the Internet and stores it in a database means in real time.

[1004] 3. AI Model Means

[1005] This method suggests the optimal type of medication, dosage, and timing based on the patient's medical history and prescription information. It uses machine learning algorithms to analyze multiple parameters to make optimal suggestions. It also includes integration with emotion recognition methods, which adjust the suggestions based on the user's emotional data.

[1006] 4. Display means

[1007] The suggested medication information is intuitively displayed on the device's display or smartphone, allowing pharmacists to provide the most appropriate medication to patients.

[1008] 5. Emotion recognition means

[1009] The server uses cameras, voice recognition, and facial expression recognition software to analyze workers' emotions in real time, and can use this emotional data to adjust production line speeds and optimize work assignments.

[1010] Program processing explanation

[1011] Hardware

[1012] Camera: Captures worker facial expressions in real time.

[1013] Server: Stores data, analyzes it, and generates suggestions.

[1014] Device (smartphone, tablet, PC): Enters and displays information.

[1015] software

[1016] OpenCV: Image processing and face recognition.

[1017] TensorFlow (Keras): Running an emotion recognition model.

[1018] Database system (e.g., SQLite): patient and worker data storage.

[1019] Machine learning algorithms: Building and analyzing AI models.

[1020] Specific examples

[1021] 1. Optimizing prescriptions for diabetes patients

[1022] The pharmacist enters the patient's medical history and new prescription information from the terminal, which is then sent to the server and stored in a database.

[1023] The AI ​​modeling tool analyzes this data and suggests optimal insulin dosages and schedules, and if the emotion recognition tool determines that additional support is needed, it will provide more detailed support suggestions.

[1024] The suggestions are displayed on the terminal screen, and the pharmacist makes the final decision.

[1025] 2. Factory production line optimization

[1026] The emotion recognition means collects and analyzes the emotional data of workers. If the worker's stress level is high, the server adjusts and optimizes the speed of the production line.

[1027] Emotional data is analyzed at regular intervals, and production plans are revised based on the emotional state.

[1028] Prompt Sentence Examples

[1029] - "Write a program that analyzes the facial expressions of factory workers and optimizes the speed of the production line based on their emotional state."

[1030] This system will enable personalized medical recommendations that take into account the patient's emotions, and will also enable optimization of factory production lines that take into account the emotional state of workers, thereby improving production efficiency and reducing worker stress.

[1031] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1032] Step 1:

[1033] The server receives the patient's medical history and prescription information from the terminal. The data entered on the terminal (input: patient's medical history and prescription information) is sent to the server via the Internet. The server converts and normalizes the received data and stores it in a database (output: normalized patient information).

[1034] Step 2:

[1035] The server passes the stored data to an AI model means to generate optimal medication suggestions. The server retrieves patient information from a database (input: patient information from database), analyzes the data using machine learning algorithms, and suggests medication types, dosages, and timing (output: optimal medication suggestions).

[1036] Step 3:

[1037] The terminal receives the drug suggestion information from the server and presents it to the pharmacist through the display means. The terminal receives and displays the suggested drug information (input: suggested information from the server) and displays it on the screen in a format that is easy for the user to understand (output: presented drug information).

[1038] Step 4:

[1039] The emotion recognition means captures the facial expressions of workers in real time through a camera. The emotion recognition means analyzes the captured video data (input: video data from the camera) and extracts facial expression features (output: facial expression feature data).

[1040] Step 5:

[1041] The server recognizes emotions based on the facial expression feature data sent from the emotion recognition means. The server receives the feature data (input: facial expression feature data), analyzes it using an emotion recognition model such as TensorFlow, and estimates the worker's emotional state (output: emotion data).

[1042] Step 6:

[1043] The server optimizes the speed of the production line and the allocation of work based on the emotional data. The server retrieves emotional data from a database over a certain period of time (input: past emotional data) and determines the optimal production plan based on the results of an analysis of the data with the current emotional data (output: optimized production plan).

[1044] Step 7:

[1045] The terminal receives the optimization results of the production plan from the server and transmits them to the production line control device. The terminal receives the optimization data from the server (input: production plan data from the server) and sends it to the production line control device (output: instructions to the control device).

[1046] Step 8:

[1047] The production line control device adjusts the speed of the production line and the allocation of work in accordance with instructions from the terminal. The control device adjusts the speed of the production line based on the input optimization instructions (input: instructions from the terminal) to improve production efficiency (output: optimized production line).

[1048] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1049] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1050] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1051] [Third embodiment]

[1052] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1053] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1054] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1055] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1056] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1057] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1058] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1059] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1060] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1061] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1062] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1063] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1064] The present invention provides a system for pharmacists to provide optimal medications to patients. This system includes a database means, a receiving means, an AI model means, and a display means. It may further include an additional receiving means, an inquiry receiving means, and a support means as needed. The following describes in detail the embodiment of each element of the system and the processing of the program.

[1065] System Configuration

[1066] Database means: This is a database management system for persistently storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[1067] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[1068] AI model means: Based on stored medical history and prescription information, it recommends the most appropriate medication, its dosage, and administration method. The AI ​​model uses machine learning algorithms to analyze multiple parameters.

[1069] Display: The suggestions generated by the AI ​​model are displayed on the device screen. This interface is designed to be intuitive and easy to understand, making it easy for pharmacists to use.

[1070] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[1071] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[1072] Program processing

[1073] 1. User Registration and Authentication

[1074] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[1075] Server: The received user information is saved in the database and the input information is validated. If authentication is required, authentication is performed using the user name and password, and if authentication is successful, a session ID is generated.

[1076] User: Once a user has successfully registered and authenticated, they can access various services.

[1077] 2. Enter patient information

[1078] Terminal: Pharmacists input and submit patient medical history and prescription information through an intuitive user interface.

[1079] Server: Stores the received patient information in a database, and simultaneously performs data preprocessing (format normalization, etc.).

[1080] 3. Prescription suggestions based on AI models

[1081] Server: Sends pre-processed patient information to the AI ​​model, which analyzes the received data and suggests the optimal medication, dosage, and dosing schedule.

[1082] Terminal: Suggested medication information is displayed. Pharmacists use this information to provide the most appropriate medication for the patient.

[1083] 4. Collaboration between online and home medical care

[1084] Terminal: Receives medical data entered online or through home medical care.

[1085] Server: Stores medical data in a database and sends it to the AI ​​model as needed to generate new suggestions.

[1086] Terminal: Displays medical results and new suggestions.

[1087] 5. 24-hour support

[1088] Terminal: Provides an inquiry form for patients or pharmacists and accepts submitted inquiries.

[1089] Server: Stores the inquiry and responds via an automated response system or support staff.

[1090] User: Check the support details and take any necessary action.

[1091] Specific examples

[1092] Example 1: Prescription optimization for diabetic patients

[1093] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[1094] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model.

[1095] AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[1096] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[1097] Example 2: Online medical consultation from a remote location

[1098] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[1099] Server: Receives medical data and stores it in a database.

[1100] AI model method: Recommend the most appropriate antibiotic based on clinical data.

[1101] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[1102] By implementing such a system and program, it is possible to reduce the workload of pharmacists, reduce dispensing errors, and provide high-quality medical services. The system of the present invention supports the provision of optimal medications according to individual patient needs, thereby helping to improve the quality of medical services and ensure safety.

[1103] The processing flow will be explained below.

[1104] Program processing steps

[1105] User Registration and Authentication

[1106] Step 1:

[1107] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[1108] Step 2:

[1109] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[1110] Step 3:

[1111] Terminal: Display a registration completion message to the user.

[1112] Step 4:

[1113] Device: The user enters their email address and password on the login screen and presses the login button.

[1114] Step 5:

[1115] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[1116] Step 6:

[1117] Terminal: Displays a login success message and redirects the user to the dashboard.

[1118] Entering patient information

[1119] Step 1:

[1120] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[1121] Step 2:

[1122] Server: Receives input patient information and stores it in a database means.

[1123] Step 3:

[1124] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[1125] Prescription suggestions based on AI models

[1126] Step 1:

[1127] Server: Sends pre-processed patient information to the AI ​​model means.

[1128] Step 2:

[1129] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing of administration.

[1130] Step 3:

[1131] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[1132] Step 4:

[1133] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[1134] Step 5:

[1135] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[1136] Collaboration between online and home medical care

[1137] Step 1:

[1138] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[1139] Step 2:

[1140] Server: Receives medical data and prescription information and stores them in a database.

[1141] Step 3:

[1142] Server: Sends the stored data to the AI ​​model means and generates new prescription suggestions.

[1143] Step 4:

[1144] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[1145] Step 5:

[1146] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[1147] 24-hour support

[1148] Step 1:

[1149] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[1150] Step 2:

[1151] Server: Receives the inquiry and stores it in a database.

[1152] Step 3:

[1153] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[1154] Step 4:

[1155] Terminal: The user sees an automated response or a response from the support staff.

[1156] Step 5:

[1157] User: Review the support provided and determine the next action required.

[1158] This system will not only help provide the most appropriate medication based on the patient's medical history and prescription, making pharmacists' work more efficient, but also provide reliable medical services through 24-hour support.

[1159] Example 1

[1160] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1161] Conventional medication management systems require manual input and management of patient medical history and prescription information, which can easily lead to input errors and inconsistencies, making it difficult to recommend optimal medications. Furthermore, data integration with online and home medical consultations was insufficient, resulting in delayed responses to patients in remote locations. Responses to inquiries from patients and pharmacists also lacked real-time response capabilities, making it difficult to provide satisfactory support. There is a need for a system that can solve these issues.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1163] In this invention, the server includes a user management unit, a database unit, a receiving unit, an AI model generation unit, and a display unit. This allows for efficient and accurate management of patient medical history and prescription information, and the AI ​​model can be used to suggest optimal medications, their dosages, and administration schedules. Furthermore, by receiving data from online and home medical consultations and incorporating it into the AI ​​model in real time, it is possible to respond quickly to patients in remote locations. Furthermore, by receiving inquiries from patients and pharmacists and providing automated or manual support, 24-hour support can be provided.

[1164] "User management means" refers to the means for inputting, receiving, validating, and storing registration information and authentication information for new and existing users.

[1165] "Database Means" means a database management system for storing patient medical history and prescription information.

[1166] The "receiving means" is a means for receiving inputted patient medical history and prescription information in real time and storing it in the database means.

[1167] The "generative AI model means" is an artificial intelligence model that uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on stored medical history and prescription information.

[1168] The "display means" is an interface for displaying the suggested drug information on the user terminal.

[1169] The "additional receiving means" is a means for receiving medical data entered by a patient or a medical provider through online medical consultation or home medical consultation, storing it in the database means, and transmitting it to the generating AI model means as needed.

