System

Abstract

To provide a system for enabling a user to obtain the proposal of a proper drug on the market, the risk check of interaction and the analysis of a side effect risk only by inputting his or her own symptom or drug information during taking, and to select and take the drug without anxiety.SOLUTION: An input unit configured to input information on a drug being taken by a user and a symptom of the drug; a receiving unit configured to receive information transmitted from the input unit; A system comprising: a proposal means for proposing an appropriate commercially available drug based on a symptom; an interaction check means for checking an interaction between a drug being taken and a proposed commercially available drug; a side effect risk analysis means for analyzing a side effect risk based on personal information of a user; an information generation means for summarizing the analysis result and providing the analysis result to the user; SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With the recent diversification of pharmaceuticals, it has become increasingly important to identify interactions between drugs users are taking and the risk of side effects to specific drugs based on an individual's constitution. However, many users do not have easy access to this information, making it difficult to select the appropriate medication. Furthermore, suggesting appropriate over-the-counter medications based on symptoms requires specialized knowledge, which could lead to health risks due to incorrect drug selection or use. Against this background, there is a demand for a system that allows users to easily obtain reliable information. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: an input means for a user to input information about drugs being taken and symptoms; a receiving means for receiving information transmitted from the input means; a suggestion means for analyzing the received information and suggesting appropriate over-the-counter drugs based on the symptoms; an interaction checking means for checking interactions between the drugs being taken and the suggested over-the-counter drugs; a side effect risk analysis means for analyzing the risk of side effects based on the user's personal information; an information generating means for compiling the analysis results and providing them to the user; a transmitting means for transmitting the information generated by the information generating means to the user's terminal; and a display means for displaying the information transmitted by the transmitting means on the user's terminal. The present invention allows a user to select and take drugs with confidence by simply inputting their symptoms and information about the drugs being taken, and receives suggestions for appropriate over-the-counter drugs, a drug interaction risk check, and an analysis of the risk of side effects.

[0006] "User" refers to the person who uses this system and enters information about symptoms and medications being taken.

[0007] "Input means" refers to a device or interface through which a user inputs information, including, for example, a smartphone or computer application.

[0008] "Receiving means" refers to the function of transmitting information entered by the user to the server and receiving the information on the server side.

[0009] "Recommendation means" refers to an algorithm or function for suggesting appropriate over-the-counter medications to address symptoms based on the received information.

[0010] "Interaction checking means" refers to a function that checks for interactions between medications currently being taken and proposed over-the-counter medications and assesses the risks.

[0011] "Side effect risk analysis means" refers to a function for assessing the risk of side effects based on the user's personal information and information on the ingredients of pharmaceutical products.

[0012] "Information generation means" refers to the function of compiling the results of the proposal means, interaction check means, and side effect risk analysis means and arranging them into an information format to be provided to users.

[0013] "Transmission means" refers to a function for transmitting the generated information to the user's terminal.

[0014] "Display means" refers to a function for displaying transmitted information on the user's terminal. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a drug management system that utilizes generative AI, and by having users input information about the drugs they are taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, and analyzes the risk of side effects. This system is implemented as follows.

[0037] 1. Enter user information

[0038] 1.1 Application launch

[0039] The user launches the application on their smartphone or computer, and the home screen of the application displays a form for entering information about symptoms, medications they are taking, and allergies.

[0040] 1.2 Entering symptoms and medication information

[0041] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. The device temporarily stores the entered data and prepares it for transmission.

[0042] 1.3 Data Transmission

[0043] The terminal transmits the information entered by the user to the server using a secure communication protocol.

[0044] 2. Data processing and analysis

[0045] 2.1 Data Reception

[0046] The server receives the information sent from the device, including the user's symptoms, medications they are taking, and allergy information.

[0047] 2.2 Symptom-based medication suggestions

[0048] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[0049] 2.3 Drug interaction check

[0050] The server checks for interactions between the current medication (losartan) and the proposed over-the-counter medications (e.g., ibuprofen, acetaminophen). Using an interaction risk assessment algorithm, the server evaluates the interaction between each over-the-counter medication and losartan. The server determines that ibuprofen requires caution due to its high risk of interaction with losartan.

[0051] 2.4 Analysis of side effect risks

[0052] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[0053] 2.5 Prophylactic drug production

[0054] The server will suggest appropriate preventive medicines (herbal medicines or supplements) based on individual user information, past data, and current health status. It will then extract candidate preventive medicines from a database and present the most appropriate one.

[0055] 3. Returning the results

[0056] 3.1 Generating Results

[0057] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "care should be taken with ibuprofen" due to the risk of interactions with antihypertensive drugs.

[0058] 3.2 Data Transmission

[0059] The server sends the generated information to the user's terminal using a secure communication protocol.

[0060] 4. Display and notification of results

[0061] 4.1 Displaying the results

[0062] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[0063] 4.2 Caution Notice

[0064] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0065] Specific examples

[0066] User example: Hanako

[0067] 1. Hanako launches the app and wants to treat her headache.

[0068] 2. Hanako enters "headache," "antihypertensive medication currently being taken: losartan," and "no allergies" into the form on the home screen.

[0069] 3. The device sends this information to the server.

[0070] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0071] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[0072] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0073] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[0074] 8. The device will display this information to Mary and send her notifications if necessary.

[0075] This will allow Hanako to choose and take the appropriate medication with peace of mind.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The user launches the application and the home screen appears.

[0079] Step 2:

[0080] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into the form.

[0081] Step 3:

[0082] The terminal temporarily stores the input data and prepares it for transmission.

[0083] Step 4:

[0084] The terminal transmits the user's input information to the server.

[0085] Step 5:

[0086] The server receives information from the device, including symptoms, medications being taken, and allergy information.

[0087] Step 6:

[0088] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[0089] Step 7:

[0090] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0091] Step 8:

[0092] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[0093] Step 9:

[0094] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[0095] Step 10:

[0096] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[0097] Step 11:

[0098] The server compiles the analysis results and generates symptom-based medication suggestions, interaction check results, and information on side effect risks.

[0099] Step 12:

[0100] The server transmits the generated information to the terminal using a secure communication protocol.

[0101] Step 13:

[0102] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[0103] Step 14:

[0104] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0105] Example 1

[0106] 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."

[0107] Conventional drug management systems suggest appropriate over-the-counter medications and check for drug interactions based on the drug information and symptoms entered by the user, but they often do not adequately suggest preventative medications or provide additional warnings. As a result, users may not receive sufficient information about the risk of side effects and drug interactions, leading to inappropriate drug selection. Furthermore, there were few systems that took into account interactions with multiple drugs and foods. The purpose of this invention is to solve these problems and realize safe and effective drug management.

[0108] 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.

[0109] In this invention, the server includes an input means for the user to input information about medications being taken and symptoms, a preventive medicine generation means for generating an appropriate preventive medicine based on the received information and analysis results, and a notification means for notifying the user of additional precautions. This makes it possible to suggest safe and effective over-the-counter medications based on the information input by the user, analyze drug interactions and side effect risks, and even generate preventive medicines and provide warnings.

[0110] The "input means" is an interface that allows the user to input information about medications currently being taken, symptoms, allergies, and the like.

[0111] The "receiving means" is a function for receiving information sent from the input means.

[0112] The "suggestion means" is a function for analyzing the received information and suggesting appropriate over-the-counter medications based on the symptoms.

[0113] The "interaction checker" is a function for checking interactions between the medication being taken and proposed over-the-counter medications, multiple medications, and foods.

[0114] The "side effect risk analysis means" is a function for analyzing the risk of side effects based on the user's personal information.

[0115] The "prophylactic medicine generating means" is a function for generating an appropriate prophylactic medicine based on the received information and analysis results.

[0116] The "information generating means" is a function that compiles the analysis results and the generated preventive medicines, and generates information to be provided to the user.

[0117] The "transmission means" is a function for transmitting the information generated by the information generation means to the user's terminal.

[0118] The "display means" is a function for displaying the information transmitted by the transmission means on the user terminal.

[0119] "Notification means" is a function for notifying the user of additional precautions.

[0120] This invention is a drug management system that utilizes a generative AI model. By inputting information about the drugs a user is taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, analyzes the risk of side effects, and even generates preventative medications and provides necessary warnings. This system is implemented as follows.

[0121] Enter user information

[0122] The user launches the application on their smartphone or computer and enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into a form displayed on the home screen. The device temporarily stores this information and prepares it for transmission. When the user taps the send button, the input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0123] Data processing and analysis

[0124] The server receives user information sent from the device. Based on the received information, it suggests appropriate over-the-counter medications to address the patient's condition. Specifically, it retrieves information on the relevant over-the-counter medications from a database and selects the best medication based on the patient's symptoms. It then uses an interaction risk assessment algorithm to check for interactions between the current medication and the suggested over-the-counter medication. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). The side effect risk assessment module compares the results with the risk database to make an assessment.

[0125] The server also suggests appropriate preventive medicines (e.g., herbal medicines or supplements) based on the received information, past data, and current health status. Using a preventive medicine generation means, it retrieves candidate preventive medicines from the database and presents the most suitable one.

[0126] Returning the results

[0127] The server compiles the analysis results and generated preventive medication information and generates information to provide to the user, including recommended medications for symptoms and associated precautions. The generated information is then sent to the user's device using a secure communication protocol.

[0128] Displaying and notifying results

[0129] The device displays the received information to the user. Specifically, it displays "Acetaminophen is recommended. Consult your doctor before using ibuprofen." It also uses notification functionality to provide additional warnings to the user as needed, such as providing a link to purchase the recommended medication or other important information.

[0130] Overview of the hardware and software used

[0131] Hardware

[0132] Smartphones (general mobile devices)

[0133] A computer (typical desktop or laptop)

[0134] Server (cloud computing environment)

[0135] software

[0136] Mobile applications (general cross-platform development frameworks)

[0137] Web applications (general front-end frameworks)

[0138] Server-side software (popular back-end frameworks and scripting languages)

[0139] Database (general relational database management system)

[0140] Secure communication protocols (HTTPS, SSL / TLS)

[0141] Specific examples

[0142] User example: User A

[0143] 1. User A launches the app and wants to treat a headache.

[0144] 2. User A enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen.

[0145] 3. The device sends this information to the server.

[0146] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0147] 5. The server checks for interactions with the blood pressure medication the patient is already taking and determines that acetaminophen is a safe option.

[0148] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0149] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[0150] 8. The device displays this information to User A and also sends notifications if necessary.

[0151] This allows user A to select and take the appropriate medication with peace of mind.

[0152] Example prompt: "Enter your current symptoms and medications to identify potential interactions and risk of side effects. For example, 'I have a headache and am taking blood pressure medication. Can I take over-the-counter acetaminophen?'"

[0153] The above is a specific embodiment of this medicine management system.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] The user launches the application. The app's home screen appears, providing a form for entering symptoms, medications, and allergies. The information entered is temporarily stored on the device for future submission.

[0157] Specific actions

[0158] The user taps the app icon.

[0159] The home screen will be displayed.

[0160] The input form will be displayed.

[0161] The user enters information using a keyboard or touch screen.

[0162] The terminal temporarily stores the input data in memory.

[0163] Input: User's symptoms, medications, and allergy information

[0164] Output: Temporarily saved input data

[0165] Step 2:

[0166] When the user taps the submit button, the entered information is sent to the server using a secure communication protocol (e.g., HTTPS).

[0167] Specific actions

[0168] The user taps the send button.

[0169] The terminal prepares the data for transmission and encrypts it.

[0170] The encrypted data is sent to the server.

[0171] Input: Temporarily saved input data, submit button tap event

[0172] Output: Encrypted data sent to the server

[0173] Step 3:

[0174] The server receives the information sent from the device. The received information includes the user's symptoms, medications they are taking, and allergy information. The received data is stored in a database for analysis.

[0175] Specific actions

[0176] A data reception module runs on the server.

[0177] The server decrypts the transmitted data.

[0178] The received data is stored in a database.

[0179] Input: Encrypted user data

[0180] Output: User data stored in the database

[0181] Step 4:

[0182] The server analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms. It also retrieves information on the corresponding over-the-counter medications from a database and selects the most appropriate medication.

[0183] Specific actions

[0184] The server uses the user's symptoms as a database search query.

[0185] Obtain a list of over-the-counter medications that address your symptoms from the database.

[0186] An algorithm selects the over-the-counter medication that best suits your symptoms.

[0187] Input: User's symptoms, medications, and allergy information stored in the database

[0188] Output: List of over-the-counter drugs and best over-the-counter drug information

[0189] Step 5:

[0190] The server uses an interaction risk assessment algorithm to check for interactions between current medications and proposed over-the-counter medications.

[0191] Specific actions

[0192] The server lists the medications you are taking and suggested over-the-counter medications.

[0193] Run the interaction risk assessment algorithm.

[0194] Generates an alert if a risk is detected.

[0195] Input: List of over-the-counter medications, information on medications currently being taken

[0196] Output: Interaction risk assessment results and warning information

[0197] Step 6:

[0198] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), and evaluates the risk using a side effect risk assessment module.

[0199] Specific actions

[0200] The server passes user information to the side effect risk assessment module.

[0201] The side effect risk assessment module checks against the risk database.

[0202] Generate risk assessment results.

[0203] Input: User's personal information stored in the database

[0204] Output: Side effect risk assessment results

[0205] Step 7:

[0206] The server generates an appropriate preventive medicine based on the received information, past data, and current health status. Using the preventive medicine generation means, candidate preventive medicines are extracted and the most suitable one is presented.