[1170] The "inquiry receiving means" is a means for receiving inquiries from patients or pharmacists.

[1171] "Support means" refers to means for providing automatic responses or manual support to received inquiries.

[1172] The present invention provides a system used by pharmacists to provide optimal medicines to patients, which includes a user management means, a database means, a receiving means, a generating AI model means, a display means, and optionally an additional receiving means, an inquiry receiving means, and a support means.

[1173] System configuration

[1174] 1. User Management Methods

[1175] Terminal: A new user (pharmacist or patient) enters information such as name, email address, and password into a registration form within the application.

[1176] Server: The received user information is saved in the database and validated. If authentication is required, the username and password are hashed and saved in the database.

[1177] 2. Database Means

[1178] Server: A database management system is used to permanently store patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[1179] 3. Receiving Method

[1180] Terminal: The pharmacist inputs the patient's medical history and prescription information and sends it to the server. This operation is performed using an intuitive user interface.

[1181] Server: Stores the received patient information in a database and performs data preprocessing (e.g., normalization of data format).

[1182] 4. Generative AI Model Means

[1183] Server: Sends preprocessed patient information to the generative AI model, which uses machine learning frameworks such as TensorFlow or PyTorch to analyze multiple parameters and propose optimal medications, dosages, and dosing schedules.

[1184] 5. Display means

[1185] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the most appropriate medication based on this suggestion.

[1186] 6. Additional Receiving Methods (Optional)

[1187] Terminal: Patients or healthcare providers enter medical data through online or home consultations.

[1188] Server: Receives medical data in real time, stores it in a database, and sends it to the generative AI model as needed.

[1189] 7. Inquiry and support methods (optional)

[1190] Terminal: The patient or pharmacist enters a question through an inquiry form and sends it to the server.

[1191] Server: Receives the inquiry and stores it in a database. An automated response system or support staff analyzes the inquiry and responds.

[1192] Specific examples

[1193] Example 1: Prescription optimization for diabetic patients

[1194] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[1195] Server: Receives medical history and prescription information and stores it in a database.

[1196] Generative AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[1197] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[1198] Example 2: Online medical consultation from a remote location

[1199] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[1200] Server: Receives medical data and stores it in a database.

[1201] Generative AI model method: Recommends the most appropriate antibiotic based on clinical data.

[1202] Terminal: The proposal is displayed on the doctor's or patient's screen, and the healthcare provider makes the final prescription decision.

[1203] Prompt Sentence Examples

[1204] "The AI ​​model suggests optimal insulin dosages based on a diabetic patient's blood glucose history and dietary information."

[1205] By combining these components and processing procedures, the system ensures efficient and accurate medication provision for both pharmacists and patients. It also quickly updates data from online and home consultations, enabling appropriate medical services to be provided to patients in remote locations. Furthermore, a 24-hour support function resolves user questions and concerns, providing highly reliable medical services.

[1206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1207] Step 1:

[1208] Enter user registration information

[1209] Terminal: A new user fills in the registration form within the application with the required information, such as name, email address, and password. Once the information is complete, they click the "Register" button to send the information to the server. The input for this step is the user's basic information, and the output is sending the registration information to the server.

[1210] Step 2:

[1211] Storing and validating user information

[1212] Server: The received user information is saved in the database. Furthermore, the input information must be validated to check that the information is in the correct format and that there is no invalid or duplicate information. If necessary, a confirmation email is sent. The input for this step is the user information, and the output is the saved user information and the validation results.

[1213] Step 3:

[1214] Logging in and generating a session ID

[1215] User: An existing user enters their username and password into the login form and clicks the "Login" button.

[1216] Server: Checks the username and password, and if authentication is successful, generates a session ID and returns it to the user. The input to this step is the user's authentication information, and the output is a session ID.

[1217] Step 4:

[1218] Entering patient information

[1219] Terminal: The pharmacist enters the patient's basic information (name, age, sex, etc.), medical history, allergy information, medical history, etc. New prescription information (drug name, dosage, administration method) is also entered and sent. The input for this step is the patient's detailed information, and the output is sending the information to the server.

[1220] Step 5:

[1221] Patient information storage and preprocessing

[1222] Server: Stores the received patient information in a database and performs preprocessing, which includes normalizing the data format, filling in missing data, and checking for inconsistencies. The input of this step is the received patient information, and the output is the normalized patient information.

[1223] Step 6:

[1224] Prescription suggestions based on AI models

[1225] Server: Sends preprocessed patient information to the generative AI model. The generative AI model uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on the input data. The input is normalized patient information, and the output is suggested medication information.

[1226] Step 7:

[1227] Displaying suggested drug information

[1228] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the optimal medication based on this suggestion. The input to this step is the suggested information from the generative AI model, and the output is the information displayed to the user.

[1229] Step 8:

[1230] Receiving and storing online medical data (optional)

[1231] Device: Patients consult with doctors through an online medical consultation app, enter medical data, and send it.

[1232] Server: Receives medical data and stores it in a database. If necessary, it sends it to the generative AI model to generate new medication recommendations. The input for this step is the medical data, and the output is updated recommendation information.

[1233] Step 9:

[1234] Inquiry reception and support (optional)

[1235] Terminal: The patient or pharmacist enters a question into the inquiry form and submits it.

[1236] Server: Receives the inquiry and stores it in a database. An automated response system or support staff responds to the inquiry. The input to this step is the inquiry, and the output is the response.

[1237] Through these processing steps, the system will ensure efficient and accurate drug delivery, enable rapid response to patients in remote locations, and increase user confidence by providing 24 / 7 support.

[1238] (Application example 1)

[1239] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1240] Although conventional drug recommendation systems manage patients' medical history and prescription information and recommend optimal medications, they lack the ability to manage user authentication, link with online medical consultations, and provide 24-hour support. Furthermore, it is difficult for patients to efficiently receive medical services from home, and they are also incomplete tools for pharmacists to provide optimal medications to patients.

[1241] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1242] In this invention, the server includes a data management unit that stores patient medical history and prescription information, a communication unit that receives the patient's medical history and prescription information and stores it in the data management unit, a machine learning model unit that recommends optimal medications based on the stored medical history and prescription information, a display unit that displays the recommended medication information, and an authentication unit that performs user registration and manages authentication. This allows users to easily receive medical services from home, and pharmacists can efficiently recommend optimal medications for patients. Furthermore, by providing online medical consultations and 24-hour support, more advanced and comprehensive medical support can be realized.

[1243] "Data management means" refers to a database system that permanently stores medical data such as patient medical history and prescription information, and allows efficient search and reference as needed.

[1244] "Communication Means" refers to a data communication module for receiving patient history and prescription information and storing it appropriately in the Data Management Means.

[1245] A "machine learning model" is an AI algorithm that analyzes stored medical history and prescription information to suggest the most appropriate medication and its administration method.

[1246] The "display means" refers to an interface for visually displaying to the user the medication suggestions generated by the AI ​​model means.

[1247] "Authentication means" refers to an authentication system for registering users, managing authentication information, and providing secure access.

[1248] The "additional communication means" refers to a data communication module for receiving medical data entered through telemedicine or home medical care and for adding and storing the data in the data management means.

[1249] The "inquiry communication means" refers to a data communication module for accepting inquiries from patients and pharmacists.

[1250] "Support means" refers to a system for providing automated response or human support to received inquiries.

[1251] System Overview

[1252] The present invention is a system that recommends optimal medications based on a patient's medical history and prescription information. The system includes data management means, communication means, machine learning model means, display means, and authentication means. This allows users to conveniently receive medical services from home. It also provides remote medical care and 24-hour support.

[1253] Specific program description

[1254] The core of the system is the server, which processes data and performs calculations using the following means:

[1255] Data Management Measures

[1256] The server contains a database that persistently stores patient history and prescription information, often using a standard database management system such as MySQL or PostgreSQL. Each patient is assigned a unique ID, and information is stored based on that ID.

[1257] communication means

[1258] The patient's medical history and prescription information sent from the device is received by the server's communications module, which in most cases uses the HTTP / HTTPS protocol over the Internet to send and receive data, using the Python Flask or Django framework.

[1259] Machine Learning Model Means

[1260] Based on the stored medical history and prescription information, AI algorithms are used to suggest the optimal medication, dosage, and administration method. This part utilizes machine learning frameworks such as TensorFlow and PyTorch. Multiple parameters (e.g., patient age, medical history, allergy information) are analyzed to suggest the optimal medication.

[1261] Display means

[1262] The medication suggestions generated by the server are displayed to the user through a display module on the terminal. The user interface is intuitively designed using modern web frameworks such as React and Vue.js.

[1263] Authentication Method

[1264] User registration and authentication are important to provide secure access. When a user registers, the information they enter is validated and stored in a database. When authentication is required, a session ID is issued for the username and password combination.

[1265] Examples and prompts

[1266] Example: User registration

[1267] For example: "In the user registration form, I entered the following: username: 'test_user', password: 'password123', email: 'test@example.com'."

[1268] Prompt Sentence Examples

[1269] Please enter your username, password, and email address to register. If an existing username or email address is in use, an error message will appear.

[1270] In this way, by managing patients' medical history and prescription information, displaying optimal medication suggestions based on machine learning models, and integrating online medical consultation and 24-hour support functions, patients can receive medical services efficiently from home.

[1271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1272] Step 1:

[1273] A user accesses the application from a smartphone and enters new registration information (user name, password, email address). This information is the input data.

[1274] Step 2:

[1275] The terminal sends the entered user registration information to the server. The information is sent via the HTTP / HTTPS protocol by the communication means. This data becomes the input to the server.

[1276] Step 3:

[1277] The server validates the received user registration information before saving it in the data management means. Validation checks whether the username already exists and whether the email address format is correct. After verification, the user data is saved in the database. This is the data processing stage. If successful, a message indicating registration completion is output.

[1278] Step 4:

[1279] If user registration is successful, the server generates authentication information and issues a session ID. This session ID is used for subsequent authentication. The session ID is output.

[1280] Step 5:

[1281] When a user logs in, they enter authentication information (user name, password), which is the login input data.

[1282] Step 6:

[1283] The terminal sends the login information to the server. It is sent again via the communication means via the HTTP / HTTPS protocol. This data becomes the input to the server.

[1284] Step 7:

[1285] The server checks the received login information against its database to see if the hash of the entered password matches. If authentication is successful, a new session ID is issued. This is the output data, which is sent back to the user's device.

[1286] Step 8:

[1287] Once the authentication is successful, the user can use the application functions. Enter patient information (medical history, current health condition, medications being taken). This is the input data for entering patient information.

[1288] Step 9:

[1289] The terminal sends the entered patient information to the server. It is sent again via the communication means using the HTTP / HTTPS protocol. The patient information becomes the input data for the server.

[1290] Step 10:

[1291] The server stores the received patient information in a database and sends the data to the machine learning modeling means. The machine learning algorithm analyzes the data and suggests the optimal medication and its administration method. This is the data calculation step. The analysis results become the output data.

[1292] Step 11:

[1293] The server receives the proposed information from the machine learning model and sends it to the user's device via a display means. The results are displayed as output data to the user.

[1294] Step 12:

[1295] When a user uses the online medical consultation function, they consult with a medical professional through a video call or chat and input medical data, which is the input data for online medical consultation.

[1296] Step 13:

[1297] The terminal transmits the medical data to the server, and the server stores the received medical data in a database. Additional communication means are used here, and the data is stored.

[1298] Step 14:

[1299] The server retransmits the stored medical data to the machine learning model means and re-proposes the updated optimal medication and its administration method. This is the data calculation update step. The new proposal information becomes the output data.

[1300] Step 15:

[1301] The server receives the new proposal information and transmits it again to the user's terminal via the display means, where the latest medical results and proposals are displayed.

[1302] Step 16:

[1303] When a user has a question or wants to ask for advice, he or she sends the inquiry to the server through an inquiry form, which is the input data for the inquiry.

[1304] Step 17:

[1305] The server receives the inquiry sent from the terminal and provides an automated response system or human support through the support means. This is the support step, and the answer to the inquiry becomes the output data.

[1306] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1307] The present invention provides a system that enables pharmacists to provide optimal medications to patients. This system includes a database, a receiving unit, an AI model, and a display unit, and by combining it with an emotion engine, it is possible to recognize the user's emotions and provide medical suggestions and support content based on those emotions. The following describes in detail the implementation of each element of the system and the program processing.

[1308] System Configuration

[1309] Database means: A database for storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are stored.

[1310] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[1311] AI model means: Based on stored medical history and prescription information, it recommends the optimal type of medication, dosage, and timing of administration. This AI model uses machine learning algorithms to analyze multiple parameters.

[1312] Display means: The suggestions generated by the AI ​​model means are displayed on the device screen. The user interface is designed to be intuitive and easy to understand.

[1313] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[1314] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[1315] Emotion Engine: Recognizes the user's emotions in real time and adjusts medical suggestions and support content. This emotion engine uses voice and facial recognition technology to analyze the user's emotions.

[1316] Program processing

[1317] 1. User Registration and Authentication

[1318] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[1319] Server: Save the received user information in the database and validate the input information. If authentication is required, authenticate with the username and password. If authentication is successful, generate a session ID.

[1320] User: Once a user has successfully registered and authenticated, they can access various services.

[1321] 2. Enter patient information

[1322] Terminal: Pharmacists input and submit patient medical history and prescription information. The input form can be operated through an intuitive user interface.

[1323] Server: Stores the received patient information in a database and performs data preprocessing (format conversion, normalization).

[1324] 3. Emotional Recognition

[1325] Terminal: When a user inputs information, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data in real time.

[1326] Server: Stores the emotion data generated by the emotion engine in a database and sends it to the AI ​​model.

[1327] 4. Prescription suggestions based on AI models

[1328] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[1329] AI model means: Analyzes the transmitted data and suggests the optimal type of medication, dosage, and timing of administration. It can also adjust the suggestions based on the user's emotional data.

[1330] Server: Stores the generated prescription suggestions in a database and prepares them to be returned to the terminal.

[1331] Terminal: The suggested medication information is displayed on the pharmacist's screen. The pharmacist uses this information to provide the most appropriate medication for the patient.

[1332] 5. Collaboration between online and home medical care

[1333] Device: The patient or doctor enters and sends new medical data and prescription information through the online medical consultation app.

[1334] Server: Receives medical data and prescription information and stores them in a database.

[1335] AI model means: Generate new prescription suggestions based on stored data.

[1336] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[1337] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[1338] 6. 24-hour support

[1339] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[1340] Server: Receives the inquiry and stores it in a database.

[1341] Server: An automated response system responds immediately to inquiries that can be handled, and complex inquiries are forwarded to support staff.

[1342] Terminal: The user sees an automated response or a response from the support staff.

[1343] User: Review the support provided and determine the next action required.

[1344] Specific examples

[1345] Example 1: Prescription optimization for diabetic patients

[1346] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[1347] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model and emotion engine.

[1348] Emotion engine: Analyzes the patient's emotions and generates emotion data.

[1349] AI model means: Analyzes data and suggests appropriate insulin dosage and administration schedule, and suggests additional support if the patient shows emotional instability.

[1350] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[1351] Example 2: Online medical consultation from a remote location

[1352] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[1353] Server: Receives medical data and stores it in a database.

[1354] Emotion engine: Analyzes patient emotions during medical consultations and generates emotional data.

[1355] AI model method: Recommend the most suitable antibiotic based on clinical and emotional data.

[1356] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[1357] The system of the present invention, which incorporates an emotion engine, can provide more personalized medical services by recognizing patients' emotions in real time and making medical recommendations based on those emotions. This reduces the workload of pharmacists, reduces the risk of dispensing errors, and realizes high-quality medical services.

[1358] The processing flow will be explained below.

[1359] Program processing steps (system combining emotion engines)

[1360] User Registration and Authentication

[1361] Step 1:

[1362] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[1363] Step 2:

[1364] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[1365] Step 3:

[1366] Terminal: Display a registration completion message to the user.

[1367] Step 4:

[1368] Device: The user enters their email address and password on the login screen and presses the login button.

[1369] Step 5:

[1370] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[1371] Step 6:

[1372] Terminal: Displays a login success message and redirects the user to the dashboard.

[1373] Entering patient information

[1374] Step 1:

[1375] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[1376] Step 2:

[1377] Server: Receives input patient information and stores it in a database means.

[1378] Step 3:

[1379] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[1380] Emotion recognition

[1381] Step 1:

[1382] Terminal: While the user is entering information, the emotion engine analyzes the user's tone of voice and facial expressions in real time.

[1383] Step 2:

[1384] Server: Receives emotion data generated by the emotion engine and stores it in a database means.

[1385] Prescription suggestions based on AI models

[1386] Step 1:

[1387] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[1388] Step 2:

[1389] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing, taking into account the user's emotional data.

[1390] Step 3:

[1391] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[1392] Step 4:

[1393] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[1394] Step 5:

[1395] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[1396] Collaboration between online and home medical care

[1397] Step 1:

[1398] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[1399] Step 2:

[1400] Server: Receives medical data and prescription information and stores them in a database.

[1401] Step 3:

[1402] Emotion engine: Analyzes the patient's emotions (e.g., anxiety, relief) in real time during medical treatment and generates emotional data.

[1403] Step 4:

[1404] Server: Sends medical data and emotion data to the AI ​​model to generate new prescription suggestions.

[1405] Step 5:

[1406] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[1407] Step 6:

[1408] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[1409] 24-hour support

[1410] Step 1:

[1411] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[1412] Step 2:

[1413] Server: Receives the inquiry and stores it in a database.

[1414] Step 3:

[1415] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[1416] Step 4:

[1417] Terminal: The user sees an automated response or a response from the support staff.

[1418] Step 5:

[1419] User: Review the support provided and determine the next action required.

[1420] Such systems will help provide the most appropriate medication based on the patient's medical history, prescription, and emotional data, not only making pharmacists' work more efficient but also providing personalized, emotionally-driven medical services.

[1421] Example 2

[1422] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1423] While conventional systems offer medical suggestions based on a patient's medical history and prescription information, they are unable to consider the patient's feelings, making it difficult to provide personalized medical care. Furthermore, while there is a need for data integration for remote and home medical care, and for prompt responses to inquiries from patients and medical professionals, the current system is inadequate. This makes it difficult to improve patient satisfaction or reduce the workload of pharmacists.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1425] In this invention, the server includes a data storage means for storing the patient's medical history and prescription information, an information receiving means for receiving the patient's medical history and prescription information and storing it in the data storage means, an artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, an information display means for displaying the proposed medication information, and an emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. This enables personalized medical suggestions and support that take the patient's emotions into consideration, strengthens cooperation with remote medical care and home medical care, and enables quicker response to inquiries.

[1426] 1. "Data Storage Means" means a database device for storing patient medical history and prescription information.

[1427] 2. "Information receiving means" means a communication device for receiving patient medical history and prescription information and storing it in a data storage means.

[1428] 3. "Artificial intelligence model means" is a system that implements a machine learning algorithm to suggest optimal medications based on stored medical history, prescription information, and emotional data.

[1429] 4. "Information display means" means a display device or software for displaying suggested drug information in a form visible to the user.

[1430] 5. "Emotion analysis means" refers to a system that combines voice recognition and facial recognition technologies to recognize a user's emotions in real time and adjust medical suggestions and support content based on the results.

[1431] 6. "Additional information receiving means" means a communication device for receiving medical data entered by a patient or a medical professional through remote medical consultation or home medical consultation and storing the data in a data storage means.

[1432] 7. "Inquiry information receiving means" means a communication device for receiving and processing inquiries from patients or medical professionals.

[1433] 8. "Support means" means a system for providing automated responses or manual support to received inquiries.

[1434] This invention is a system for enabling pharmacists to provide patients with optimal medications, and includes the following elements: data storage means for storing the patient's medical history and prescription information, information receiving means for receiving the patient's medical history and prescription information, artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, information display means for displaying the proposed medication information, and emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. Embodiments of each of these elements are described in detail below.

[1435] The data storage means is used to store patient medical history and prescription information. Specific examples include relational database management systems (RDBMS) such as PostgreSQL and MySQL. This allows a unique ID to be assigned to each patient, and data such as medical history, allergy information, and past prescription information to be securely stored.

[1436] The information receiving means receives information entered by the patient or pharmacist using a terminal. This can be achieved by data communication via a web browser or mobile application. For example, some modules are built with React Native, and information is received in real time via the internet.

[1437] The AI ​​model is a machine learning algorithm that uses stored medical history, prescription information, and emotional data to provide the optimal medication type, dosage, and timing. It can analyze multiple parameters using TensorFlow and Google Cloud AI Platform, resulting in more accurate medication recommendations.

[1438] The information display means displays the suggested medication information on the device screen. Specifically, the user interface (UI) is built with React Native, allowing for intuitive and easy operation. This allows pharmacists to easily check the suggested medication information and provide the most appropriate medication for the patient.

[1439] Emotion analysis is a technology that recognizes a user's emotions in real time and adjusts medical suggestions and support content. For example, it uses Google Cloud Vision API and Amazon Polly, and combines voice and facial recognition technologies to analyze a user's emotions. This makes it possible to provide personalized medical suggestions that take into account the patient's emotional state.