[0207] Specific actions

[0208] The server uses historical data and current information to generate a list of preventative medication candidates.

[0209] The preventive medicine generating means selects the optimum preventive medicine.

[0210] Input: User information stored in the database, past data

[0211] Output: List of preventive drug candidates and information on the optimal preventive drug

[0212] Step 8:

[0213] The server compiles the analysis results and information about the generated preventive medications, and generates information to provide to the user, including recommended medications for the symptoms and associated precautions.

[0214] Specific actions

[0215] The server aggregates the results of each analysis.

[0216] Generate user-friendly information in the form of text, charts, etc.

[0217] Input: Analysis results, preventive medicine information

[0218] Output: Information provided to the user (recommended medications, precautions, etc.)

[0219] Step 9:

[0220] The server transmits the generated information to the user's terminal using a secure communication protocol.

[0221] Specific actions

[0222] The server encrypts the data and prepares it for transmission.

[0223] The encrypted data is sent to the terminal.

[0224] Input: Information to be provided to the user (recommended medications, precautions, etc.)

[0225] Output: Encrypted information sent to the user's device

[0226] Step 10:

[0227] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[0228] Specific actions

[0229] The terminal decrypts the received data.

[0230] Present the data in a format suitable for the user interface.

[0231] Input: Encrypted information

[0232] Output: Information displayed to the user

[0233] Step 11:

[0234] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0235] Specific actions

[0236] The device generates a notification.

[0237] Notify the user in the form of a popup or message.

[0238] Input: Notification information to provide to users

[0239] Output: Warnings and links notified to the user

[0240] The above are the specific processing steps of the medicine management system of the present invention.

[0241] (Application example 1)

[0242] 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."

[0243] Currently, drug selection, interaction checks, and side effect risk analysis must be done individually, which is cumbersome for users. Even when an appropriate drug is presented, the purchasing process is often time-consuming and laborious. This makes it difficult to obtain over-the-counter drugs quickly and safely.

[0244] 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.

[0245] In this invention, the server includes an input means for inputting information about drugs being taken by the user and symptoms, a receiving means for receiving information transmitted from the input means, a suggestion means for analyzing the received information and suggesting appropriate over-the-counter drugs based on the symptoms, an interaction checking means for checking interactions between the drugs being taken and the suggested over-the-counter drugs, a side effect risk analysis means for analyzing the risk of side effects based on the user's personal information, an information generating means for summarizing the analysis results and providing them to the user, a transmitting means for transmitting the information generated by the information generating means to the user's terminal, a display means for displaying the information transmitted by the transmitting means on the user's terminal, and an electronic payment means for quickly and safely purchasing the suggested over-the-counter drugs. This enables a unified process from drug selection, interaction checking, and side effect risk analysis to the prompt purchase of appropriate over-the-counter drugs.

[0246] "Input means" refers to a device or interface that allows a user to input information such as information about medications being taken and symptoms.

[0247] The "receiving means" is a device or system that allows the server to receive the information transmitted from the input means.

[0248] The "suggestion means" is a device or system that analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms.

[0249] An "interaction checking means" is a device or system for checking interactions between medications currently being taken and proposed over-the-counter medications and conducting risk assessments.

[0250] "Side effect risk analysis means" refers to a device or system for analyzing and evaluating side effect risks based on the user's personal information.

[0251] The "information generating means" is a device or system that generates information to be provided to users based on the analysis results.

[0252] The "transmission means" is a device or system for transmitting the information generated by the information generation means to the user's terminal.

[0253] "Display means" refers to a device or interface for displaying the transmitted information on the user's terminal.

[0254] "Electronic payment method" means an electronic payment method or system for the fast and secure purchase of the proposed over-the-counter drug.

[0255] This invention is a drug management system that utilizes generative AI, and can suggest appropriate over-the-counter drugs, check for drug interactions, and analyze the risk of side effects based on the drug information and symptoms entered by the user. It also has an electronic payment method for quickly and safely purchasing the suggested over-the-counter drugs.

[0256] Overall system configuration

[0257] The system consists of the following elements:

[0258] 1. Input method: An interface (e.g., smartphone app) through which users can input information about symptoms and medications they are taking.

[0259] 2. Receiving means: A server system (using a Flask application) that receives information sent from the input means.

[0260] 3. Recommendation method: A system that analyzes the received information and suggests over-the-counter medications that address the symptoms.

[0261] 4. Interaction Checking Measures: A system to check for interactions between medications currently being taken and proposed over-the-counter medications.

[0262] 5. Side effect risk analysis means: A system that analyzes the risk of side effects based on the user's personal information.

[0263] 6. Information generation means: A system that generates information to be provided to users based on the analysis results.

[0264] 7. Transmission means: A system that transmits the information generated by the information generation means to the user's terminal.

[0265] 8. Display means: An interface that displays the transmitted information on the user's terminal.

[0266] 9. Electronic Payment Methods: An electronic payment system for fast and secure purchase of proposed over-the-counter medications.

[0267] Data processing and analysis description

[0268] The server receives the drug information and symptoms sent by the user and analyzes that information using a generative AI model. Specifically, it retrieves appropriate over-the-counter drugs from a database based on the symptoms and checks for interactions between each drug and any other medications the user is currently taking. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.).

[0269] The information generation means compiles these analysis results and generates information including recommended medicines and precautions for the user. The generated information is sent to the user's terminal via a secure communication protocol.

[0270] The electronic payment method provides a payment interface that includes multiple payment methods (credit cards, electronic money, QR code payment, etc.) so that users can instantly purchase the suggested over-the-counter medications.

[0271] Examples of concrete examples and prompts

[0272] As a concrete example, let's say a user wants to treat a "headache." In this case, the user launches the app and enters "headache," "current antihypertensive medication: Losartan," and "no allergies." The device sends this information to the server, which analyzes it and notifies the user that acetaminophen is recommended. The user can also quickly complete the purchase process within the app.

[0273] An example prompt has the following format:

[0274] "Please enter your symptoms and medications:"

[0275] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0276] Step 1:

[0277] The user launches the smartphone app and inputs symptoms, medication information, and allergy information. The user enters this information into the input form and presses the send button, temporarily saving the data on the device. The input data includes symptoms (e.g., headache), medications being taken (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies). The output is the input data temporarily saved on the device.

[0278] Step 2:

[0279] The terminal sends the information entered by the user to the server using a secure communication protocol (e.g., TLS / SSL). At this time, the terminal converts the temporarily saved data into JSON format and sends it as an HTTPS request. The input is the temporarily saved data, and the output is the data sent to the server.

[0280] Step 3:

[0281] The server receives the information sent from the device and stores it in a database. At the same time, it begins analyzing the received data. Specifically, it retrieves a list of over-the-counter medications that match the symptoms from the database and proceeds with the analysis. The input is the data sent from the device, and the output is the list of over-the-counter medications from the database used for analysis.

[0282] Step 4:

[0283] The server uses a generative AI model to suggest appropriate over-the-counter medications based on the received data (user symptoms, medications being taken, and allergy information). This generative AI model analyzes over-the-counter medication information corresponding to symptoms in the database and makes an appropriate selection. The input is symptoms and medication information, and the output is a list of suggested over-the-counter medications.

[0284] Step 5:

[0285] The server checks interactions between the proposed over-the-counter drugs and the medications the user is currently taking. To do this, it uses a pre-built interaction checking algorithm to evaluate the interaction risk between each over-the-counter drug and the medication the user is currently taking. The input is the list of proposed over-the-counter drugs and information about the medications the user is currently taking, and the output is the evaluation result of the interaction risk.

[0286] Step 6:

[0287] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). It obtains information about side effects from the database and performs a risk assessment based on that information. The input is personal information and drug information, and the output is the assessment result of the side effect risk.

[0288] Step 7:

[0289] The server generates information to provide to users based on these analysis results. The generated information includes suggested over-the-counter medications and precautions. The information generation means uses a generative AI model to appropriately summarize the analysis results and convert them into a format optimized for user viewing. The input is the analysis results, and the output is the generated information.

[0290] Step 8:

[0291] The server sends the generated information to the user's device using a secure communication protocol. At this time, the generated information is sent in an appropriate format (e.g., JSON format). The input is the generated information, and the output is the sent information. TLS / SSL is used for transmission to ensure data security.

[0292] Step 9:

[0293] The device displays the received information to the user, specifically, the suggested over-the-counter drug information and warnings using an appropriate interface, and sends notifications as needed. The input is the received information, and the output is the displayed information and sent notifications.

[0294] Step 10:

[0295] The user then goes through the process of purchasing the over-the-counter medication suggested within the app. The payment is completed quickly and securely using electronic payment methods. Various payment methods (credit cards, electronic money, QR code payment, etc.) are integrated into the interface. The input is the intention to purchase and payment information, and the output is a notification that the purchase has been completed.

[0296] 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.

[0297] The present invention is a system that provides advice and drug suggestions according to the user's mental state by combining a drug management system that utilizes generative AI with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[0298] 1. Enter user information

[0299] 1.1 Application launch

[0300] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[0301] 1.2 Entering symptoms and medication information

[0302] The user enters their symptoms (e.g., headache), medications (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[0303] 1.3 Data Transmission

[0304] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[0305] 2. Data processing and analysis

[0306] 2.1 Data Reception

[0307] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[0308] 2.2 Symptom-based medication suggestions

[0309] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[0310] 2.3 Emotion Analysis

[0311] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[0312] 2.4 Drug interaction check

[0313] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0314] 2.5 Analysis of side effect risks

[0315] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[0316] 2.6 Adjusting Suggestions Based on Emotions

[0317] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is experiencing high stress, it will provide medications and advice that have a calming effect.

[0318] 3. Returning the results

[0319] 3.1 Generating Results

[0320] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It also includes advice based on the patient's emotional state.

[0321] 3.2 Data Transmission

[0322] The server sends the generated information to the user's terminal using a secure communication protocol.

[0323] 4. Display and notification of results

[0324] 4.1 Displaying the results

[0325] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[0326] 4.2 Caution Notice

[0327] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0328] Specific examples

[0329] User example: Hanako

[0330] 1. Hanako launches the app and wants to treat her headache.

[0331] 2. Hanako enters "headache," "hypertensive medication currently being taken: Losartan," and "no allergies" into the form on the home screen. She also uses voice input to say, "I've been feeling stressed lately."

[0332] 3. The device sends this information to the server.

[0333] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0334] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[0335] 6. The server analyzes the emotional data and recognizes that Hanako is under a lot of stress.

[0336] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0337] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[0338] 9. The device will display this information to Mary and send her notifications if necessary.

[0339] This allows Hanako to choose and take the appropriate medication with peace of mind, and also receive advice tailored to her own mental state.

[0340] The processing flow will be explained below.

[0341] Step 1:

[0342] When a user launches the application, they are presented with a home screen, which includes a form where they can enter information about their symptoms, medications, and allergies. Additionally, a voice input button is provided to visualize their emotional state.

[0343] Step 2:

[0344] The user inputs symptoms into the form. For example, the user inputs "headache." The user also selects "Losartan, an antihypertensive drug" as the medication currently being taken, and selects "No allergies" as the allergy information.

[0345] Step 3:

[0346] The user presses the voice input button and provides an emotional state through voice input, for example, saying, "I've been feeling stressed lately."

[0347] Step 4:

[0348] The device temporarily stores the entered data (symptoms, medications, allergy information, and voice data) and prepares it for transmission.

[0349] Step 5:

[0350] The device sends the user's input information and voice data to the server, using a secure communication protocol to keep the data safe.

[0351] Step 6:

[0352] The server receives information from the device, including symptoms, medications being taken, allergy information, and emotional data.

[0353] Step 7:

[0354] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[0355] Step 8:

[0356] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0357] Step 9:

[0358] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[0359] Step 10:

[0360] The server analyzes the voice input data and recognizes the user's emotional state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[0361] Step 11:

[0362] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[0363] Step 12:

[0364] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[0365] Step 13:

[0366] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if stress levels are high, it will provide medications and advice with a calming effect.

[0367] Step 14:

[0368] The server compiles the analysis results and generates information including appropriate medication suggestions, precautions, and emotion-based advice.

[0369] Step 15:

[0370] The server sends the generated information to the user's terminal using a secure communication protocol.

[0371] Step 16:

[0372] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[0373] Step 17:

[0374] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0375] Example 2

[0376] 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."

[0377] Conventional drug management systems suggest appropriate over-the-counter medications based on the user's current medication information and symptoms, but they are unable to provide advice that takes into account the user's emotional state. As a result, it is difficult for users to receive appropriate advice when they are stressed or mentally unstable. In addition, there is a need to improve safety due to insufficient analysis of drug interactions and side effect risks.

[0378] 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.

[0379] In this invention, the server includes input means for inputting information about medications the user is taking, symptoms, and emotional state, receiving means for receiving information transmitted from the input means, suggestion means for analyzing the received information and suggesting appropriate medications based on the symptoms, interaction checking means for checking interactions between the medications the user is taking and the suggested medications, side effect risk analysis means for analyzing the risk of side effects based on the user's personal information and emotional state, information generation means for summarizing the analysis results and providing them to the user, transmission means for transmitting the information generated by the information generation means to the user's terminal, and display means for displaying the information transmitted by the transmission means on the user's terminal. This enables accurate advice and medication suggestions based on the user's emotional state, thereby improving safety and psychological support.

[0380] "User" refers to a person who uses the system to input their symptoms, information about the medications they are taking, and their emotional state, and receives appropriate medication suggestions and advice.

[0381] "Drug information" refers to detailed information such as the name, dosage, and usage instructions of the medications the user is currently taking.