[1440] Combining these technologies will create a system that suggests the most appropriate medication for a patient. As a concrete example, consider optimizing prescriptions for a diabetic patient. A pharmacist inputs the diabetic patient's medical history (e.g., blood glucose level history, dietary information) and new prescription (e.g., insulin), and this information is saved in a data storage means. An emotion analysis means analyzes the patient's emotions, and an artificial intelligence model means suggests the appropriate insulin dosage and administration schedule. This makes it possible to suggest the most appropriate medical treatment that takes into account the patient's emotional state.

[1441] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1442] Step 1: User Registration and Authentication

[1443] Device: A new user (pharmacist or patient) uses a device (e.g., tablet or smartphone) to enter their information (name, email address, password, etc.) into the registration form within the application and submit it.

[1444] Input: User information (name, email address, password, etc.)

[1445] Server: The received user information is stored in a data storage medium (e.g., AWS RDS) and the information is validated using the Django framework. If the information is invalid or incomplete, an error message is generated and sent back to the terminal.

[1446] Input: Received user information

[1447] Data processing: Validation using the Django framework

[1448] Output: Validation result (success / failure)

[1449] Server: If validation is successful, authenticate the user, generate a session ID and save it in the Redis session store.

[1450] Output: Session ID

[1451] User: If authentication is successful, the dashboard screen will be displayed. From this screen, various services can be accessed.

[1452] Step 2: Enter patient information

[1453] Terminal: The pharmacist inputs and transmits the patient's medical history and prescription information. The patient's medical history includes past illnesses, allergies, and past prescription information.

[1454] Input: Patient medical history and prescription information

[1455] Server: Stores the received patient information in a data storage medium (e.g., PostgreSQL) and performs data preprocessing (data format conversion and normalization) using Python scripts.

[1456] Input: Received patient information

[1457] Data processing: data format conversion, normalization

[1458] Output: Preprocessed patient information

[1459] Server: Stores the preprocessed data in a database so that it can be used in the next step.

[1460] Step 3: Recognize emotions

[1461] Terminal: As the patient enters information, the emotion analysis unit analyzes their voice tone and facial expressions to generate emotion data in real time. For example, emotion analysis can be performed by combining voice recognition and facial recognition technology.

[1462] Input: User's voice and facial expressions

[1463] Server: Stores the generated emotion data in a database (e.g., MongoDB) and sends it to the AI ​​model.

[1464] Input: Generated emotion data

[1465] Data processing: Emotion recognition through voice and facial expression analysis

[1466] Output: Emotion data

[1467] Step 4: Prescription suggestions using AI models

[1468] Server: Sends preprocessed patient information and emotion data to an AI model (e.g., a TensorFlow model hosted on Google Cloud AI Platform).

[1469] Input: Preprocessed patient information, emotion data

[1470] Data Computation: Data Analysis with AI Models

[1471] AI model means: Analyzes the transmitted data and suggests the most appropriate type of medication, dosage, and timing. It also takes into account emotional data and makes suggestions based on the patient's condition.

[1472] Output: Optimal prescription suggestions

[1473] Server: Stores the generated prescription suggestions in a database and returns them to the user's terminal.

[1474] Step 5: View prescription suggestions

[1475] Terminal: The pharmacist's screen displays suggested medication information, allowing them to dispense the most appropriate medication. The user interface is designed to be intuitive and easy to operate.

[1476] Input: Prescription suggestions sent from the server

[1477] Output: Prescription suggestions displayed in the user interface

[1478] Step 6: Collaboration between online and home medical care

[1479] Device: The patient or doctor uses the telemedicine app to enter and send new medical data and prescription information.

[1480] Input: medical data, prescription information

[1481] Server: Receives medical data and stores it in a database.

[1482] Input: Received medical data

[1483] Output: Saved medical data

[1484] AI model means: Make new prescription suggestions based on received data.

[1485] Output: New recipe suggestions

[1486] Terminal: The suggestions are displayed on the doctor's or pharmacist's screen, who then decides on the final prescription and provides it to the patient.

[1487] Step 7: 24-hour support

[1488] Terminal: The patient or pharmacist enters the question into the inquiry form and submits it.

[1489] Input: Inquiry details

[1490] Server: Receives the query and stores it in a database (e.g., Elasticsearch).

[1491] Input: Received inquiry

[1492] Output: Saved inquiry details

[1493] Server: An automated response system using Dialogflow responds immediately to simple inquiries and transfers complex inquiries to support staff.

[1494] Output: Response, support ticket

[1495] Terminal: The user sees an automated response or a response from the support staff.

[1496] Input: Response from the server

[1497] Output: The response displayed in the user interface

[1498] (Application example 2)

[1499] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1500] Conventional medical suggestion systems have limitations in making suggestions based on a patient's medical history and prescription information, and do not adequately provide personalized medical services that take into account the patient's emotions and mental state. Furthermore, factory production lines are not optimized to take into account the emotional state of workers, and there is a need for methods to improve production efficiency and reduce worker stress. A system that solves these problems is needed.

[1501] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1502] In this invention, the server includes database means for storing patient medical history and prescription information, receiving means for receiving patient medical history and prescription information and saving it in the database means, AI model means for proposing optimal medications based on the saved medical history and prescription information, display means for displaying the suggested medication information, and emotion recognition means for recognizing worker emotions and optimizing production plans and lines based on that data. This enables personalized medical recommendations that take patient emotions into account, and further allows factory production lines to be optimized taking into account the emotional states of workers, thereby improving production efficiency and reducing worker stress.

[1503] "Patient medical history and prescription information" refers to the patient's medical history and drug use instructions issued by a doctor.

[1504] "Database means" refers to a system that has the function of accumulating and storing information and searching and retrieving it as needed.

[1505] The "receiving means" is a device or function that has the function of receiving data from the outside and transmitting it to the internal system.

[1506] "AI model means" is a function that uses a machine learning algorithm to realize an artificial intelligence model that analyzes input data and generates results.

[1507] "Display means" refers to a device or function for visually displaying output information from the system.

[1508] "Worker emotions" refers to the emotional state of workers engaged in their daily work, and is data that requires analysis.

[1509] "Emotion recognition means" refers to a function for analyzing and recognizing emotions using voice recognition, facial expression recognition, and other sensor technologies.

[1510] A "production plan" is a plan for setting the schedule and methods of the production process.

[1511] "Line optimization" refers to the coordination and management of a production line to maximize its efficiency.

[1512] The present invention provides a system that suggests optimal medications based on a patient's medical history and prescription information, and a system that recognizes the emotional state of workers on a factory production line and performs production planning and line optimization. The following describes in detail the implementation of each element of the system and the program processing.

[1513] System Configuration

[1514] The system includes the following means:

[1515] 1. Database Means

[1516] The server's database stores patient medical history and prescription information along with a unique ID. This database also stores past prescription information, allergy information, and medical history, and is used as input data for analysis by the AI ​​model.

[1517] 2. Receiving Method

[1518] The server receives patient medical history and prescription information over the Internet and stores it in a database means in real time.

[1519] 3. AI Model Means

[1520] This method suggests the optimal type of medication, dosage, and timing based on the patient's medical history and prescription information. It uses machine learning algorithms to analyze multiple parameters to make optimal suggestions. It also includes integration with emotion recognition methods, which adjust the suggestions based on the user's emotional data.

[1521] 4. Display means

[1522] The suggested medication information is intuitively displayed on the device's display or smartphone, allowing pharmacists to provide the most appropriate medication to patients.

[1523] 5. Emotion recognition means

[1524] The server uses cameras, voice recognition, and facial expression recognition software to analyze workers' emotions in real time, and can use this emotional data to adjust production line speeds and optimize work assignments.

[1525] Program processing explanation

[1526] Hardware

[1527] Camera: Captures worker facial expressions in real time.

[1528] Server: Stores data, analyzes it, and generates suggestions.

[1529] Device (smartphone, tablet, PC): Enters and displays information.

[1530] software

[1531] OpenCV: Image processing and face recognition.

[1532] TensorFlow (Keras): Running an emotion recognition model.

[1533] Database system (e.g., SQLite): patient and worker data storage.

[1534] Machine learning algorithms: Building and analyzing AI models.

[1535] Specific examples

[1536] 1. Optimizing prescriptions for diabetes patients

[1537] The pharmacist enters the patient's medical history and new prescription information from the terminal, which is then sent to the server and stored in a database.

[1538] The AI ​​modeling tool analyzes this data and suggests optimal insulin dosages and schedules, and if the emotion recognition tool determines that additional support is needed, it will provide more detailed support suggestions.

[1539] The suggestions are displayed on the terminal screen, and the pharmacist makes the final decision.

[1540] 2. Factory production line optimization

[1541] The emotion recognition means collects and analyzes the emotional data of workers. If the worker's stress level is high, the server adjusts and optimizes the speed of the production line.

[1542] Emotional data is analyzed at regular intervals, and production plans are revised based on the emotional state.

[1543] Prompt Sentence Examples

[1544] - "Write a program that analyzes the facial expressions of factory workers and optimizes the speed of the production line based on their emotional state."

[1545] This system will enable personalized medical recommendations that take into account the patient's emotions, and will also enable optimization of factory production lines that take into account the emotional state of workers, thereby improving production efficiency and reducing worker stress.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] The server receives the patient's medical history and prescription information from the terminal. The data entered on the terminal (input: patient's medical history and prescription information) is sent to the server via the Internet. The server converts and normalizes the received data and stores it in a database (output: normalized patient information).

[1549] Step 2:

[1550] The server passes the stored data to an AI model means to generate optimal medication suggestions. The server retrieves patient information from a database (input: patient information from database), analyzes the data using machine learning algorithms, and suggests medication types, dosages, and timing (output: optimal medication suggestions).

[1551] Step 3:

[1552] The terminal receives the drug suggestion information from the server and presents it to the pharmacist through the display means. The terminal receives and displays the suggested drug information (input: suggested information from the server) and displays it on the screen in a format that is easy for the user to understand (output: presented drug information).

[1553] Step 4:

[1554] The emotion recognition means captures the facial expressions of workers in real time through a camera. The emotion recognition means analyzes the captured video data (input: video data from the camera) and extracts facial expression features (output: facial expression feature data).

[1555] Step 5:

[1556] The server recognizes emotions based on the facial expression feature data sent from the emotion recognition means. The server receives the feature data (input: facial expression feature data), analyzes it using an emotion recognition model such as TensorFlow, and estimates the worker's emotional state (output: emotion data).

[1557] Step 6:

[1558] The server optimizes the speed of the production line and the allocation of work based on the emotional data. The server retrieves emotional data from a database over a certain period of time (input: past emotional data) and determines the optimal production plan based on the results of an analysis of the data with the current emotional data (output: optimized production plan).

[1559] Step 7:

[1560] The terminal receives the optimization results of the production plan from the server and transmits them to the production line control device. The terminal receives the optimization data from the server (input: production plan data from the server) and sends it to the production line control device (output: instructions to the control device).

[1561] Step 8:

[1562] The production line control device adjusts the speed of the production line and the allocation of work in accordance with instructions from the terminal. The control device adjusts the speed of the production line based on the input optimization instructions (input: instructions from the terminal) to improve production efficiency (output: optimized production line).

[1563] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1564] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1565] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1566] [Fourth embodiment]

[1567] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1568] 7, a 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.

[1569] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1570] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1571] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1572] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1573] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1574] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1575] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1576] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1577] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1578] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1579] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1580] The present invention provides a system for pharmacists to provide optimal medications to patients. This system includes a database means, a receiving means, an AI model means, and a display means. It may further include an additional receiving means, an inquiry receiving means, and a support means as needed. The following describes in detail the embodiment of each element of the system and the processing of the program.

[1581] System Configuration

[1582] Database means: This is a database management system for persistently storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[1583] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[1584] AI model means: Based on stored medical history and prescription information, it recommends the most appropriate medication, its dosage, and administration method. The AI ​​model uses machine learning algorithms to analyze multiple parameters.

[1585] Display: The suggestions generated by the AI ​​model are displayed on the device screen. This interface is designed to be intuitive and easy to understand, making it easy for pharmacists to use.

[1586] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[1587] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[1588] Program processing

[1589] 1. User Registration and Authentication

[1590] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[1591] Server: The received user information is saved in the database and the input information is validated. If authentication is required, authentication is performed using the user name and password, and if authentication is successful, a session ID is generated.

[1592] User: Once a user has successfully registered and authenticated, they can access various services.

[1593] 2. Enter patient information

[1594] Terminal: Pharmacists input and submit patient medical history and prescription information through an intuitive user interface.

[1595] Server: Stores the received patient information in a database, and simultaneously performs data preprocessing (format normalization, etc.).

[1596] 3. Prescription suggestions based on AI models

[1597] Server: Sends pre-processed patient information to the AI ​​model, which analyzes the received data and suggests the optimal medication, dosage, and dosing schedule.

[1598] Terminal: Suggested medication information is displayed. Pharmacists use this information to provide the most appropriate medication for the patient.

[1599] 4. Collaboration between online and home medical care

[1600] Terminal: Receives medical data entered online or through home medical care.

[1601] Server: Stores medical data in a database and sends it to the AI ​​model as needed to generate new suggestions.

[1602] Terminal: Displays medical results and new suggestions.

[1603] 5. 24-hour support

[1604] Terminal: Provides an inquiry form for patients or pharmacists and accepts submitted inquiries.

[1605] Server: Stores the inquiry and responds via an automated response system or support staff.

[1606] User: Check the support details and take any necessary action.

[1607] Specific examples

[1608] Example 1: Prescription optimization for diabetic patients

[1609] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[1610] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model.

[1611] AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[1612] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[1613] Example 2: Online medical consultation from a remote location

[1614] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[1615] Server: Receives medical data and stores it in a database.

[1616] AI model method: Recommend the most appropriate antibiotic based on clinical data.

[1617] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[1618] By implementing such a system and program, it is possible to reduce the workload of pharmacists, reduce dispensing errors, and provide high-quality medical services. The system of the present invention supports the provision of optimal medications according to individual patient needs, thereby helping to improve the quality of medical services and ensure safety.

[1619] The processing flow will be explained below.

[1620] Program processing steps

[1621] User Registration and Authentication

[1622] Step 1:

[1623] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[1624] Step 2:

[1625] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[1626] Step 3:

[1627] Terminal: Display a registration completion message to the user.

[1628] Step 4:

[1629] Device: The user enters their email address and password on the login screen and presses the login button.

[1630] Step 5:

[1631] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[1632] Step 6:

[1633] Terminal: Displays a login success message and redirects the user to the dashboard.

[1634] Entering patient information

[1635] Step 1:

[1636] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[1637] Step 2:

[1638] Server: Receives input patient information and stores it in a database means.

[1639] Step 3:

[1640] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[1641] Prescription suggestions based on AI models

[1642] Step 1:

[1643] Server: Sends pre-processed patient information to the AI ​​model means.

[1644] Step 2:

[1645] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing of administration.

[1646] Step 3:

[1647] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[1648] Step 4:

[1649] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[1650] Step 5:

[1651] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[1652] Collaboration between online and home medical care

[1653] Step 1:

[1654] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[1655] Step 2:

[1656] Server: Receives medical data and prescription information and stores them in a database.

[1657] Step 3:

[1658] Server: Sends the stored data to the AI ​​model means and generates new prescription suggestions.

[1659] Step 4:

[1660] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[1661] Step 5:

[1662] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[1663] 24-hour support

[1664] Step 1:

[1665] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[1666] Step 2:

[1667] Server: Receives the inquiry and stores it in a database.

[1668] Step 3:

[1669] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[1670] Step 4:

[1671] Terminal: The user sees an automated response or a response from the support staff.

[1672] Step 5:

[1673] User: Review the support provided and determine the next action required.

[1674] This system will not only help provide the most appropriate medication based on the patient's medical history and prescription, making pharmacists' work more efficient, but also provide reliable medical services through 24-hour support.

[1675] Example 1

[1676] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1677] Conventional medication management systems require manual input and management of patient medical history and prescription information, which can easily lead to input errors and inconsistencies, making it difficult to recommend optimal medications. Furthermore, data integration with online and home medical consultations was insufficient, resulting in delayed responses to patients in remote locations. Responses to inquiries from patients and pharmacists also lacked real-time response capabilities, making it difficult to provide satisfactory support. There is a need for a system that can solve these issues.

[1678] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1679] In this invention, the server includes a user management unit, a database unit, a receiving unit, an AI model generation unit, and a display unit. This allows for efficient and accurate management of patient medical history and prescription information, and the AI ​​model can be used to suggest optimal medications, their dosages, and administration schedules. Furthermore, by receiving data from online and home medical consultations and incorporating it into the AI ​​model in real time, it is possible to respond quickly to patients in remote locations. Furthermore, by receiving inquiries from patients and pharmacists and providing automated or manual support, 24-hour support can be provided.

[1680] "User management means" refers to the means for inputting, receiving, validating, and storing registration information and authentication information for new and existing users.

[1681] "Database Means" means a database management system for storing patient medical history and prescription information.

[1682] The "receiving means" is a means for receiving inputted patient medical history and prescription information in real time and storing it in the database means.

[1683] The "generative AI model means" is an artificial intelligence model that uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on stored medical history and prescription information.

[1684] The "display means" is an interface for displaying the suggested drug information on the user terminal.

[1685] The "additional receiving means" is a means for receiving medical data entered by a patient or a medical provider through online medical consultation or home medical consultation, storing it in the database means, and transmitting it to the generating AI model means as needed.

[1686] The "inquiry receiving means" is a means for receiving inquiries from patients or pharmacists.

[1687] "Support means" refers to means for providing automatic responses or manual support to received inquiries.

[1688] The present invention provides a system used by pharmacists to provide optimal medicines to patients, which includes a user management means, a database means, a receiving means, a generating AI model means, a display means, and optionally an additional receiving means, an inquiry receiving means, and a support means.

[1689] System configuration

[1690] 1. User Management Methods

[1691] Terminal: A new user (pharmacist or patient) enters information such as name, email address, and password into a registration form within the application.

[1692] Server: The received user information is saved in the database and validated. If authentication is required, the username and password are hashed and saved in the database.

[1693] 2. Database Means

[1694] Server: A database management system is used to permanently store patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are efficiently stored.

[1695] 3. Receiving Method

[1696] Terminal: The pharmacist inputs the patient's medical history and prescription information and sends it to the server. This operation is performed using an intuitive user interface.

[1697] Server: Stores the received patient information in a database and performs data preprocessing (e.g., normalization of data format).

[1698] 4. Generative AI Model Means

[1699] Server: Sends preprocessed patient information to the generative AI model, which uses machine learning frameworks such as TensorFlow or PyTorch to analyze multiple parameters and propose optimal medications, dosages, and dosing schedules.

[1700] 5. Display means

[1701] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the most appropriate medication based on this suggestion.

[1702] 6. Additional Receiving Methods (Optional)

[1703] Terminal: Patients or healthcare providers enter medical data through online or home consultations.

[1704] Server: Receives medical data in real time, stores it in a database, and sends it to the generative AI model as needed.

[1705] 7. Inquiry and support methods (optional)

[1706] Terminal: The patient or pharmacist enters a question through an inquiry form and sends it to the server.

[1707] Server: Receives the inquiry and stores it in a database. An automated response system or support staff analyzes the inquiry and responds.

[1708] Specific examples

[1709] Example 1: Prescription optimization for diabetic patients

[1710] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[1711] Server: Receives medical history and prescription information and stores it in a database.

[1712] Generative AI model means: Analyzes data and suggests appropriate insulin doses and administration schedules.

[1713] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[1714] Example 2: Online medical consultation from a remote location

[1715] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[1716] Server: Receives medical data and stores it in a database.

[1717] Generative AI model method: Recommends the most appropriate antibiotic based on clinical data.

[1718] Terminal: The proposal is displayed on the doctor's or patient's screen, and the healthcare provider makes the final prescription decision.

[1719] Prompt Sentence Examples

[1720] "The AI ​​model suggests optimal insulin dosages based on a diabetic patient's blood glucose history and dietary information."

[1721] By combining these components and processing procedures, the system ensures efficient and accurate medication provision for both pharmacists and patients. It also quickly updates data from online and home consultations, enabling appropriate medical services to be provided to patients in remote locations. Furthermore, a 24-hour support function resolves user questions and concerns, providing highly reliable medical services.

[1722] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1723] Step 1:

[1724] Enter user registration information

[1725] Terminal: A new user fills in the registration form within the application with the required information, such as name, email address, and password. Once the information is complete, they click the "Register" button to send the information to the server. The input for this step is the user's basic information, and the output is sending the registration information to the server.

[1726] Step 2:

[1727] Storing and validating user information

[1728] Server: The received user information is saved in the database. Furthermore, the input information must be validated to check that the information is in the correct format and that there is no invalid or duplicate information. If necessary, a confirmation email is sent. The input for this step is the user information, and the output is the saved user information and the validation results.

[1729] Step 3:

[1730] Logging in and generating a session ID

[1731] User: An existing user enters their username and password into the login form and clicks the "Login" button.

[1732] Server: Checks the username and password, and if authentication is successful, generates a session ID and returns it to the user. The input to this step is the user's authentication information, and the output is a session ID.

[1733] Step 4:

[1734] Entering patient information

[1735] Terminal: The pharmacist enters the patient's basic information (name, age, sex, etc.), medical history, allergy information, medical history, etc. New prescription information (drug name, dosage, administration method) is also entered and sent. The input for this step is the patient's detailed information, and the output is sending the information to the server.