[0382] "Symptom" means information that describes a physical or mental illness or condition that a user is currently experiencing.

[0383] "Emotional state" refers to information that indicates a user's current state of mind or emotion, such as stress, anxiety, or calmness.

[0384] "Input means" refers to the interface through which users input their medication information, symptoms, and emotional state into the system. Specifically, this includes form entry and voice input functions.

[0385] The "receiving means" refers to a function that allows the system to acquire information sent from the input means.

[0386] The "suggestion means" refers to a function for analyzing the information acquired by the receiving means and suggesting an appropriate medication based on the symptoms of the user.

[0387] "Interaction checking means" refers to a function for analyzing interactions between the medications the user is taking and the proposed medication and assessing the risks.

[0388] "Side effect risk analysis means" refers to a function for assessing the side effect risk of a proposed drug based on the user's personal information and emotional state.

[0389] "Information generation means" refers to the function by which the system compiles the analysis results and generates information to be provided to users.

[0390] The "transmission means" refers to a function for transmitting the information generated by the information generation means to the user's terminal.

[0391] The "display means" refers to a function for displaying the information transmitted by the transmission means on the user terminal.

[0392] "Database" refers to an information management system that stores information on drugs, interactions, risk of side effects, etc., and makes it accessible to the system as needed.

[0393] "HTTPS" is a protocol for securely sending and receiving data over the Internet, and refers to technology that ensures data protection.

[0394] MODE FOR CARRYING OUT THE INVENTION

[0395] This invention is a system that provides advice and drug suggestions based on the user's mental state by combining a drug management system that utilizes a generative AI model with an emotion engine that recognizes the user's emotions. This system is implemented as an application that can be accessed from a smartphone or computer.

[0396] 1. Enter user information

[0397] 1.1 Application launch

[0398] Users launch the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[0399] 1.2 Entering symptoms and medication information

[0400] The user enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensives), and allergy information (e.g., no allergies) into the form. They also use voice input to provide emotional status information, such as "I've been feeling stressed lately." The device temporarily stores the entered data and prepares it for transmission.

[0401] 1.3 Data Transmission

[0402] The device transmits the information and emotion data entered by the user to the server using secure communication technology such as the HTTPS protocol.

[0403] 2. Data processing and analysis

[0404] 2.1 Data Reception

[0405] The server receives the information sent from the terminal, including the user's symptoms, medications being taken, allergy information, and emotional data.

[0406] 2.2 Symptom-based medication suggestions

[0407] The server analyzes the received information and suggests appropriate medications for the user's symptoms. For example, it retrieves information on medications for "headache" (e.g., acetaminophen, ibuprofen) from a database and selects the most appropriate medication.

[0408] 2.3 Emotion Analysis

[0409] The server uses an emotion engine to analyze emotion data, including voice input data, and recognize the user's mental state (e.g., stress, anxiety, calm).

[0410] 2.4 Drug interaction check

[0411] The server checks for interactions between current medications (e.g., antihypertensives) and proposed over-the-counter medications (e.g., acetaminophen, ibuprofen), and assesses the risk using a risk assessment algorithm.

[0412] 2.5 Analysis of side effect risks

[0413] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information about the risk of side effects from the database, and makes an assessment.

[0414] 2.6 Adjusting Suggestions Based on Emotions

[0415] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide advice on medications with a calming effect or to promote relaxation.

[0416] 3. Returning the results

[0417] 3.1 Generating Results

[0418] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[0419] 3.2 Data Transmission

[0420] The server sends the generated information to the user's device using secure communication technology such as the HTTPS protocol.

[0421] 4. Display and notification of results

[0422] 4.1 Displaying the results

[0423] The device then displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[0424] 4.2 Caution Notice

[0425] If necessary, the device will use notifications to provide additional user attention, such as providing links to purchase recommended medications or other important information.

[0426] Specific examples

[0427] User example: A user

[0428] 1. A user launches the app and wants to treat a headache.

[0429] 2. The user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[0430] 3. The device sends this information to the server.

[0431] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0432] 5. The server checks for interactions with antihypertensive medications and determines that acetaminophen is a safe option.

[0433] 6. The server analyzes the emotional data and recognizes that the user is under high stress.

[0434] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0435] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[0436] 9. The device displays this information to the user and also sends notifications if necessary.

[0437] This example allows users to choose and take appropriate medication with peace of mind, and also receive advice tailored to their own mental state.

[0438] Example prompts for generative AI models

[0439] Input prompt: "Please enter your headache symptoms and tell me about over-the-counter medications that can be taken with your current blood pressure medication. If you've been under a lot of stress lately, what medication would be appropriate?"

[0440] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0441] Step 1:

[0442] The user launches the application on their smartphone or computer. The home screen displays a form for entering symptoms, medication information, and allergy information. The user enters their symptoms (e.g., headache), medications (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into the form, and provides their emotional state, such as "I've been feeling stressed lately," via voice input. The entered data is temporarily stored on the device.

[0443] Input: User's symptoms, medications, allergy information, emotional state

[0444] Output: Saved user data

[0445] Step 2:

[0446] The device sends the stored user data to the server using the secure HTTPS protocol. The server receives and stores the information sent from the device.

[0447] Input: Saved user data

[0448] Output: User data stored on the server

[0449] Step 3:

[0450] The server analyzes the received information. First, it retrieves medication information corresponding to the symptoms from the database (e.g., acetaminophen or ibuprofen for headaches) and selects the most appropriate medication. Next, it converts the voice input data into text and uses an emotion engine to analyze the user's emotional state (e.g., stress, anxiety, calm).

[0451] Input: User data stored on the server

[0452] Output: Analysis results (suggested medication for symptoms and user's emotional state)

[0453] Step 4:

[0454] The server checks interactions between current medications (e.g., antihypertensives) and proposed over-the-counter drugs (e.g., acetaminophen, ibuprofen), queries an interaction database, and assesses the risk of interaction using a risk assessment algorithm.

[0455] Input: Analysis results (suggested medications), medication information

[0456] Output: Interaction check results (risk assessment)

[0457] Step 5:

[0458] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and uses a risk assessment model to select the most appropriate drug candidate.

[0459] Input: User's personal information, suggested medication

[0460] Output: Side effect risk assessment results

[0461] Step 6:

[0462] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide sedative medications and advice on how to relax.

[0463] Input: Side effect risk assessment results, emotion analysis results

[0464] Output: Tailored suggestions and advice

[0465] Step 7:

[0466] The server compiles the analysis results and generates information for the user, including suggestions for appropriate medications and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[0467] Input: Tailored suggestions and advice

[0468] Output: Generated suggestions and warning information

[0469] Step 8:

[0470] The server sends the generated information to the user's device using the secure HTTPS protocol, where it displays the received information to the user and, if necessary, uses a notification function to provide additional attention.

[0471] Input: Generated suggestions and warning information

[0472] Output: Information displayed to the user and any necessary notifications

[0473] (Application example 2)

[0474] 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."

[0475] Conventional drug management systems did not provide advice or suggestions based on the user's emotional state, and could only make uniform suggestions regardless of the user's mental state. This made it difficult to provide comprehensive health management that took into account mental factors such as stress and anxiety, and to suggest appropriate over-the-counter medications and dietary recommendations. Furthermore, drug-food interactions were often not sufficiently considered.

[0476] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a suggestion means that provides advice and dietary suggestions appropriate to the user's mental state based on emotion data analyzed using an emotion engine, a suggestion means that analyzes received information and suggests appropriate over-the-counter medications based on symptoms, and an interaction check means that checks interactions between medications currently being taken and the suggested over-the-counter medications. This enables comprehensive health management that takes the user's mental state into consideration.

[0477] The "input means" is a device through which the user inputs drug information, symptoms, allergy information, and emotions.

[0478] The "receiving means" is a device that receives information transmitted from the input means.

[0479] The "suggestion means" is a device that analyzes the received information and suggests appropriate over-the-counter medicines based on symptoms, as well as advice and diet appropriate to the patient's mental state based on emotional data.

[0480] An "interaction checking means" is a device that checks for interactions between a drug being taken and a proposed over-the-counter drug, as well as interactions with multiple drugs and foods.

[0481] The "side effect risk analysis means" is a device that analyzes the side effect risk based on the user's personal information.

[0482] An "emotion engine" is a program or device that analyzes emotional data such as voice input and understands the user's mental state.

[0483] The "information generating means" is a device that compiles the analysis results and generates information to be provided to the user.

[0484] The "transmitting means" is a device that transmits the information generated by the information generating means to the user's terminal.

[0485] The "display means" is a device that displays the information transmitted by the transmission means on the user terminal.

[0486] 1. Enter user information

[0487] 1.1 Application launch

[0488] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[0489] 1.2 Entering symptoms and medication information

[0490] The user enters their symptoms (e.g., headache), medications (e.g., antihypertensives), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[0491] 1.3 Data Transmission

[0492] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[0493] 2. Data processing and analysis

[0494] 2.1 Data Reception

[0495] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[0496] 2.2 Symptom-based medication suggestions

[0497] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the appropriate over-the-counter medications from a database and selects the best medication based on the symptoms.

[0498] 2.3 Emotion Analysis

[0499] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). It does this using an emotion engine.

[0500] 2.4 Drug interaction check

[0501] The server checks interactions between medications currently being taken (e.g., antihypertensives) and potential over-the-counter drugs (e.g., acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0502] 2.5 Analysis of side effect risks

[0503] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[0504] 2.6 Adjusting Suggestions Based on Emotions

[0505] The server then adjusts the selection of suggested over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is under high stress, it will provide medications and advice with a calming effect. It will also suggest easy-to-digest meals.

[0506] 3. Returning the results

[0507] 3.1 Generating Results

[0508] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, but note that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the patient's emotional state.

[0509] 3.2 Data Transmission

[0510] The server sends the generated information to the user's terminal using a secure communication protocol.

[0511] 4. Display and notification of results

[0512] 4.1 Displaying the results

[0513] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[0514] 4.2 Caution Notice

[0515] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0516] Specific examples

[0517] User example: General user

[0518] 1. A regular user launches the app and wants to treat a headache.

[0519] 2. A general user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[0520] 3. The device sends this information to the server.

[0521] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0522] 5. The server checks for interactions with the blood pressure medication being taken and determines that acetaminophen is a safe option.

[0523] 6. The server analyzes the emotional data and recognizes that the general user is under high stress.

[0524] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0525] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[0526] 9. The device displays this information to the general user and also sends notifications if necessary.

[0527] This allows general users to choose and take appropriate medication with peace of mind, and also receive advice based on their own mental state.

[0528] Prompt Sentence Examples

[0529] "I'm feeling stressed at work. Can you suggest some foods that will help relieve stress?"

[0530] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0531] Step 1:

[0532] The user launches the application on their smartphone or computer and inputs their symptoms, medication information, and allergy information. Using the input means, they enter this information into a form or record their emotional state using voice input. The input data is temporarily stored on the device.

[0533] input:

[0534] Symptoms (e.g. headache)

[0535] Medications you are taking (e.g. antihypertensive drugs)

[0536] Allergy information (e.g., no allergies)

[0537] Emotional information (e.g., voice input of stress)

[0538] output:

[0539] User information temporarily stored on the device

[0540] Step 2:

[0541] The terminal transmits the input user information and emotion data to the server using a secure communication protocol. The data arrives at the server using the transmission means.

[0542] input:

[0543] User information stored on the device

[0544] output:

[0545] User information sent to the server

[0546] Step 3:

[0547] The server receives the information transmitted from the terminal and prepares to analyze the received information using the receiving means.

[0548] input:

[0549] User information sent from the device

[0550] output:

[0551] Received information ready for analysis

[0552] Step 4:

[0553] The server analyzes the received information and suggests appropriate over-the-counter medications based on the patient's symptoms. It also retrieves information on the appropriate over-the-counter medications from a database and selects the best medication based on the patient's symptoms.

[0554] input:

[0555] Reception information (symptoms)

[0556] output:

[0557] Symptom-based over-the-counter medication suggestions

[0558] Step 5:

[0559] The server analyzes the emotion data, including the voice input data, and uses the emotion engine to recognize the user's mental state. Based on the analysis results, the server identifies the user's emotional state.

[0560] input:

[0561] Received information (emotion data)

[0562] output:

[0563] Analyzed emotional state (e.g., stress state)

[0564] Step 6:

[0565] The server uses an interaction checker to check for interactions between the currently taken medication and the potential over-the-counter medication, and evaluates the risk using an interaction risk assessment algorithm.

[0566] input:

[0567] Information about medications you are taking

[0568] Marketed drug candidate information

[0569] output:

[0570] Interaction risk assessment results

[0571] Step 7:

[0572] The server analyzes the risk of side effects based on the user's personal information, retrieves information from the database, and evaluates the risk of side effects for recommended over-the-counter drugs.

[0573] input:

[0574] Received information (personal information)

[0575] Marketed drug candidate information

[0576] output:

[0577] Side effect risk assessment results

[0578] Step 8:

[0579] The server then adjusts the suggested medicines and advice based on the analysis results of the emotion engine. For example, if the patient is under high stress, it will suggest medicines with a calming effect or foods that will help them relax.

[0580] input:

[0581] Analyzed emotional state

[0582] Symptom-based general drug information

[0583] output:

[0584] Tailored medication and dietary suggestions

[0585] Step 9:

[0586] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions, and sends the generated information to the user's terminal using the information generation means.

[0587] input:

[0588] Tailored Proposal Information

[0589] output:

[0590] Information sent to the user's device

[0591] Step 10:

[0592] The device displays the received information to the user, and uses notifications to provide the user with appropriate medications, advice, and additional precautions as needed.