[1736] Step 5:

[1737] Patient information storage and preprocessing

[1738] Server: Stores the received patient information in a database and performs preprocessing, which includes normalizing the data format, filling in missing data, and checking for inconsistencies. The input of this step is the received patient information, and the output is the normalized patient information.

[1739] Step 6:

[1740] Prescription suggestions based on AI models

[1741] Server: Sends preprocessed patient information to the generative AI model. The generative AI model uses machine learning algorithms to suggest optimal medications, their dosages, and administration schedules based on the input data. The input is normalized patient information, and the output is suggested medication information.

[1742] Step 7:

[1743] Displaying suggested drug information

[1744] Terminal: The suggested medication information is displayed on the user's screen. The pharmacist provides the patient with the optimal medication based on this suggestion. The input to this step is the suggested information from the generative AI model, and the output is the information displayed to the user.

[1745] Step 8:

[1746] Receiving and storing online medical data (optional)

[1747] Device: Patients consult with doctors through an online medical consultation app, enter medical data, and send it.

[1748] Server: Receives medical data and stores it in a database. If necessary, it sends it to the generative AI model to generate new medication recommendations. The input for this step is the medical data, and the output is updated recommendation information.

[1749] Step 9:

[1750] Inquiry reception and support (optional)

[1751] Terminal: The patient or pharmacist enters a question into the inquiry form and submits it.

[1752] Server: Receives the inquiry and stores it in a database. An automated response system or support staff responds to the inquiry. The input to this step is the inquiry, and the output is the response.

[1753] Through these processing steps, the system will ensure efficient and accurate drug delivery, enable rapid response to patients in remote locations, and increase user confidence by providing 24 / 7 support.

[1754] (Application example 1)

[1755] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1756] Although conventional drug recommendation systems manage patients' medical history and prescription information and recommend optimal medications, they lack the ability to manage user authentication, link with online medical consultations, and provide 24-hour support. Furthermore, it is difficult for patients to efficiently receive medical services from home, and they are also incomplete tools for pharmacists to provide optimal medications to patients.

[1757] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1758] In this invention, the server includes a data management unit that stores patient medical history and prescription information, a communication unit that receives the patient's medical history and prescription information and stores it in the data management unit, a machine learning model unit that recommends optimal medications based on the stored medical history and prescription information, a display unit that displays the recommended medication information, and an authentication unit that performs user registration and manages authentication. This allows users to easily receive medical services from home, and pharmacists can efficiently recommend optimal medications for patients. Furthermore, by providing online medical consultations and 24-hour support, more advanced and comprehensive medical support can be realized.

[1759] "Data management means" refers to a database system that permanently stores medical data such as patient medical history and prescription information, and allows efficient search and reference as needed.

[1760] "Communication Means" refers to a data communication module for receiving patient history and prescription information and storing it appropriately in the Data Management Means.

[1761] A "machine learning model" is an AI algorithm that analyzes stored medical history and prescription information to suggest the most appropriate medication and its administration method.

[1762] The "display means" refers to an interface for visually displaying to the user the medication suggestions generated by the AI ​​model means.

[1763] "Authentication means" refers to an authentication system for registering users, managing authentication information, and providing secure access.

[1764] The "additional communication means" refers to a data communication module for receiving medical data entered through telemedicine or home medical care and for adding and storing the data in the data management means.

[1765] The "inquiry communication means" refers to a data communication module for accepting inquiries from patients and pharmacists.

[1766] "Support means" refers to a system for providing automated response or human support to received inquiries.

[1767] System Overview

[1768] The present invention is a system that recommends optimal medications based on a patient's medical history and prescription information. The system includes data management means, communication means, machine learning model means, display means, and authentication means. This allows users to conveniently receive medical services from home. It also provides remote medical care and 24-hour support.

[1769] Specific program description

[1770] The core of the system is the server, which processes data and performs calculations using the following means:

[1771] Data Management Measures

[1772] The server contains a database that persistently stores patient history and prescription information, often using a standard database management system such as MySQL or PostgreSQL. Each patient is assigned a unique ID, and information is stored based on that ID.

[1773] communication means

[1774] The patient's medical history and prescription information sent from the device is received by the server's communications module, which in most cases uses the HTTP / HTTPS protocol over the Internet to send and receive data, using the Python Flask or Django framework.

[1775] Machine Learning Model Means

[1776] Based on the stored medical history and prescription information, AI algorithms are used to suggest the optimal medication, dosage, and administration method. This part utilizes machine learning frameworks such as TensorFlow and PyTorch. Multiple parameters (e.g., patient age, medical history, allergy information) are analyzed to suggest the optimal medication.

[1777] Display means

[1778] The medication suggestions generated by the server are displayed to the user through a display module on the terminal. The user interface is intuitively designed using modern web frameworks such as React and Vue.js.

[1779] Authentication Method

[1780] User registration and authentication are important to provide secure access. When a user registers, the information they enter is validated and stored in a database. When authentication is required, a session ID is issued for the username and password combination.

[1781] Examples and prompts

[1782] Example: User registration

[1783] For example: "In the user registration form, I entered the following: username: 'test_user', password: 'password123', email: 'test@example.com'."

[1784] Prompt Sentence Examples

[1785] Please enter your username, password, and email address to register. If an existing username or email address is in use, an error message will appear.

[1786] In this way, by managing patients' medical history and prescription information, displaying optimal medication suggestions based on machine learning models, and integrating online medical consultation and 24-hour support functions, patients can receive medical services efficiently from home.

[1787] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1788] Step 1:

[1789] A user accesses the application from a smartphone and enters new registration information (user name, password, email address). This information is the input data.

[1790] Step 2:

[1791] The terminal sends the entered user registration information to the server. The information is sent via the HTTP / HTTPS protocol by the communication means. This data becomes the input to the server.

[1792] Step 3:

[1793] The server validates the received user registration information before saving it in the data management means. Validation checks whether the username already exists and whether the email address format is correct. After verification, the user data is saved in the database. This is the data processing stage. If successful, a message indicating registration completion is output.

[1794] Step 4:

[1795] If user registration is successful, the server generates authentication information and issues a session ID. This session ID is used for subsequent authentication. The session ID is output.

[1796] Step 5:

[1797] When a user logs in, they enter authentication information (user name, password), which is the login input data.

[1798] Step 6:

[1799] The terminal sends the login information to the server. It is sent again via the communication means via the HTTP / HTTPS protocol. This data becomes the input to the server.

[1800] Step 7:

[1801] The server checks the received login information against its database to see if the hash of the entered password matches. If authentication is successful, a new session ID is issued. This is the output data, which is sent back to the user's device.

[1802] Step 8:

[1803] Once the authentication is successful, the user can use the application functions. Enter patient information (medical history, current health condition, medications being taken). This is the input data for entering patient information.

[1804] Step 9:

[1805] The terminal sends the entered patient information to the server. It is sent again via the communication means using the HTTP / HTTPS protocol. The patient information becomes the input data for the server.

[1806] Step 10:

[1807] The server stores the received patient information in a database and sends the data to the machine learning modeling means. The machine learning algorithm analyzes the data and suggests the optimal medication and its administration method. This is the data calculation step. The analysis results become the output data.

[1808] Step 11:

[1809] The server receives the proposed information from the machine learning model and sends it to the user's device via a display means. The results are displayed as output data to the user.

[1810] Step 12:

[1811] When a user uses the online medical consultation function, they consult with a medical professional through a video call or chat and input medical data, which is the input data for online medical consultation.

[1812] Step 13:

[1813] The terminal transmits the medical data to the server, and the server stores the received medical data in a database. Additional communication means are used here, and the data is stored.

[1814] Step 14:

[1815] The server retransmits the stored medical data to the machine learning model means and re-proposes the updated optimal medication and its administration method. This is the data calculation update step. The new proposal information becomes the output data.

[1816] Step 15:

[1817] The server receives the new proposal information and transmits it again to the user's terminal via the display means, where the latest medical results and proposals are displayed.

[1818] Step 16:

[1819] When a user has a question or wants to ask for advice, he or she sends the inquiry to the server through an inquiry form, which is the input data for the inquiry.

[1820] Step 17:

[1821] The server receives the inquiry sent from the terminal and provides an automated response system or human support through the support means. This is the support step, and the answer to the inquiry becomes the output data.

[1822] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1823] The present invention provides a system that enables pharmacists to provide optimal medications to patients. This system includes a database, a receiving unit, an AI model, and a display unit, and by combining it with an emotion engine, it is possible to recognize the user's emotions and provide medical suggestions and support content based on those emotions. The following describes in detail the implementation of each element of the system and the program processing.

[1824] System Configuration

[1825] Database means: A database for storing patient medical history and prescription information. A unique ID is assigned to each patient, and medical history, allergy information, past prescription information, etc. are stored.

[1826] Receiving means: Receives patient medical history and prescription information entered from the terminal in real time and stores it in the database means. This means includes a data communication module via the Internet.

[1827] AI model means: Based on stored medical history and prescription information, it recommends the optimal type of medication, dosage, and timing of administration. This AI model uses machine learning algorithms to analyze multiple parameters.

[1828] Display means: The suggestions generated by the AI ​​model means are displayed on the device screen. The user interface is designed to be intuitive and easy to understand.

[1829] Additional receiving means (optional): Receives online or home medical care data and stores it in the database. This means you can receive accurate medical data even from a remote environment.

[1830] Inquiry receiving means and support means (optional): Has the function of accepting inquiries from patients or pharmacists, and provides automatic responses or manual support for received inquiries.

[1831] Emotion Engine: Recognizes the user's emotions in real time and adjusts medical suggestions and support content. This emotion engine uses voice and facial recognition technology to analyze the user's emotions.

[1832] Program processing

[1833] 1. User Registration and Authentication

[1834] Terminal: A new user (pharmacist or patient) enters information into a registration form within the application and submits it.

[1835] Server: Save the received user information in the database and validate the input information. If authentication is required, authenticate with the username and password. If authentication is successful, generate a session ID.

[1836] User: Once a user has successfully registered and authenticated, they can access various services.

[1837] 2. Enter patient information

[1838] Terminal: Pharmacists input and submit patient medical history and prescription information. The input form can be operated through an intuitive user interface.

[1839] Server: Stores the received patient information in a database and performs data preprocessing (format conversion, normalization).

[1840] 3. Emotional Recognition

[1841] Terminal: When a user inputs information, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data in real time.

[1842] Server: Stores the emotion data generated by the emotion engine in a database and sends it to the AI ​​model.