[0593] input:

[0594] Information sent from the server

[0595] output:

[0596] Information and notifications displayed to users

[0597] Through these steps, users can choose and take the appropriate medication, and receive advice based on their mental state. This system utilizes generative AI models and an emotion engine to achieve more comprehensive health management.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Second embodiment]

[0602] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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."

[0614] This invention is a drug management system that utilizes generative AI, and by having users input information about the drugs they are taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, and analyzes the risk of side effects. This system is implemented as follows.

[0615] 1. Enter user information

[0616] 1.1 Application launch

[0617] The user launches the application on their smartphone or computer, and the home screen of the application displays a form for entering information about symptoms, medications they are taking, and allergies.

[0618] 1.2 Entering symptoms and medication information

[0619] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. The device temporarily stores the entered data and prepares it for transmission.

[0620] 1.3 Data Transmission

[0621] The terminal transmits the information entered by the user to the server using a secure communication protocol.

[0622] 2. Data processing and analysis

[0623] 2.1 Data Reception

[0624] The server receives the information sent from the device, including the user's symptoms, medications they are taking, and allergy information.

[0625] 2.2 Symptom-based medication suggestions

[0626] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[0627] 2.3 Drug interaction check

[0628] The server checks for interactions between the current medication (losartan) and the proposed over-the-counter medications (e.g., ibuprofen, acetaminophen). Using an interaction risk assessment algorithm, the server evaluates the interaction between each over-the-counter medication and losartan. The server determines that ibuprofen requires caution due to its high risk of interaction with losartan.

[0629] 2.4 Analysis of side effect risks

[0630] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[0631] 2.5 Prophylactic drug production

[0632] The server will suggest appropriate preventive medicines (herbal medicines or supplements) based on individual user information, past data, and current health status. It will then extract candidate preventive medicines from a database and present the most appropriate one.

[0633] 3. Returning the results

[0634] 3.1 Generating Results

[0635] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "care should be taken with ibuprofen" due to the risk of interactions with antihypertensive drugs.

[0636] 3.2 Data Transmission

[0637] The server sends the generated information to the user's terminal using a secure communication protocol.

[0638] 4. Display and notification of results

[0639] 4.1 Displaying the results

[0640] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[0641] 4.2 Caution Notice

[0642] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0643] Specific examples

[0644] User example: Hanako

[0645] 1. Hanako launches the app and wants to treat her headache.

[0646] 2. Hanako enters "headache," "antihypertensive medication currently being taken: losartan," and "no allergies" into the form on the home screen.

[0647] 3. The device sends this information to the server.

[0648] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0649] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[0650] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0651] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[0652] 8. The device will display this information to Mary and send her notifications if necessary.

[0653] This will allow Hanako to choose and take the appropriate medication with peace of mind.

[0654] The processing flow will be explained below.

[0655] Step 1:

[0656] The user launches the application and the home screen appears.

[0657] Step 2:

[0658] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into the form.

[0659] Step 3:

[0660] The terminal temporarily stores the input data and prepares it for transmission.

[0661] Step 4:

[0662] The terminal transmits the user's input information to the server.

[0663] Step 5:

[0664] The server receives information from the device, including symptoms, medications being taken, and allergy information.

[0665] Step 6:

[0666] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[0667] Step 7:

[0668] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0669] Step 8:

[0670] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[0671] Step 9:

[0672] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[0673] Step 10:

[0674] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[0675] Step 11:

[0676] The server compiles the analysis results and generates symptom-based medication suggestions, interaction check results, and information on side effect risks.

[0677] Step 12:

[0678] The server transmits the generated information to the terminal using a secure communication protocol.

[0679] Step 13:

[0680] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[0681] Step 14:

[0682] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0683] Example 1

[0684] 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."

[0685] Conventional drug management systems suggest appropriate over-the-counter medications and check for drug interactions based on the drug information and symptoms entered by the user, but they often do not adequately suggest preventative medications or provide additional warnings. As a result, users may not receive sufficient information about the risk of side effects and drug interactions, leading to inappropriate drug selection. Furthermore, there were few systems that took into account interactions with multiple drugs and foods. The purpose of this invention is to solve these problems and realize safe and effective drug management.

[0686] 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.

[0687] In this invention, the server includes an input means for the user to input information about medications being taken and symptoms, a preventive medicine generation means for generating an appropriate preventive medicine based on the received information and analysis results, and a notification means for notifying the user of additional precautions. This makes it possible to suggest safe and effective over-the-counter medications based on the information input by the user, analyze drug interactions and side effect risks, and even generate preventive medicines and provide warnings.

[0688] The "input means" is an interface that allows the user to input information about medications currently being taken, symptoms, allergies, and the like.

[0689] The "receiving means" is a function for receiving information sent from the input means.

[0690] The "suggestion means" is a function for analyzing the received information and suggesting appropriate over-the-counter medications based on the symptoms.

[0691] The "interaction checker" is a function for checking interactions between the medication being taken and proposed over-the-counter medications, multiple medications, and foods.

[0692] The "side effect risk analysis means" is a function for analyzing the risk of side effects based on the user's personal information.

[0693] The "prophylactic medicine generating means" is a function for generating an appropriate prophylactic medicine based on the received information and analysis results.

[0694] The "information generating means" is a function that compiles the analysis results and the generated preventive medicines, and generates information to be provided to the user.

[0695] The "transmission means" is a function for transmitting the information generated by the information generation means to the user's terminal.

[0696] The "display means" is a function for displaying the information transmitted by the transmission means on the user terminal.

[0697] "Notification means" is a function for notifying the user of additional precautions.

[0698] This invention is a drug management system that utilizes a generative AI model. By inputting information about the drugs a user is taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, analyzes the risk of side effects, and even generates preventative medications and provides necessary warnings. This system is implemented as follows.

[0699] Enter user information

[0700] The user launches the application on their smartphone or computer and enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into a form displayed on the home screen. The device temporarily stores this information and prepares it for transmission. When the user taps the send button, the input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[0701] Data processing and analysis

[0702] The server receives user information sent from the device. Based on the received information, it suggests appropriate over-the-counter medications to address the patient's condition. Specifically, it retrieves information on the relevant over-the-counter medications from a database and selects the best medication based on the patient's symptoms. It then uses an interaction risk assessment algorithm to check for interactions between the current medication and the suggested over-the-counter medication. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). The side effect risk assessment module compares the results with the risk database to make an assessment.

[0703] The server also suggests appropriate preventive medicines (e.g., herbal medicines or supplements) based on the received information, past data, and current health status. Using a preventive medicine generation means, it retrieves candidate preventive medicines from the database and presents the most suitable one.

[0704] Returning the results

[0705] The server compiles the analysis results and generated preventive medication information and generates information to provide to the user, including recommended medications for symptoms and associated precautions. The generated information is then sent to the user's device using a secure communication protocol.

[0706] Displaying and notifying results

[0707] The device displays the received information to the user. Specifically, it displays "Acetaminophen is recommended. Consult your doctor before using ibuprofen." It also uses notification functionality to provide additional warnings to the user as needed, such as providing a link to purchase the recommended medication or other important information.

[0708] Overview of the hardware and software used

[0709] Hardware

[0710] Smartphones (general mobile devices)

[0711] A computer (typical desktop or laptop)

[0712] Server (cloud computing environment)

[0713] software

[0714] Mobile applications (general cross-platform development frameworks)

[0715] Web applications (general front-end frameworks)

[0716] Server-side software (popular back-end frameworks and scripting languages)

[0717] Database (general relational database management system)

[0718] Secure communication protocols (HTTPS, SSL / TLS)

[0719] Specific examples

[0720] User example: User A

[0721] 1. User A launches the app and wants to treat a headache.

[0722] 2. User A enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen.

[0723] 3. The device sends this information to the server.

[0724] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0725] 5. The server checks for interactions with the blood pressure medication the patient is already taking and determines that acetaminophen is a safe option.

[0726] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0727] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[0728] 8. The device displays this information to User A and also sends notifications if necessary.

[0729] This allows user A to select and take the appropriate medication with peace of mind.

[0730] Example prompt: "Enter your current symptoms and medications to identify potential interactions and risk of side effects. For example, 'I have a headache and am taking blood pressure medication. Can I take over-the-counter acetaminophen?'"

[0731] The above is a specific embodiment of this medicine management system.

[0732] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] The user launches the application. The app's home screen appears, providing a form for entering symptoms, medications, and allergies. The information entered is temporarily stored on the device for future submission.

[0735] Specific actions

[0736] The user taps the app icon.

[0737] The home screen will be displayed.

[0738] The input form will be displayed.

[0739] The user enters information using a keyboard or touch screen.

[0740] The terminal temporarily stores the input data in memory.

[0741] Input: User's symptoms, medications, and allergy information

[0742] Output: Temporarily saved input data

[0743] Step 2:

[0744] When the user taps the submit button, the entered information is sent to the server using a secure communication protocol (e.g., HTTPS).

[0745] Specific actions

[0746] The user taps the send button.

[0747] The terminal prepares the data for transmission and encrypts it.

[0748] The encrypted data is sent to the server.

[0749] Input: Temporarily saved input data, submit button tap event

[0750] Output: Encrypted data sent to the server

[0751] Step 3:

[0752] The server receives the information sent from the device. The received information includes the user's symptoms, medications they are taking, and allergy information. The received data is stored in a database for analysis.

[0753] Specific actions

[0754] A data reception module runs on the server.

[0755] The server decrypts the transmitted data.

[0756] The received data is stored in a database.

[0757] Input: Encrypted user data

[0758] Output: User data stored in the database

[0759] Step 4:

[0760] The server analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms. It also retrieves information on the corresponding over-the-counter medications from a database and selects the most appropriate medication.

[0761] Specific actions

[0762] The server uses the user's symptoms as a database search query.

[0763] Obtain a list of over-the-counter medications that address your symptoms from the database.

[0764] An algorithm selects the over-the-counter medication that best suits your symptoms.

[0765] Input: User's symptoms, medications, and allergy information stored in the database

[0766] Output: List of over-the-counter drugs and best over-the-counter drug information

[0767] Step 5:

[0768] The server uses an interaction risk assessment algorithm to check for interactions between current medications and proposed over-the-counter medications.

[0769] Specific actions

[0770] The server lists the medications you are taking and suggested over-the-counter medications.

[0771] Run the interaction risk assessment algorithm.

[0772] Generates an alert if a risk is detected.

[0773] Input: List of over-the-counter medications, information on medications currently being taken

[0774] Output: Interaction risk assessment results and warning information

[0775] Step 6:

[0776] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), and evaluates the risk using a side effect risk assessment module.

[0777] Specific actions

[0778] The server passes user information to the side effect risk assessment module.

[0779] The side effect risk assessment module checks against the risk database.

[0780] Generate risk assessment results.

[0781] Input: User's personal information stored in the database

[0782] Output: Side effect risk assessment results

[0783] Step 7:

[0784] The server generates an appropriate preventive medicine based on the received information, past data, and current health status. Using the preventive medicine generation means, candidate preventive medicines are extracted and the most suitable one is presented.

[0785] Specific actions

[0786] The server uses historical data and current information to generate a list of preventative medication candidates.

[0787] The preventive medicine generating means selects the optimum preventive medicine.

[0788] Input: User information stored in the database, past data

[0789] Output: List of preventive drug candidates and information on the optimal preventive drug

[0790] Step 8:

[0791] The server compiles the analysis results and information about the generated preventive medications, and generates information to provide to the user, including recommended medications for the symptoms and associated precautions.

[0792] Specific actions

[0793] The server aggregates the results of each analysis.

[0794] Generate user-friendly information in the form of text, charts, etc.

[0795] Input: Analysis results, preventive medicine information

[0796] Output: Information provided to the user (recommended medications, precautions, etc.)

[0797] Step 9:

[0798] The server transmits the generated information to the user's terminal using a secure communication protocol.

[0799] Specific actions

[0800] The server encrypts the data and prepares it for transmission.

[0801] The encrypted data is sent to the terminal.

[0802] Input: Information to be provided to the user (recommended medications, precautions, etc.)

[0803] Output: Encrypted information sent to the user's device

[0804] Step 10:

[0805] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[0806] Specific actions

[0807] The terminal decrypts the received data.

[0808] Present the data in a format suitable for the user interface.

[0809] Input: Encrypted information

[0810] Output: Information displayed to the user

[0811] Step 11:

[0812] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0813] Specific actions

[0814] The device generates a notification.

[0815] Notify the user in the form of a popup or message.

[0816] Input: Notification information to provide to users

[0817] Output: Warnings and links notified to the user

[0818] The above are the specific processing steps of the medicine management system of the present invention.

[0819] (Application example 1)

[0820] 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."

[0821] Currently, drug selection, interaction checks, and side effect risk analysis must be done individually, which is cumbersome for users. Even when an appropriate drug is presented, the purchasing process is often time-consuming and laborious. This makes it difficult to obtain over-the-counter drugs quickly and safely.

[0822] 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.

[0823] In this invention, the server includes an input means for inputting information about drugs being taken by the user and symptoms, a receiving means for receiving information transmitted from the input means, a suggestion means for analyzing the received information and suggesting appropriate over-the-counter drugs based on the symptoms, an interaction checking means for checking interactions between the drugs being taken and the suggested over-the-counter drugs, a side effect risk analysis means for analyzing the risk of side effects based on the user's personal information, an information generating means for summarizing the analysis results and providing them to the user, a transmitting means for transmitting the information generated by the information generating means to the user's terminal, a display means for displaying the information transmitted by the transmitting means on the user's terminal, and an electronic payment means for quickly and safely purchasing the suggested over-the-counter drugs. This enables a unified process from drug selection, interaction checking, and side effect risk analysis to the prompt purchase of appropriate over-the-counter drugs.

[0824] "Input means" refers to a device or interface that allows a user to input information such as information about medications being taken and symptoms.

[0825] The "receiving means" is a device or system that allows the server to receive the information transmitted from the input means.

[0826] The "suggestion means" is a device or system that analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms.

[0827] An "interaction checking means" is a device or system for checking interactions between medications currently being taken and proposed over-the-counter medications and conducting risk assessments.

[0828] "Side effect risk analysis means" refers to a device or system for analyzing and evaluating side effect risks based on the user's personal information.

[0829] The "information generating means" is a device or system that generates information to be provided to users based on the analysis results.

[0830] The "transmission means" is a device or system for transmitting the information generated by the information generation means to the user's terminal.

[0831] "Display means" refers to a device or interface for displaying the transmitted information on the user's terminal.

[0832] "Electronic payment method" means an electronic payment method or system for the fast and secure purchase of the proposed over-the-counter drug.

[0833] This invention is a drug management system that utilizes generative AI, and can suggest appropriate over-the-counter drugs, check for drug interactions, and analyze the risk of side effects based on the drug information and symptoms entered by the user. It also has an electronic payment method for quickly and safely purchasing the suggested over-the-counter drugs.

[0834] Overall system configuration

[0835] The system consists of the following elements:

[0836] 1. Input method: An interface (e.g., smartphone app) through which users can input information about symptoms and medications they are taking.

[0837] 2. Receiving means: A server system (using a Flask application) that receives information sent from the input means.

[0838] 3. Recommendation method: A system that analyzes the received information and suggests over-the-counter medications that address the symptoms.

[0839] 4. Interaction Checking Measures: A system to check for interactions between medications currently being taken and proposed over-the-counter medications.

[0840] 5. Side effect risk analysis means: A system that analyzes the risk of side effects based on the user's personal information.

[0841] 6. Information generation means: A system that generates information to be provided to users based on the analysis results.

[0842] 7. Transmission means: A system that transmits the information generated by the information generation means to the user's terminal.

[0843] 8. Display means: An interface that displays the transmitted information on the user's terminal.

[0844] 9. Electronic Payment Methods: An electronic payment system for fast and secure purchase of proposed over-the-counter medications.

[0845] Data processing and analysis description

[0846] The server receives the drug information and symptoms sent by the user and analyzes that information using a generative AI model. Specifically, it retrieves appropriate over-the-counter drugs from a database based on the symptoms and checks for interactions between each drug and any other medications the user is currently taking. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.).

[0847] The information generation means compiles these analysis results and generates information including recommended medicines and precautions for the user. The generated information is sent to the user's terminal via a secure communication protocol.

[0848] The electronic payment method provides a payment interface that includes multiple payment methods (credit cards, electronic money, QR code payment, etc.) so that users can instantly purchase the suggested over-the-counter medications.

[0849] Examples of concrete examples and prompts

[0850] As a concrete example, let's say a user wants to treat a "headache." In this case, the user launches the app and enters "headache," "current antihypertensive medication: Losartan," and "no allergies." The device sends this information to the server, which analyzes it and notifies the user that acetaminophen is recommended. The user can also quickly complete the purchase process within the app.

[0851] An example prompt has the following format:

[0852] "Please enter your symptoms and medications:"

[0853] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0854] Step 1:

[0855] The user launches the smartphone app and inputs symptoms, medication information, and allergy information. The user enters this information into the input form and presses the send button, temporarily saving the data on the device. The input data includes symptoms (e.g., headache), medications being taken (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies). The output is the input data temporarily saved on the device.

[0856] Step 2:

[0857] The terminal sends the information entered by the user to the server using a secure communication protocol (e.g., TLS / SSL). At this time, the terminal converts the temporarily saved data into JSON format and sends it as an HTTPS request. The input is the temporarily saved data, and the output is the data sent to the server.

[0858] Step 3:

[0859] The server receives the information sent from the device and stores it in a database. At the same time, it begins analyzing the received data. Specifically, it retrieves a list of over-the-counter medications that match the symptoms from the database and proceeds with the analysis. The input is the data sent from the device, and the output is the list of over-the-counter medications from the database used for analysis.

[0860] Step 4:

[0861] The server uses a generative AI model to suggest appropriate over-the-counter medications based on the received data (user symptoms, medications being taken, and allergy information). This generative AI model analyzes over-the-counter medication information corresponding to symptoms in the database and makes an appropriate selection. The input is symptoms and medication information, and the output is a list of suggested over-the-counter medications.

[0862] Step 5:

[0863] The server checks interactions between the proposed over-the-counter drugs and the medications the user is currently taking. To do this, it uses a pre-built interaction checking algorithm to evaluate the interaction risk between each over-the-counter drug and the medication the user is currently taking. The input is the list of proposed over-the-counter drugs and information about the medications the user is currently taking, and the output is the evaluation result of the interaction risk.

[0864] Step 6:

[0865] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). It obtains information about side effects from the database and performs a risk assessment based on that information. The input is personal information and drug information, and the output is the assessment result of the side effect risk.

[0866] Step 7:

[0867] The server generates information to provide to users based on these analysis results. The generated information includes suggested over-the-counter medications and precautions. The information generation means uses a generative AI model to appropriately summarize the analysis results and convert them into a format optimized for user viewing. The input is the analysis results, and the output is the generated information.

[0868] Step 8:

[0869] The server sends the generated information to the user's device using a secure communication protocol. At this time, the generated information is sent in an appropriate format (e.g., JSON format). The input is the generated information, and the output is the sent information. TLS / SSL is used for transmission to ensure data security.

[0870] Step 9:

[0871] The device displays the received information to the user, specifically, the suggested over-the-counter drug information and warnings using an appropriate interface, and sends notifications as needed. The input is the received information, and the output is the displayed information and sent notifications.

[0872] Step 10:

[0873] The user then goes through the process of purchasing the over-the-counter medication suggested within the app. The payment is completed quickly and securely using electronic payment methods. Various payment methods (credit cards, electronic money, QR code payment, etc.) are integrated into the interface. The input is the intention to purchase and payment information, and the output is a notification that the purchase has been completed.

[0874] 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.

[0875] The present invention is a system that provides advice and drug suggestions according to the user's mental state by combining a drug management system that utilizes generative AI with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[0876] 1. Enter user information

[0877] 1.1 Application launch

[0878] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[0879] 1.2 Entering symptoms and medication information

[0880] The user enters their symptoms (e.g., headache), medications (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[0881] 1.3 Data Transmission

[0882] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[0883] 2. Data processing and analysis

[0884] 2.1 Data Reception

[0885] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[0886] 2.2 Symptom-based medication suggestions

[0887] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[0888] 2.3 Emotion Analysis

[0889] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[0890] 2.4 Drug interaction check

[0891] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0892] 2.5 Analysis of side effect risks

[0893] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[0894] 2.6 Adjusting Suggestions Based on Emotions

[0895] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is experiencing high stress, it will provide medications and advice that have a calming effect.

[0896] 3. Returning the results

[0897] 3.1 Generating Results

[0898] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It also includes advice based on the patient's emotional state.

[0899] 3.2 Data Transmission

[0900] The server sends the generated information to the user's terminal using a secure communication protocol.

[0901] 4. Display and notification of results

[0902] 4.1 Displaying the results

[0903] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[0904] 4.2 Caution Notice

[0905] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0906] Specific examples

[0907] User example: Hanako

[0908] 1. Hanako launches the app and wants to treat her headache.

[0909] 2. Hanako enters "headache," "hypertensive medication currently being taken: Losartan," and "no allergies" into the form on the home screen. She also uses voice input to say, "I've been feeling stressed lately."

[0910] 3. The device sends this information to the server.

[0911] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[0912] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[0913] 6. The server analyzes the emotional data and recognizes that Hanako is under a lot of stress.

[0914] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[0915] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[0916] 9. The device will display this information to Mary and send her notifications if necessary.

[0917] This allows Hanako to choose and take the appropriate medication with peace of mind, and also receive advice tailored to her own mental state.

[0918] The processing flow will be explained below.

[0919] Step 1:

[0920] When a user launches the application, they are presented with a home screen, which includes a form where they can enter information about their symptoms, medications, and allergies. Additionally, a voice input button is provided to visualize their emotional state.

[0921] Step 2:

[0922] The user inputs symptoms into the form. For example, the user inputs "headache." The user also selects "Losartan, an antihypertensive drug" as the medication currently being taken, and selects "No allergies" as the allergy information.

[0923] Step 3:

[0924] The user presses the voice input button and provides an emotional state through voice input, for example, saying, "I've been feeling stressed lately."

[0925] Step 4:

[0926] The device temporarily stores the entered data (symptoms, medications, allergy information, and voice data) and prepares it for transmission.

[0927] Step 5:

[0928] The device sends the user's input information and voice data to the server, using a secure communication protocol to keep the data safe.

[0929] Step 6:

[0930] The server receives information from the device, including symptoms, medications being taken, allergy information, and emotional data.

[0931] Step 7:

[0932] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[0933] Step 8:

[0934] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[0935] Step 9:

[0936] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[0937] Step 10:

[0938] The server analyzes the voice input data and recognizes the user's emotional state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[0939] Step 11:

[0940] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[0941] Step 12:

[0942] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[0943] Step 13:

[0944] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if stress levels are high, it will provide medications and advice with a calming effect.

[0945] Step 14:

[0946] The server compiles the analysis results and generates information including appropriate medication suggestions, precautions, and emotion-based advice.

[0947] Step 15:

[0948] The server sends the generated information to the user's terminal using a secure communication protocol.

[0949] Step 16:

[0950] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[0951] Step 17:

[0952] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[0953] Example 2

[0954] 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."

[0955] Conventional drug management systems suggest appropriate over-the-counter medications based on the user's current medication information and symptoms, but they are unable to provide advice that takes into account the user's emotional state. As a result, it is difficult for users to receive appropriate advice when they are stressed or mentally unstable. In addition, there is a need to improve safety due to insufficient analysis of drug interactions and side effect risks.

[0956] 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.

[0957] In this invention, the server includes input means for inputting information about medications the user is taking, symptoms, and emotional state, receiving means for receiving information transmitted from the input means, suggestion means for analyzing the received information and suggesting appropriate medications based on the symptoms, interaction checking means for checking interactions between the medications the user is taking and the suggested medications, side effect risk analysis means for analyzing the risk of side effects based on the user's personal information and emotional state, information generation means for summarizing the analysis results and providing them to the user, transmission means for transmitting the information generated by the information generation means to the user's terminal, and display means for displaying the information transmitted by the transmission means on the user's terminal. This enables accurate advice and medication suggestions based on the user's emotional state, thereby improving safety and psychological support.

[0958] "User" refers to a person who uses the system to input their symptoms, information about the medications they are taking, and their emotional state, and receives appropriate medication suggestions and advice.

[0959] "Drug information" refers to detailed information such as the name, dosage, and usage instructions of the medications the user is currently taking.

[0960] "Symptom" means information that describes a physical or mental illness or condition that a user is currently experiencing.

[0961] "Emotional state" refers to information that indicates a user's current state of mind or emotion, such as stress, anxiety, or calmness.

[0962] "Input means" refers to the interface through which users input their medication information, symptoms, and emotional state into the system. Specifically, this includes form entry and voice input functions.

[0963] The "receiving means" refers to a function that allows the system to acquire information sent from the input means.

[0964] The "suggestion means" refers to a function for analyzing the information acquired by the receiving means and suggesting an appropriate medication based on the symptoms of the user.

[0965] "Interaction checking means" refers to a function for analyzing interactions between the medications the user is taking and the proposed medication and assessing the risks.

[0966] "Side effect risk analysis means" refers to a function for assessing the side effect risk of a proposed drug based on the user's personal information and emotional state.

[0967] "Information generation means" refers to the function by which the system compiles the analysis results and generates information to be provided to users.

[0968] The "transmission means" refers to a function for transmitting the information generated by the information generation means to the user's terminal.

[0969] The "display means" refers to a function for displaying the information transmitted by the transmission means on the user terminal.

[0970] "Database" refers to an information management system that stores information on drugs, interactions, risk of side effects, etc., and makes it accessible to the system as needed.

[0971] "HTTPS" is a protocol for securely sending and receiving data over the Internet, and refers to technology that ensures data protection.

[0972] MODE FOR CARRYING OUT THE INVENTION

[0973] This invention is a system that provides advice and drug suggestions based on the user's mental state by combining a drug management system that utilizes a generative AI model with an emotion engine that recognizes the user's emotions. This system is implemented as an application that can be accessed from a smartphone or computer.

[0974] 1. Enter user information

[0975] 1.1 Application launch

[0976] Users launch the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[0977] 1.2 Entering symptoms and medication information

[0978] The user enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensives), and allergy information (e.g., no allergies) into the form. They also use voice input to provide emotional status information, such as "I've been feeling stressed lately." The device temporarily stores the entered data and prepares it for transmission.

[0979] 1.3 Data Transmission

[0980] The device transmits the information and emotion data entered by the user to the server using secure communication technology such as the HTTPS protocol.

[0981] 2. Data processing and analysis

[0982] 2.1 Data Reception

[0983] The server receives the information sent from the terminal, including the user's symptoms, medications being taken, allergy information, and emotional data.