[1843] 4. Prescription suggestions based on AI models

[1844] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[1845] AI model means: Analyzes the transmitted data and suggests the optimal type of medication, dosage, and timing of administration. It can also adjust the suggestions based on the user's emotional data.

[1846] Server: Stores the generated prescription suggestions in a database and prepares them to be returned to the terminal.

[1847] Terminal: The suggested medication information is displayed on the pharmacist's screen. The pharmacist uses this information to provide the most appropriate medication for the patient.

[1848] 5. Collaboration between online and home medical care

[1849] Device: The patient or doctor enters and sends new medical data and prescription information through the online medical consultation app.

[1850] Server: Receives medical data and prescription information and stores them in a database.

[1851] AI model means: Generate new prescription suggestions based on stored data.

[1852] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[1853] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[1854] 6. 24-hour support

[1855] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[1856] Server: Receives the inquiry and stores it in a database.

[1857] Server: An automated response system responds immediately to inquiries that can be handled, and complex inquiries are forwarded to support staff.

[1858] Terminal: The user sees an automated response or a response from the support staff.

[1859] User: Review the support provided and determine the next action required.

[1860] Specific examples

[1861] Example 1: Prescription optimization for diabetic patients

[1862] Terminal: The pharmacist enters the diabetic patient's medical history (e.g., blood sugar history, dietary information) and new prescription (e.g., insulin).

[1863] Server: Receives medical history and prescription information, stores it in a database, and sends it to the AI ​​model and emotion engine.

[1864] Emotion engine: Analyzes the patient's emotions and generates emotion data.

[1865] AI model means: Analyzes data and suggests appropriate insulin dosage and administration schedule, and suggests additional support if the patient shows emotional instability.

[1866] Terminal: The suggested information is displayed on the pharmacist's screen, and the pharmacist confirms the insulin prescription based on the suggestions.

[1867] Example 2: Online medical consultation from a remote location

[1868] Device: A patient consults a doctor via an online medical consultation app and receives a prescription for a new medication (e.g., antibiotics).

[1869] Server: Receives medical data and stores it in a database.

[1870] Emotion engine: Analyzes patient emotions during medical consultations and generates emotional data.

[1871] AI model method: Recommend the most suitable antibiotic based on clinical and emotional data.

[1872] Terminal: The proposal is displayed on the doctor's or patient's screen, and the doctor makes the final prescription decision.

[1873] The system of the present invention, which incorporates an emotion engine, can provide more personalized medical services by recognizing patients' emotions in real time and making medical recommendations based on those emotions. This reduces the workload of pharmacists, reduces the risk of dispensing errors, and realizes high-quality medical services.

[1874] The processing flow will be explained below.

[1875] Program processing steps (system combining emotion engines)

[1876] User Registration and Authentication

[1877] Step 1:

[1878] Terminal: The user enters their name, email address, and password into the registration form and presses the submit button.

[1879] Step 2:

[1880] Server: Receives the entered information and performs data validation. If validation is successful, saves the user information in a database.

[1881] Step 3:

[1882] Terminal: Display a registration completion message to the user.

[1883] Step 4:

[1884] Device: The user enters their email address and password on the login screen and presses the login button.

[1885] Step 5:

[1886] Server: Checks the entered authentication information using a database, and if authentication is successful, generates a session ID and returns it to the terminal.

[1887] Step 6:

[1888] Terminal: Displays a login success message and redirects the user to the dashboard.

[1889] Entering patient information

[1890] Step 1:

[1891] Terminal: The pharmacist enters the patient's medical history, allergy information, medical history, and current prescription information into the input form and presses the send button.

[1892] Step 2:

[1893] Server: Receives input patient information and stores it in a database means.

[1894] Step 3:

[1895] Server: Performs preprocessing such as format conversion and normalization on the received data, converting it into a format that is easy to process.

[1896] Emotion recognition

[1897] Step 1:

[1898] Terminal: While the user is entering information, the emotion engine analyzes the user's tone of voice and facial expressions in real time.

[1899] Step 2:

[1900] Server: Receives emotion data generated by the emotion engine and stores it in a database means.

[1901] Prescription suggestions based on AI models

[1902] Step 1:

[1903] Server: Sends pre-processed patient information and emotion data to the AI ​​model means.

[1904] Step 2:

[1905] Server: The AI ​​model analyzes the received data and calculates the optimal type of medication, dosage, and timing, taking into account the user's emotional data.

[1906] Step 3:

[1907] Server: Stores the generated prescription suggestions in a database means and prepares to return them to the terminal.

[1908] Step 4:

[1909] Terminal: Suggested medication information is displayed on the pharmacist's screen.

[1910] Step 5:

[1911] User: The pharmacist reviews the suggestion and makes adjustments as needed, or simply provides the patient with the optimal medication.

[1912] Collaboration between online and home medical care

[1913] Step 1:

[1914] Terminal: The patient or doctor enters new medical data and prescription information through the online medical consultation app and presses the send button.

[1915] Step 2:

[1916] Server: Receives medical data and prescription information and stores them in a database.

[1917] Step 3:

[1918] Emotion engine: Analyzes the patient's emotions (e.g., anxiety, relief) in real time during medical treatment and generates emotional data.

[1919] Step 4:

[1920] Server: Sends medical data and emotion data to the AI ​​model to generate new prescription suggestions.

[1921] Step 5:

[1922] Terminal: New medical findings and prescription suggestions are displayed on the doctor's or pharmacist's screen.

[1923] Step 6:

[1924] User: The doctor or pharmacist reviews the proposal, decides on the final prescription, and provides it to the patient.

[1925] 24-hour support

[1926] Step 1:

[1927] Terminal: The patient or pharmacist enters the question into the inquiry form and presses the send button.

[1928] Step 2:

[1929] Server: Receives the inquiry and stores it in a database.

[1930] Step 3:

[1931] Server: An automated system responds immediately to inquiries that can be handled, while complex inquiries are forwarded to support staff.

[1932] Step 4:

[1933] Terminal: The user sees an automated response or a response from the support staff.

[1934] Step 5:

[1935] User: Review the support provided and determine the next action required.

[1936] Such systems will help provide the most appropriate medication based on the patient's medical history, prescription, and emotional data, not only making pharmacists' work more efficient but also providing personalized, emotionally-driven medical services.

[1937] Example 2

[1938] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1939] While conventional systems offer medical suggestions based on a patient's medical history and prescription information, they are unable to consider the patient's feelings, making it difficult to provide personalized medical care. Furthermore, while there is a need for data integration for remote and home medical care, and for prompt responses to inquiries from patients and medical professionals, the current system is inadequate. This makes it difficult to improve patient satisfaction or reduce the workload of pharmacists.

[1940] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1941] In this invention, the server includes a data storage means for storing the patient's medical history and prescription information, an information receiving means for receiving the patient's medical history and prescription information and storing it in the data storage means, an artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, an information display means for displaying the proposed medication information, and an emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. This enables personalized medical suggestions and support that take the patient's emotions into consideration, strengthens cooperation with remote medical care and home medical care, and enables quicker response to inquiries.

[1942] 1. "Data Storage Means" means a database device for storing patient medical history and prescription information.

[1943] 2. "Information receiving means" means a communication device for receiving patient medical history and prescription information and storing it in a data storage means.

[1944] 3. "Artificial intelligence model means" is a system that implements a machine learning algorithm to suggest optimal medications based on stored medical history, prescription information, and emotional data.

[1945] 4. "Information display means" means a display device or software for displaying suggested drug information in a form visible to the user.

[1946] 5. "Emotion analysis means" refers to a system that combines voice recognition and facial recognition technologies to recognize a user's emotions in real time and adjust medical suggestions and support content based on the results.

[1947] 6. "Additional information receiving means" means a communication device for receiving medical data entered by a patient or a medical professional through remote medical consultation or home medical consultation and storing the data in a data storage means.

[1948] 7. "Inquiry information receiving means" means a communication device for receiving and processing inquiries from patients or medical professionals.

[1949] 8. "Support means" means a system for providing automated responses or manual support to received inquiries.

[1950] This invention is a system for enabling pharmacists to provide patients with optimal medications, and includes the following elements: data storage means for storing the patient's medical history and prescription information, information receiving means for receiving the patient's medical history and prescription information, artificial intelligence model means for proposing optimal medications based on the stored medical history and prescription information and emotional data, information display means for displaying the proposed medication information, and emotion analysis means for recognizing the user's emotions in real time and adjusting medical suggestions and support content. Embodiments of each of these elements are described in detail below.

[1951] The data storage means is used to store patient medical history and prescription information. Specific examples include relational database management systems (RDBMS) such as PostgreSQL and MySQL. This allows a unique ID to be assigned to each patient, and data such as medical history, allergy information, and past prescription information to be securely stored.

[1952] The information receiving means receives information entered by the patient or pharmacist using a terminal. This can be achieved by data communication via a web browser or mobile application. For example, some modules are built with React Native, and information is received in real time via the internet.

[1953] The AI ​​model is a machine learning algorithm that uses stored medical history, prescription information, and emotional data to provide the optimal medication type, dosage, and timing. It can analyze multiple parameters using TensorFlow and Google Cloud AI Platform, resulting in more accurate medication recommendations.

[1954] The information display means displays the suggested medication information on the device screen. Specifically, the user interface (UI) is built with React Native, allowing for intuitive and easy operation. This allows pharmacists to easily check the suggested medication information and provide the most appropriate medication for the patient.

[1955] Emotion analysis is a technology that recognizes a user's emotions in real time and adjusts medical suggestions and support content. For example, it uses Google Cloud Vision API and Amazon Polly, and combines voice and facial recognition technologies to analyze a user's emotions. This makes it possible to provide personalized medical suggestions that take into account the patient's emotional state.

[1956] Combining these technologies will create a system that suggests the most appropriate medication for a patient. As a concrete example, consider optimizing prescriptions for a diabetic patient. A pharmacist inputs the diabetic patient's medical history (e.g., blood glucose level history, dietary information) and new prescription (e.g., insulin), and this information is saved in a data storage means. An emotion analysis means analyzes the patient's emotions, and an artificial intelligence model means suggests the appropriate insulin dosage and administration schedule. This makes it possible to suggest the most appropriate medical treatment that takes into account the patient's emotional state.

[1957] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1958] Step 1: User Registration and Authentication

[1959] Device: A new user (pharmacist or patient) uses a device (e.g., tablet or smartphone) to enter their information (name, email address, password, etc.) into the registration form within the application and submit it.

[1960] Input: User information (name, email address, password, etc.)

[1961] Server: The received user information is stored in a data storage medium (e.g., AWS RDS) and the information is validated using the Django framework. If the information is invalid or incomplete, an error message is generated and sent back to the terminal.

[1962] Input: Received user information

[1963] Data processing: Validation using the Django framework

[1964] Output: Validation result (success / failure)

[1965] Server: If validation is successful, authenticate the user, generate a session ID and save it in the Redis session store.