[0984] 2.2 Symptom-based medication suggestions

[0985] The server analyzes the received information and suggests appropriate medications for the user's symptoms. For example, it retrieves information on medications for "headache" (e.g., acetaminophen, ibuprofen) from a database and selects the most appropriate medication.

[0986] 2.3 Emotion Analysis

[0987] The server uses an emotion engine to analyze emotion data, including voice input data, and recognize the user's mental state (e.g., stress, anxiety, calm).

[0988] 2.4 Drug interaction check

[0989] The server checks for interactions between current medications (e.g., antihypertensives) and proposed over-the-counter medications (e.g., acetaminophen, ibuprofen), and assesses the risk using a risk assessment algorithm.

[0990] 2.5 Analysis of side effect risks

[0991] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information about the risk of side effects from the database, and makes an assessment.

[0992] 2.6 Adjusting Suggestions Based on Emotions

[0993] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide advice on medications with a calming effect or to promote relaxation.

[0994] 3. Returning the results

[0995] 3.1 Generating Results

[0996] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[0997] 3.2 Data Transmission

[0998] The server sends the generated information to the user's device using secure communication technology such as the HTTPS protocol.

[0999] 4. Display and notification of results

[1000] 4.1 Displaying the results

[1001] The device then displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[1002] 4.2 Caution Notice

[1003] If necessary, the device will use notifications to provide additional user attention, such as providing links to purchase recommended medications or other important information.

[1004] Specific examples

[1005] User example: A user

[1006] 1. A user launches the app and wants to treat a headache.

[1007] 2. The user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[1008] 3. The device sends this information to the server.

[1009] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1010] 5. The server checks for interactions with antihypertensive medications and determines that acetaminophen is a safe option.

[1011] 6. The server analyzes the emotional data and recognizes that the user is under high stress.

[1012] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1013] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[1014] 9. The device displays this information to the user and also sends notifications if necessary.

[1015] This example allows users to choose and take appropriate medication with peace of mind, and also receive advice tailored to their own mental state.

[1016] Example prompts for generative AI models

[1017] Input prompt: "Please enter your headache symptoms and tell me about over-the-counter medications that can be taken with your current blood pressure medication. If you've been under a lot of stress lately, what medication would be appropriate?"

[1018] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1019] Step 1:

[1020] The user launches the application on their smartphone or computer. The home screen displays a form for entering symptoms, medication information, and allergy information. The user enters their symptoms (e.g., headache), medications (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into the form, and provides their emotional state, such as "I've been feeling stressed lately," via voice input. The entered data is temporarily stored on the device.

[1021] Input: User's symptoms, medications, allergy information, emotional state

[1022] Output: Saved user data

[1023] Step 2:

[1024] The device sends the stored user data to the server using the secure HTTPS protocol. The server receives and stores the information sent from the device.

[1025] Input: Saved user data

[1026] Output: User data stored on the server

[1027] Step 3:

[1028] The server analyzes the received information. First, it retrieves medication information corresponding to the symptoms from the database (e.g., acetaminophen or ibuprofen for headaches) and selects the most appropriate medication. Next, it converts the voice input data into text and uses an emotion engine to analyze the user's emotional state (e.g., stress, anxiety, calm).

[1029] Input: User data stored on the server

[1030] Output: Analysis results (suggested medication for symptoms and user's emotional state)

[1031] Step 4:

[1032] The server checks interactions between current medications (e.g., antihypertensives) and proposed over-the-counter drugs (e.g., acetaminophen, ibuprofen), queries an interaction database, and assesses the risk of interaction using a risk assessment algorithm.

[1033] Input: Analysis results (suggested medications), medication information

[1034] Output: Interaction check results (risk assessment)

[1035] Step 5:

[1036] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and uses a risk assessment model to select the most appropriate drug candidate.

[1037] Input: User's personal information, suggested medication

[1038] Output: Side effect risk assessment results

[1039] Step 6:

[1040] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide sedative medications and advice on how to relax.

[1041] Input: Side effect risk assessment results, emotion analysis results

[1042] Output: Tailored suggestions and advice

[1043] Step 7:

[1044] The server compiles the analysis results and generates information for the user, including suggestions for appropriate medications and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[1045] Input: Tailored suggestions and advice

[1046] Output: Generated suggestions and warning information

[1047] Step 8:

[1048] The server sends the generated information to the user's device using the secure HTTPS protocol, where it displays the received information to the user and, if necessary, uses a notification function to provide additional attention.

[1049] Input: Generated suggestions and warning information

[1050] Output: Information displayed to the user and any necessary notifications

[1051] (Application example 2)

[1052] 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."

[1053] Conventional drug management systems did not provide advice or suggestions based on the user's emotional state, and could only make uniform suggestions regardless of the user's mental state. This made it difficult to provide comprehensive health management that took into account mental factors such as stress and anxiety, and to suggest appropriate over-the-counter medications and dietary recommendations. Furthermore, drug-food interactions were often not sufficiently considered.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a suggestion means that provides advice and dietary suggestions appropriate to the user's mental state based on emotion data analyzed using an emotion engine, a suggestion means that analyzes received information and suggests appropriate over-the-counter medications based on symptoms, and an interaction check means that checks interactions between medications currently being taken and the suggested over-the-counter medications. This enables comprehensive health management that takes the user's mental state into consideration.

[1055] The "input means" is a device through which the user inputs drug information, symptoms, allergy information, and emotions.

[1056] The "receiving means" is a device that receives information transmitted from the input means.

[1057] The "suggestion means" is a device that analyzes the received information and suggests appropriate over-the-counter medicines based on symptoms, as well as advice and diet appropriate to the patient's mental state based on emotional data.

[1058] An "interaction checking means" is a device that checks for interactions between a drug being taken and a proposed over-the-counter drug, as well as interactions with multiple drugs and foods.

[1059] The "side effect risk analysis means" is a device that analyzes the side effect risk based on the user's personal information.

[1060] An "emotion engine" is a program or device that analyzes emotional data such as voice input and understands the user's mental state.

[1061] The "information generating means" is a device that compiles the analysis results and generates information to be provided to the user.

[1062] The "transmitting means" is a device that transmits the information generated by the information generating means to the user's terminal.

[1063] The "display means" is a device that displays the information transmitted by the transmission means on the user terminal.

[1064] 1. Enter user information

[1065] 1.1 Application launch

[1066] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[1067] 1.2 Entering symptoms and medication information

[1068] The user enters their symptoms (e.g., headache), medications (e.g., antihypertensives), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[1069] 1.3 Data Transmission

[1070] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[1071] 2. Data processing and analysis

[1072] 2.1 Data Reception

[1073] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[1074] 2.2 Symptom-based medication suggestions

[1075] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the appropriate over-the-counter medications from a database and selects the best medication based on the symptoms.

[1076] 2.3 Emotion Analysis

[1077] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). It does this using an emotion engine.

[1078] 2.4 Drug interaction check

[1079] The server checks interactions between medications currently being taken (e.g., antihypertensives) and potential over-the-counter drugs (e.g., acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[1080] 2.5 Analysis of side effect risks

[1081] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[1082] 2.6 Adjusting Suggestions Based on Emotions

[1083] The server then adjusts the selection of suggested over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is under high stress, it will provide medications and advice with a calming effect. It will also suggest easy-to-digest meals.

[1084] 3. Returning the results

[1085] 3.1 Generating Results

[1086] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, but note that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the patient's emotional state.

[1087] 3.2 Data Transmission

[1088] The server sends the generated information to the user's terminal using a secure communication protocol.

[1089] 4. Display and notification of results

[1090] 4.1 Displaying the results

[1091] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[1092] 4.2 Caution Notice

[1093] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1094] Specific examples

[1095] User example: General user

[1096] 1. A regular user launches the app and wants to treat a headache.

[1097] 2. A general user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[1098] 3. The device sends this information to the server.

[1099] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1100] 5. The server checks for interactions with the blood pressure medication being taken and determines that acetaminophen is a safe option.

[1101] 6. The server analyzes the emotional data and recognizes that the general user is under high stress.

[1102] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1103] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[1104] 9. The device displays this information to the general user and also sends notifications if necessary.

[1105] This allows general users to choose and take appropriate medication with peace of mind, and also receive advice based on their own mental state.

[1106] Prompt Sentence Examples

[1107] "I'm feeling stressed at work. Can you suggest some foods that will help relieve stress?"

[1108] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1109] Step 1:

[1110] The user launches the application on their smartphone or computer and inputs their symptoms, medication information, and allergy information. Using the input means, they enter this information into a form or record their emotional state using voice input. The input data is temporarily stored on the device.

[1111] input:

[1112] Symptoms (e.g. headache)

[1113] Medications you are taking (e.g. antihypertensive drugs)

[1114] Allergy information (e.g., no allergies)

[1115] Emotional information (e.g., voice input of stress)

[1116] output:

[1117] User information temporarily stored on the device

[1118] Step 2:

[1119] The terminal transmits the input user information and emotion data to the server using a secure communication protocol. The data arrives at the server using the transmission means.

[1120] input:

[1121] User information stored on the device

[1122] output:

[1123] User information sent to the server

[1124] Step 3:

[1125] The server receives the information transmitted from the terminal and prepares to analyze the received information using the receiving means.

[1126] input:

[1127] User information sent from the device

[1128] output:

[1129] Received information ready for analysis

[1130] Step 4:

[1131] The server analyzes the received information and suggests appropriate over-the-counter medications based on the patient's symptoms. It also retrieves information on the appropriate over-the-counter medications from a database and selects the best medication based on the patient's symptoms.

[1132] input:

[1133] Reception information (symptoms)

[1134] output:

[1135] Symptom-based over-the-counter medication suggestions

[1136] Step 5:

[1137] The server analyzes the emotion data, including the voice input data, and uses the emotion engine to recognize the user's mental state. Based on the analysis results, the server identifies the user's emotional state.

[1138] input:

[1139] Received information (emotion data)

[1140] output:

[1141] Analyzed emotional state (e.g., stress state)

[1142] Step 6:

[1143] The server uses an interaction checker to check for interactions between the currently taken medication and the potential over-the-counter medication, and evaluates the risk using an interaction risk assessment algorithm.

[1144] input:

[1145] Information about medications you are taking

[1146] Marketed drug candidate information

[1147] output:

[1148] Interaction risk assessment results

[1149] Step 7:

[1150] The server analyzes the risk of side effects based on the user's personal information, retrieves information from the database, and evaluates the risk of side effects for recommended over-the-counter drugs.

[1151] input:

[1152] Received information (personal information)

[1153] Marketed drug candidate information

[1154] output:

[1155] Side effect risk assessment results

[1156] Step 8:

[1157] The server then adjusts the suggested medicines and advice based on the analysis results of the emotion engine. For example, if the patient is under high stress, it will suggest medicines with a calming effect or foods that will help them relax.

[1158] input:

[1159] Analyzed emotional state

[1160] Symptom-based general drug information

[1161] output:

[1162] Tailored medication and dietary suggestions

[1163] Step 9:

[1164] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions, and sends the generated information to the user's terminal using the information generation means.

[1165] input:

[1166] Tailored Proposal Information

[1167] output:

[1168] Information sent to the user's device

[1169] Step 10:

[1170] The device displays the received information to the user, and uses notifications to provide the user with appropriate medications, advice, and additional precautions as needed.

[1171] input:

[1172] Information sent from the server

[1173] output:

[1174] Information and notifications displayed to users

[1175] Through these steps, users can choose and take the appropriate medication, and receive advice based on their mental state. This system utilizes generative AI models and an emotion engine to achieve more comprehensive health management.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] [Third embodiment]

[1180] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1181] 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.

[1182] 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).

[1183] 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.

[1184] 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.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] 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.

[1191] 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."

[1192] This invention is a drug management system that utilizes generative AI, and by having users input information about the drugs they are taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, and analyzes the risk of side effects. This system is implemented as follows.

[1193] 1. Enter user information

[1194] 1.1 Application launch

[1195] The user launches the application on their smartphone or computer, and the home screen of the application displays a form for entering information about symptoms, medications they are taking, and allergies.

[1196] 1.2 Entering symptoms and medication information

[1197] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. The device temporarily stores the entered data and prepares it for transmission.

[1198] 1.3 Data Transmission

[1199] The terminal transmits the information entered by the user to the server using a secure communication protocol.

[1200] 2. Data processing and analysis

[1201] 2.1 Data Reception

[1202] The server receives the information sent from the device, including the user's symptoms, medications they are taking, and allergy information.

[1203] 2.2 Symptom-based medication suggestions

[1204] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[1205] 2.3 Drug interaction check

[1206] The server checks for interactions between the current medication (losartan) and the proposed over-the-counter medications (e.g., ibuprofen, acetaminophen). Using an interaction risk assessment algorithm, the server evaluates the interaction between each over-the-counter medication and losartan. The server determines that ibuprofen requires caution due to its high risk of interaction with losartan.

[1207] 2.4 Analysis of side effect risks

[1208] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[1209] 2.5 Prophylactic drug production

[1210] The server will suggest appropriate preventive medicines (herbal medicines or supplements) based on individual user information, past data, and current health status. It will then extract candidate preventive medicines from a database and present the most appropriate one.

[1211] 3. Returning the results

[1212] 3.1 Generating Results

[1213] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "care should be taken with ibuprofen" due to the risk of interactions with antihypertensive drugs.

[1214] 3.2 Data Transmission

[1215] The server sends the generated information to the user's terminal using a secure communication protocol.