[1966] Output: Session ID

[1967] User: If authentication is successful, the dashboard screen will be displayed. From this screen, various services can be accessed.

[1968] Step 2: Enter patient information

[1969] Terminal: The pharmacist inputs and transmits the patient's medical history and prescription information. The patient's medical history includes past illnesses, allergies, and past prescription information.

[1970] Input: Patient medical history and prescription information

[1971] Server: Stores the received patient information in a data storage medium (e.g., PostgreSQL) and performs data preprocessing (data format conversion and normalization) using Python scripts.

[1972] Input: Received patient information

[1973] Data processing: data format conversion, normalization

[1974] Output: Preprocessed patient information

[1975] Server: Stores the preprocessed data in a database so that it can be used in the next step.

[1976] Step 3: Recognize emotions

[1977] Terminal: As the patient enters information, the emotion analysis unit analyzes their voice tone and facial expressions to generate emotion data in real time. For example, emotion analysis can be performed by combining voice recognition and facial recognition technology.

[1978] Input: User's voice and facial expressions

[1979] Server: Stores the generated emotion data in a database (e.g., MongoDB) and sends it to the AI ​​model.

[1980] Input: Generated emotion data

[1981] Data processing: Emotion recognition through voice and facial expression analysis

[1982] Output: Emotion data

[1983] Step 4: Prescription suggestions using AI models

[1984] Server: Sends preprocessed patient information and emotion data to an AI model (e.g., a TensorFlow model hosted on Google Cloud AI Platform).

[1985] Input: Preprocessed patient information, emotion data

[1986] Data Computation: Data Analysis with AI Models

[1987] AI model means: Analyzes the transmitted data and suggests the most appropriate type of medication, dosage, and timing. It also takes into account emotional data and makes suggestions based on the patient's condition.

[1988] Output: Optimal prescription suggestions

[1989] Server: Stores the generated prescription suggestions in a database and returns them to the user's terminal.

[1990] Step 5: View prescription suggestions

[1991] Terminal: The pharmacist's screen displays suggested medication information, allowing them to dispense the most appropriate medication. The user interface is designed to be intuitive and easy to operate.

[1992] Input: Prescription suggestions sent from the server

[1993] Output: Prescription suggestions displayed in the user interface

[1994] Step 6: Collaboration between online and home medical care

[1995] Device: The patient or doctor uses the telemedicine app to enter and send new medical data and prescription information.

[1996] Input: medical data, prescription information

[1997] Server: Receives medical data and stores it in a database.

[1998] Input: Received medical data

[1999] Output: Saved medical data

[2000] AI model means: Make new prescription suggestions based on received data.

[2001] Output: New recipe suggestions

[2002] Terminal: The suggestions are displayed on the doctor's or pharmacist's screen, who then decides on the final prescription and provides it to the patient.

[2003] Step 7: 24-hour support

[2004] Terminal: The patient or pharmacist enters the question into the inquiry form and submits it.

[2005] Input: Inquiry details

[2006] Server: Receives the query and stores it in a database (e.g., Elasticsearch).

[2007] Input: Received inquiry

[2008] Output: Saved inquiry details

[2009] Server: An automated response system using Dialogflow responds immediately to simple inquiries and transfers complex inquiries to support staff.

[2010] Output: Response, support ticket

[2011] Terminal: The user sees an automated response or a response from the support staff.

[2012] Input: Response from the server

[2013] Output: The response displayed in the user interface

[2014] (Application example 2)

[2015] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2016] Conventional medical suggestion systems have limitations in making suggestions based on a patient's medical history and prescription information, and do not adequately provide personalized medical services that take into account the patient's emotions and mental state. Furthermore, factory production lines are not optimized to take into account the emotional state of workers, and there is a need for methods to improve production efficiency and reduce worker stress. A system that solves these problems is needed.

[2017] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2018] In this invention, the server includes database means for storing patient medical history and prescription information, receiving means for receiving patient medical history and prescription information and saving it in the database means, AI model means for proposing optimal medications based on the saved medical history and prescription information, display means for displaying the suggested medication information, and emotion recognition means for recognizing worker emotions and optimizing production plans and lines based on that data. This enables personalized medical recommendations that take patient emotions into account, and further allows factory production lines to be optimized taking into account the emotional states of workers, thereby improving production efficiency and reducing worker stress.

[2019] "Patient medical history and prescription information" refers to the patient's medical history and drug use instructions issued by a doctor.

[2020] "Database means" refers to a system that has the function of accumulating and storing information and searching and retrieving it as needed.

[2021] The "receiving means" is a device or function that has the function of receiving data from the outside and transmitting it to the internal system.

[2022] "AI model means" is a function that uses a machine learning algorithm to realize an artificial intelligence model that analyzes input data and generates results.

[2023] "Display means" refers to a device or function for visually displaying output information from the system.

[2024] "Worker emotions" refers to the emotional state of workers engaged in their daily work, and is data that requires analysis.

[2025] "Emotion recognition means" refers to a function for analyzing and recognizing emotions using voice recognition, facial expression recognition, and other sensor technologies.

[2026] A "production plan" is a plan for setting the schedule and methods of the production process.

[2027] "Line optimization" refers to the coordination and management of a production line to maximize its efficiency.

[2028] The present invention provides a system that suggests optimal medications based on a patient's medical history and prescription information, and a system that recognizes the emotional state of workers on a factory production line and performs production planning and line optimization. The following describes in detail the implementation of each element of the system and the program processing.

[2029] System Configuration

[2030] The system includes the following means:

[2031] 1. Database Means

[2032] The server's database stores patient medical history and prescription information along with a unique ID. This database also stores past prescription information, allergy information, and medical history, and is used as input data for analysis by the AI ​​model.

[2033] 2. Receiving Method

[2034] The server receives patient medical history and prescription information over the Internet and stores it in a database means in real time.

[2035] 3. AI Model Means

[2036] This method suggests the optimal type of medication, dosage, and timing based on the patient's medical history and prescription information. It uses machine learning algorithms to analyze multiple parameters to make optimal suggestions. It also includes integration with emotion recognition methods, which adjust the suggestions based on the user's emotional data.

[2037] 4. Display means

[2038] The suggested medication information is intuitively displayed on the device's display or smartphone, allowing pharmacists to provide the most appropriate medication to patients.

[2039] 5. Emotion recognition means

[2040] The server uses cameras, voice recognition, and facial expression recognition software to analyze workers' emotions in real time, and can use this emotional data to adjust production line speeds and optimize work assignments.

[2041] Program processing explanation

[2042] Hardware

[2043] Camera: Captures worker facial expressions in real time.

[2044] Server: Stores data, analyzes it, and generates suggestions.

[2045] Device (smartphone, tablet, PC): Enters and displays information.

[2046] software

[2047] OpenCV: Image processing and face recognition.

[2048] TensorFlow (Keras): Running an emotion recognition model.

[2049] Database system (e.g., SQLite): patient and worker data storage.

[2050] Machine learning algorithms: Building and analyzing AI models.

[2051] Specific examples

[2052] 1. Optimizing prescriptions for diabetes patients

[2053] The pharmacist enters the patient's medical history and new prescription information from the terminal, which is then sent to the server and stored in a database.

[2054] The AI ​​modeling tool analyzes this data and suggests optimal insulin dosages and schedules, and if the emotion recognition tool determines that additional support is needed, it will provide more detailed support suggestions.

[2055] The suggestions are displayed on the terminal screen, and the pharmacist makes the final decision.

[2056] 2. Factory production line optimization

[2057] The emotion recognition means collects and analyzes the emotional data of workers. If the worker's stress level is high, the server adjusts and optimizes the speed of the production line.

[2058] Emotional data is analyzed at regular intervals, and production plans are revised based on the emotional state.

[2059] Prompt Sentence Examples

[2060] - "Write a program that analyzes the facial expressions of factory workers and optimizes the speed of the production line based on their emotional state."

[2061] This system will enable personalized medical recommendations that take into account the patient's emotions, and will also enable optimization of factory production lines that take into account the emotional state of workers, thereby improving production efficiency and reducing worker stress.

[2062] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2063] Step 1:

[2064] The server receives the patient's medical history and prescription information from the terminal. The data entered on the terminal (input: patient's medical history and prescription information) is sent to the server via the Internet. The server converts and normalizes the received data and stores it in a database (output: normalized patient information).

[2065] Step 2:

[2066] The server passes the stored data to an AI model means to generate optimal medication suggestions. The server retrieves patient information from a database (input: patient information from database), analyzes the data using machine learning algorithms, and suggests medication types, dosages, and timing (output: optimal medication suggestions).

[2067] Step 3:

[2068] The terminal receives the drug suggestion information from the server and presents it to the pharmacist through the display means. The terminal receives and displays the suggested drug information (input: suggested information from the server) and displays it on the screen in a format that is easy for the user to understand (output: presented drug information).

[2069] Step 4:

[2070] The emotion recognition means captures the facial expressions of workers in real time through a camera. The emotion recognition means analyzes the captured video data (input: video data from the camera) and extracts facial expression features (output: facial expression feature data).

[2071] Step 5:

[2072] The server recognizes emotions based on the facial expression feature data sent from the emotion recognition means. The server receives the feature data (input: facial expression feature data), analyzes it using an emotion recognition model such as TensorFlow, and estimates the worker's emotional state (output: emotion data).

[2073] Step 6:

[2074] The server optimizes the speed of the production line and the allocation of work based on the emotional data. The server retrieves emotional data from a database over a certain period of time (input: past emotional data) and determines the optimal production plan based on the results of an analysis of the data with the current emotional data (output: optimized production plan).

[2075] Step 7:

[2076] The terminal receives the optimization results of the production plan from the server and transmits them to the production line control device. The terminal receives the optimization data from the server (input: production plan data from the server) and sends it to the production line control device (output: instructions to the control device).

[2077] Step 8:

[2078] The production line control device adjusts the speed of the production line and the allocation of work in accordance with instructions from the terminal. The control device adjusts the speed of the production line based on the input optimization instructions (input: instructions from the terminal) to improve production efficiency (output: optimized production line).

[2079] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such...

Claims

1. database means for storing patient medical history and prescription information; receiving means for receiving patient medical history and prescription information and storing the same in a database means; An AI model means to suggest optimal medications based on stored medical history and prescription information; a display means for displaying the suggested drug information; A system including:

2. 2. The system according to claim 1, further comprising additional receiving means for receiving medical data input by a patient or a doctor through online medical care or home medical care and storing the medical data in the database means.

3. an inquiry receiving means for receiving an inquiry from a patient or a pharmacist; 2. The system according to claim 1, further comprising a support means for providing an automatic response or manual support to the received inquiries.

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