[1216] 4. Display and notification of results

[1217] 4.1 Displaying the results

[1218] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[1219] 4.2 Caution Notice

[1220] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1221] Specific examples

[1222] User example: Hanako

[1223] 1. Hanako launches the app and wants to treat her headache.

[1224] 2. Hanako enters "headache," "antihypertensive medication currently being taken: losartan," and "no allergies" into the form on the home screen.

[1225] 3. The device sends this information to the server.

[1226] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1227] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[1228] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1229] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[1230] 8. The device will display this information to Mary and send her notifications if necessary.

[1231] This will allow Hanako to choose and take the appropriate medication with peace of mind.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] The user launches the application and the home screen appears.

[1235] Step 2:

[1236] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into the form.

[1237] Step 3:

[1238] The terminal temporarily stores the input data and prepares it for transmission.

[1239] Step 4:

[1240] The terminal transmits the user's input information to the server.

[1241] Step 5:

[1242] The server receives information from the device, including symptoms, medications being taken, and allergy information.

[1243] Step 6:

[1244] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[1245] Step 7:

[1246] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[1247] Step 8:

[1248] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[1249] Step 9:

[1250] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[1251] Step 10:

[1252] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[1253] Step 11:

[1254] The server compiles the analysis results and generates symptom-based medication suggestions, interaction check results, and information on side effect risks.

[1255] Step 12:

[1256] The server transmits the generated information to the terminal using a secure communication protocol.

[1257] Step 13:

[1258] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[1259] Step 14:

[1260] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1261] Example 1

[1262] 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."

[1263] Conventional drug management systems suggest appropriate over-the-counter medications and check for drug interactions based on the drug information and symptoms entered by the user, but they often do not adequately suggest preventative medications or provide additional warnings. As a result, users may not receive sufficient information about the risk of side effects and drug interactions, leading to inappropriate drug selection. Furthermore, there were few systems that took into account interactions with multiple drugs and foods. The purpose of this invention is to solve these problems and realize safe and effective drug management.

[1264] 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.

[1265] In this invention, the server includes an input means for the user to input information about medications being taken and symptoms, a preventive medicine generation means for generating an appropriate preventive medicine based on the received information and analysis results, and a notification means for notifying the user of additional precautions. This makes it possible to suggest safe and effective over-the-counter medications based on the information input by the user, analyze drug interactions and side effect risks, and even generate preventive medicines and provide warnings.

[1266] The "input means" is an interface that allows the user to input information about medications currently being taken, symptoms, allergies, and the like.

[1267] The "receiving means" is a function for receiving information sent from the input means.

[1268] The "suggestion means" is a function for analyzing the received information and suggesting appropriate over-the-counter medications based on the symptoms.

[1269] The "interaction checker" is a function for checking interactions between the medication being taken and proposed over-the-counter medications, multiple medications, and foods.

[1270] The "side effect risk analysis means" is a function for analyzing the risk of side effects based on the user's personal information.

[1271] The "prophylactic medicine generating means" is a function for generating an appropriate prophylactic medicine based on the received information and analysis results.

[1272] The "information generating means" is a function that compiles the analysis results and the generated preventive medicines, and generates information to be provided to the user.

[1273] The "transmission means" is a function for transmitting the information generated by the information generation means to the user's terminal.

[1274] The "display means" is a function for displaying the information transmitted by the transmission means on the user terminal.

[1275] "Notification means" is a function for notifying the user of additional precautions.

[1276] This invention is a drug management system that utilizes a generative AI model. By inputting information about the drugs a user is taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, analyzes the risk of side effects, and even generates preventative medications and provides necessary warnings. This system is implemented as follows.

[1277] Enter user information

[1278] The user launches the application on their smartphone or computer and enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into a form displayed on the home screen. The device temporarily stores this information and prepares it for transmission. When the user taps the send button, the input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[1279] Data processing and analysis

[1280] The server receives user information sent from the device. Based on the received information, it suggests appropriate over-the-counter medications to address the patient's condition. Specifically, it retrieves information on the relevant over-the-counter medications from a database and selects the best medication based on the patient's symptoms. It then uses an interaction risk assessment algorithm to check for interactions between the current medication and the suggested over-the-counter medication. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). The side effect risk assessment module compares the results with the risk database to make an assessment.

[1281] The server also suggests appropriate preventive medicines (e.g., herbal medicines or supplements) based on the received information, past data, and current health status. Using a preventive medicine generation means, it retrieves candidate preventive medicines from the database and presents the most suitable one.

[1282] Returning the results

[1283] The server compiles the analysis results and generated preventive medication information and generates information to provide to the user, including recommended medications for symptoms and associated precautions. The generated information is then sent to the user's device using a secure communication protocol.

[1284] Displaying and notifying results

[1285] The device displays the received information to the user. Specifically, it displays "Acetaminophen is recommended. Consult your doctor before using ibuprofen." It also uses notification functionality to provide additional warnings to the user as needed, such as providing a link to purchase the recommended medication or other important information.

[1286] Overview of the hardware and software used

[1287] Hardware

[1288] Smartphones (general mobile devices)

[1289] A computer (typical desktop or laptop)

[1290] Server (cloud computing environment)

[1291] software

[1292] Mobile applications (general cross-platform development frameworks)

[1293] Web applications (general front-end frameworks)

[1294] Server-side software (popular back-end frameworks and scripting languages)

[1295] Database (general relational database management system)

[1296] Secure communication protocols (HTTPS, SSL / TLS)

[1297] Specific examples

[1298] User example: User A

[1299] 1. User A launches the app and wants to treat a headache.

[1300] 2. User A enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen.

[1301] 3. The device sends this information to the server.

[1302] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1303] 5. The server checks for interactions with the blood pressure medication the patient is already taking and determines that acetaminophen is a safe option.

[1304] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1305] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[1306] 8. The device displays this information to User A and also sends notifications if necessary.

[1307] This allows user A to select and take the appropriate medication with peace of mind.

[1308] Example prompt: "Enter your current symptoms and medications to identify potential interactions and risk of side effects. For example, 'I have a headache and am taking blood pressure medication. Can I take over-the-counter acetaminophen?'"

[1309] The above is a specific embodiment of this medicine management system.

[1310] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1311] Step 1:

[1312] The user launches the application. The app's home screen appears, providing a form for entering symptoms, medications, and allergies. The information entered is temporarily stored on the device for future submission.

[1313] Specific actions

[1314] The user taps the app icon.

[1315] The home screen will be displayed.

[1316] The input form will be displayed.

[1317] The user enters information using a keyboard or touch screen.

[1318] The terminal temporarily stores the input data in memory.

[1319] Input: User's symptoms, medications, and allergy information

[1320] Output: Temporarily saved input data

[1321] Step 2:

[1322] When the user taps the submit button, the entered information is sent to the server using a secure communication protocol (e.g., HTTPS).

[1323] Specific actions

[1324] The user taps the send button.

[1325] The terminal prepares the data for transmission and encrypts it.

[1326] The encrypted data is sent to the server.

[1327] Input: Temporarily saved input data, submit button tap event

[1328] Output: Encrypted data sent to the server

[1329] Step 3:

[1330] The server receives the information sent from the device. The received information includes the user's symptoms, medications they are taking, and allergy information. The received data is stored in a database for analysis.

[1331] Specific actions

[1332] A data reception module runs on the server.

[1333] The server decrypts the transmitted data.

[1334] The received data is stored in a database.

[1335] Input: Encrypted user data

[1336] Output: User data stored in the database

[1337] Step 4:

[1338] The server analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms. It also retrieves information on the corresponding over-the-counter medications from a database and selects the most appropriate medication.

[1339] Specific actions

[1340] The server uses the user's symptoms as a database search query.

[1341] Obtain a list of over-the-counter medications that address your symptoms from the database.

[1342] An algorithm selects the over-the-counter medication that best suits your symptoms.

[1343] Input: User's symptoms, medications, and allergy information stored in the database

[1344] Output: List of over-the-counter drugs and best over-the-counter drug information

[1345] Step 5:

[1346] The server uses an interaction risk assessment algorithm to check for interactions between current medications and proposed over-the-counter medications.

[1347] Specific actions

[1348] The server lists the medications you are taking and suggested over-the-counter medications.

[1349] Run the interaction risk assessment algorithm.

[1350] Generates an alert if a risk is detected.

[1351] Input: List of over-the-counter medications, information on medications currently being taken

[1352] Output: Interaction risk assessment results and warning information

[1353] Step 6:

[1354] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), and evaluates the risk using a side effect risk assessment module.

[1355] Specific actions

[1356] The server passes user information to the side effect risk assessment module.

[1357] The side effect risk assessment module checks against the risk database.

[1358] Generate risk assessment results.

[1359] Input: User's personal information stored in the database

[1360] Output: Side effect risk assessment results

[1361] Step 7:

[1362] The server generates an appropriate preventive medicine based on the received information, past data, and current health status. Using the preventive medicine generation means, candidate preventive medicines are extracted and the most suitable one is presented.

[1363] Specific actions

[1364] The server uses historical data and current information to generate a list of preventative medication candidates.

[1365] The preventive medicine generating means selects the optimum preventive medicine.

[1366] Input: User information stored in the database, past data

[1367] Output: List of preventive drug candidates and information on the optimal preventive drug

[1368] Step 8:

[1369] The server compiles the analysis results and information about the generated preventive medications, and generates information to provide to the user, including recommended medications for the symptoms and associated precautions.

[1370] Specific actions

[1371] The server aggregates the results of each analysis.

[1372] Generate user-friendly information in the form of text, charts, etc.

[1373] Input: Analysis results, preventive medicine information

[1374] Output: Information provided to the user (recommended medications, precautions, etc.)

[1375] Step 9:

[1376] The server transmits the generated information to the user's terminal using a secure communication protocol.

[1377] Specific actions

[1378] The server encrypts the data and prepares it for transmission.

[1379] The encrypted data is sent to the terminal.

[1380] Input: Information to be provided to the user (recommended medications, precautions, etc.)

[1381] Output: Encrypted information sent to the user's device

[1382] Step 10:

[1383] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[1384] Specific actions

[1385] The terminal decrypts the received data.

[1386] Present the data in a format suitable for the user interface.

[1387] Input: Encrypted information

[1388] Output: Information displayed to the user

[1389] Step 11:

[1390] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1391] Specific actions

[1392] The device generates a notification.

[1393] Notify the user in the form of a popup or message.

[1394] Input: Notification information to provide to users

[1395] Output: Warnings and links notified to the user

[1396] The above are the specific processing steps of the medicine management system of the present invention.

[1397] (Application example 1)

[1398] 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."

[1399] Currently, drug selection, interaction checks, and side effect risk analysis must be done individually, which is cumbersome for users. Even when an appropriate drug is presented, the purchasing process is often time-consuming and laborious. This makes it difficult to obtain over-the-counter drugs quickly and safely.

[1400] 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.

[1401] In this invention, the server includes an input means for inputting information about drugs being taken by the user and symptoms, a receiving means for receiving information transmitted from the input means, a suggestion means for analyzing the received information and suggesting appropriate over-the-counter drugs based on the symptoms, an interaction checking means for checking interactions between the drugs being taken and the suggested over-the-counter drugs, a side effect risk analysis means for analyzing the risk of side effects based on the user's personal information, an information generating means for summarizing the analysis results and providing them to the user, a transmitting means for transmitting the information generated by the information generating means to the user's terminal, a display means for displaying the information transmitted by the transmitting means on the user's terminal, and an electronic payment means for quickly and safely purchasing the suggested over-the-counter drugs. This enables a unified process from drug selection, interaction checking, and side effect risk analysis to the prompt purchase of appropriate over-the-counter drugs.

[1402] "Input means" refers to a device or interface that allows a user to input information such as information about medications being taken and symptoms.

[1403] The "receiving means" is a device or system that allows the server to receive the information transmitted from the input means.

[1404] The "suggestion means" is a device or system that analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms.

[1405] An "interaction checking means" is a device or system for checking interactions between medications currently being taken and proposed over-the-counter medications and conducting risk assessments.

[1406] "Side effect risk analysis means" refers to a device or system for analyzing and evaluating side effect risks based on the user's personal information.

[1407] The "information generating means" is a device or system that generates information to be provided to users based on the analysis results.

[1408] The "transmission means" is a device or system for transmitting the information generated by the information generation means to the user's terminal.

[1409] "Display means" refers to a device or interface for displaying the transmitted information on the user's terminal.

[1410] "Electronic payment method" means an electronic payment method or system for the fast and secure purchase of the proposed over-the-counter drug.

[1411] This invention is a drug management system that utilizes generative AI, and can suggest appropriate over-the-counter drugs, check for drug interactions, and analyze the risk of side effects based on the drug information and symptoms entered by the user. It also has an electronic payment method for quickly and safely purchasing the suggested over-the-counter drugs.

[1412] Overall system configuration

[1413] The system consists of the following elements:

[1414] 1. Input method: An interface (e.g., smartphone app) through which users can input information about symptoms and medications they are taking.

[1415] 2. Receiving means: A server system (using a Flask application) that receives information sent from the input means.

[1416] 3. Recommendation method: A system that analyzes the received information and suggests over-the-counter medications that address the symptoms.

[1417] 4. Interaction Checking Measures: A system to check for interactions between medications currently being taken and proposed over-the-counter medications.

[1418] 5. Side effect risk analysis means: A system that analyzes the risk of side effects based on the user's personal information.

[1419] 6. Information generation means: A system that generates information to be provided to users based on the analysis results.

[1420] 7. Transmission means: A system that transmits the information generated by the information generation means to the user's terminal.

[1421] 8. Display means: An interface that displays the transmitted information on the user's terminal.

[1422] 9. Electronic Payment Methods: An electronic payment system for fast and secure purchase of proposed over-the-counter medications.

[1423] Data processing and analysis description

[1424] The server receives the drug information and symptoms sent by the user and analyzes that information using a generative AI model. Specifically, it retrieves appropriate over-the-counter drugs from a database based on the symptoms and checks for interactions between each drug and any other medications the user is currently taking. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.).

[1425] The information generation means compiles these analysis results and generates information including recommended medicines and precautions for the user. The generated information is sent to the user's terminal via a secure communication protocol.

[1426] The electronic payment method provides a payment interface that includes multiple payment methods (credit cards, electronic money, QR code payment, etc.) so that users can instantly purchase the suggested over-the-counter medications.

[1427] Examples of concrete examples and prompts

[1428] As a concrete example, let's say a user wants to treat a "headache." In this case, the user launches the app and enters "headache," "current antihypertensive medication: Losartan," and "no allergies." The device sends this information to the server, which analyzes it and notifies the user that acetaminophen is recommended. The user can also quickly complete the purchase process within the app.

[1429] An example prompt has the following format:

[1430] "Please enter your symptoms and medications:"

[1431] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1432] Step 1:

[1433] The user launches the smartphone app and inputs symptoms, medication information, and allergy information. The user enters this information into the input form and presses the send button, temporarily saving the data on the device. The input data includes symptoms (e.g., headache), medications being taken (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies). The output is the input data temporarily saved on the device.

[1434] Step 2:

[1435] The terminal sends the information entered by the user to the server using a secure communication protocol (e.g., TLS / SSL). At this time, the terminal converts the temporarily saved data into JSON format and sends it as an HTTPS request. The input is the temporarily saved data, and the output is the data sent to the server.

[1436] Step 3:

[1437] The server receives the information sent from the device and stores it in a database. At the same time, it begins analyzing the received data. Specifically, it retrieves a list of over-the-counter medications that match the symptoms from the database and proceeds with the analysis. The input is the data sent from the device, and the output is the list of over-the-counter medications from the database used for analysis.

[1438] Step 4:

[1439] The server uses a generative AI model to suggest appropriate over-the-counter medications based on the received data (user symptoms, medications being taken, and allergy information). This generative AI model analyzes over-the-counter medication information corresponding to symptoms in the database and makes an appropriate selection. The input is symptoms and medication information, and the output is a list of suggested over-the-counter medications.

[1440] Step 5:

[1441] The server checks interactions between the proposed over-the-counter drugs and the medications the user is currently taking. To do this, it uses a pre-built interaction checking algorithm to evaluate the interaction risk between each over-the-counter drug and the medication the user is currently taking. The input is the list of proposed over-the-counter drugs and information about the medications the user is currently taking, and the output is the evaluation result of the interaction risk.

[1442] Step 6:

[1443] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). It obtains information about side effects from the database and performs a risk assessment based on that information. The input is personal information and drug information, and the output is the assessment result of the side effect risk.

[1444] Step 7:

[1445] The server generates information to provide to users based on these analysis results. The generated information includes suggested over-the-counter medications and precautions. The information generation means uses a generative AI model to appropriately summarize the analysis results and convert them into a format optimized for user viewing. The input is the analysis results, and the output is the generated information.

[1446] Step 8:

[1447] The server sends the generated information to the user's device using a secure communication protocol. At this time, the generated information is sent in an appropriate format (e.g., JSON format). The input is the generated information, and the output is the sent information. TLS / SSL is used for transmission to ensure data security.

[1448] Step 9:

[1449] The device displays the received information to the user, specifically, the suggested over-the-counter drug information and warnings using an appropriate interface, and sends notifications as needed. The input is the received information, and the output is the displayed information and sent notifications.

[1450] Step 10:

[1451] The user then goes through the process of purchasing the over-the-counter medication suggested within the app. The payment is completed quickly and securely using electronic payment methods. Various payment methods (credit cards, electronic money, QR code payment, etc.) are integrated into the interface. The input is the intention to purchase and payment information, and the output is a notification that the purchase has been completed.

[1452] 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.

[1453] The present invention is a system that provides advice and drug suggestions according to the user's mental state by combining a drug management system that utilizes generative AI with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[1454] 1. Enter user information

[1455] 1.1 Application launch

[1456] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[1457] 1.2 Entering symptoms and medication information

[1458] The user enters their symptoms (e.g., headache), medications (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[1459] 1.3 Data Transmission

[1460] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[1461] 2. Data processing and analysis

[1462] 2.1 Data Reception

[1463] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[1464] 2.2 Symptom-based medication suggestions

[1465] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[1466] 2.3 Emotion Analysis

[1467] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[1468] 2.4 Drug interaction check

[1469] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[1470] 2.5 Analysis of side effect risks

[1471] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[1472] 2.6 Adjusting Suggestions Based on Emotions

[1473] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is experiencing high stress, it will provide medications and advice that have a calming effect.

[1474] 3. Returning the results

[1475] 3.1 Generating Results

[1476] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It also includes advice based on the patient's emotional state.

[1477] 3.2 Data Transmission

[1478] The server sends the generated information to the user's terminal using a secure communication protocol.

[1479] 4. Display and notification of results

[1480] 4.1 Displaying the results

[1481] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[1482] 4.2 Caution Notice

[1483] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1484] Specific examples

[1485] User example: Hanako

[1486] 1. Hanako launches the app and wants to treat her headache.

[1487] 2. Hanako enters "headache," "hypertensive medication currently being taken: Losartan," and "no allergies" into the form on the home screen. She also uses voice input to say, "I've been feeling stressed lately."

[1488] 3. The device sends this information to the server.

[1489] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1490] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[1491] 6. The server analyzes the emotional data and recognizes that Hanako is under a lot of stress.

[1492] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1493] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[1494] 9. The device will display this information to Mary and send her notifications if necessary.

[1495] This allows Hanako to choose and take the appropriate medication with peace of mind, and also receive advice tailored to her own mental state.

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] When a user launches the application, they are presented with a home screen, which includes a form where they can enter information about their symptoms, medications, and allergies. Additionally, a voice input button is provided to visualize their emotional state.

[1499] Step 2:

[1500] The user inputs symptoms into the form. For example, the user inputs "headache." The user also selects "Losartan, an antihypertensive drug" as the medication currently being taken, and selects "No allergies" as the allergy information.

[1501] Step 3:

[1502] The user presses the voice input button and provides an emotional state through voice input, for example, saying, "I've been feeling stressed lately."

[1503] Step 4:

[1504] The device temporarily stores the entered data (symptoms, medications, allergy information, and voice data) and prepares it for transmission.

[1505] Step 5:

[1506] The device sends the user's input information and voice data to the server, using a secure communication protocol to keep the data safe.

[1507] Step 6:

[1508] The server receives information from the device, including symptoms, medications being taken, allergy information, and emotional data.

[1509] Step 7:

[1510] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[1511] Step 8:

[1512] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[1513] Step 9:

[1514] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[1515] Step 10:

[1516] The server analyzes the voice input data and recognizes the user's emotional state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[1517] Step 11:

[1518] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[1519] Step 12:

[1520] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[1521] Step 13:

[1522] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if stress levels are high, it will provide medications and advice with a calming effect.

[1523] Step 14:

[1524] The server compiles the analysis results and generates information including appropriate medication suggestions, precautions, and emotion-based advice.

[1525] Step 15:

[1526] The server sends the generated information to the user's terminal using a secure communication protocol.

[1527] Step 16:

[1528] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[1529] Step 17:

[1530] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1531] Example 2

[1532] 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."

[1533] Conventional drug management systems suggest appropriate over-the-counter medications based on the user's current medication information and symptoms, but they are unable to provide advice that takes into account the user's emotional state. As a result, it is difficult for users to receive appropriate advice when they are stressed or mentally unstable. In addition, there is a need to improve safety due to insufficient analysis of drug interactions and side effect risks.

[1534] 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.

[1535] In this invention, the server includes input means for inputting information about medications the user is taking, symptoms, and emotional state, receiving means for receiving information transmitted from the input means, suggestion means for analyzing the received information and suggesting appropriate medications based on the symptoms, interaction checking means for checking interactions between the medications the user is taking and the suggested medications, side effect risk analysis means for analyzing the risk of side effects based on the user's personal information and emotional state, information generation means for summarizing the analysis results and providing them to the user, transmission means for transmitting the information generated by the information generation means to the user's terminal, and display means for displaying the information transmitted by the transmission means on the user's terminal. This enables accurate advice and medication suggestions based on the user's emotional state, thereby improving safety and psychological support.

[1536] "User" refers to a person who uses the system to input their symptoms, information about the medications they are taking, and their emotional state, and receives appropriate medication suggestions and advice.

[1537] "Drug information" refers to detailed information such as the name, dosage, and usage instructions of the medications the user is currently taking.

[1538] "Symptom" means information that describes a physical or mental illness or condition that a user is currently experiencing.

[1539] "Emotional state" refers to information that indicates a user's current state of mind or emotion, such as stress, anxiety, or calmness.

[1540] "Input means" refers to the interface through which users input their medication information, symptoms, and emotional state into the system. Specifically, this includes form entry and voice input functions.

[1541] The "receiving means" refers to a function that allows the system to acquire information sent from the input means.

[1542] The "suggestion means" refers to a function for analyzing the information acquired by the receiving means and suggesting an appropriate medication based on the symptoms of the user.

[1543] "Interaction checking means" refers to a function for analyzing interactions between the medications the user is taking and the proposed medication and assessing the risks.

[1544] "Side effect risk analysis means" refers to a function for assessing the side effect risk of a proposed drug based on the user's personal information and emotional state.

[1545] "Information generation means" refers to the function by which the system compiles the analysis results and generates information to be provided to users.

[1546] The "transmission means" refers to a function for transmitting the information generated by the information generation means to the user's terminal.

[1547] The "display means" refers to a function for displaying the information transmitted by the transmission means on the user terminal.

[1548] "Database" refers to an information management system that stores information on drugs, interactions, risk of side effects, etc., and makes it accessible to the system as needed.

[1549] "HTTPS" is a protocol for securely sending and receiving data over the Internet, and refers to technology that ensures data protection.

[1550] MODE FOR CARRYING OUT THE INVENTION

[1551] This invention is a system that provides advice and drug suggestions based on the user's mental state by combining a drug management system that utilizes a generative AI model with an emotion engine that recognizes the user's emotions. This system is implemented as an application that can be accessed from a smartphone or computer.

[1552] 1. Enter user information

[1553] 1.1 Application launch

[1554] Users launch the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[1555] 1.2 Entering symptoms and medication information

[1556] The user enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensives), and allergy information (e.g., no allergies) into the form. They also use voice input to provide emotional status information, such as "I've been feeling stressed lately." The device temporarily stores the entered data and prepares it for transmission.

[1557] 1.3 Data Transmission

[1558] The device transmits the information and emotion data entered by the user to the server using secure communication technology such as the HTTPS protocol.

[1559] 2. Data processing and analysis

[1560] 2.1 Data Reception

[1561] The server receives the information sent from the terminal, including the user's symptoms, medications being taken, allergy information, and emotional data.

[1562] 2.2 Symptom-based medication suggestions

[1563] The server analyzes the received information and suggests appropriate medications for the user's symptoms. For example, it retrieves information on medications for "headache" (e.g., acetaminophen, ibuprofen) from a database and selects the most appropriate medication.

[1564] 2.3 Emotion Analysis

[1565] The server uses an emotion engine to analyze emotion data, including voice input data, and recognize the user's mental state (e.g., stress, anxiety, calm).

[1566] 2.4 Drug interaction check

[1567] The server checks for interactions between current medications (e.g., antihypertensives) and proposed over-the-counter medications (e.g., acetaminophen, ibuprofen), and assesses the risk using a risk assessment algorithm.

[1568] 2.5 Analysis of side effect risks

[1569] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information about the risk of side effects from the database, and makes an assessment.

[1570] 2.6 Adjusting Suggestions Based on Emotions

[1571] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide advice on medications with a calming effect or to promote relaxation.

[1572] 3. Returning the results

[1573] 3.1 Generating Results

[1574] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[1575] 3.2 Data Transmission

[1576] The server sends the generated information to the user's device using secure communication technology such as the HTTPS protocol.

[1577] 4. Display and notification of results

[1578] 4.1 Displaying the results

[1579] The device then displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[1580] 4.2 Caution Notice

[1581] If necessary, the device will use notifications to provide additional user attention, such as providing links to purchase recommended medications or other important information.

[1582] Specific examples

[1583] User example: A user

[1584] 1. A user launches the app and wants to treat a headache.

[1585] 2. The user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[1586] 3. The device sends this information to the server.

[1587] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1588] 5. The server checks for interactions with antihypertensive medications and determines that acetaminophen is a safe option.

[1589] 6. The server analyzes the emotional data and recognizes that the user is under high stress.

[1590] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1591] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[1592] 9. The device displays this information to the user and also sends notifications if necessary.

[1593] This example allows users to choose and take appropriate medication with peace of mind, and also receive advice tailored to their own mental state.

[1594] Example prompts for generative AI models

[1595] Input prompt: "Please enter your headache symptoms and tell me about over-the-counter medications that can be taken with your current blood pressure medication. If you've been under a lot of stress lately, what medication would be appropriate?"

[1596] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1597] Step 1:

[1598] The user launches the application on their smartphone or computer. The home screen displays a form for entering symptoms, medication information, and allergy information. The user enters their symptoms (e.g., headache), medications (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into the form, and provides their emotional state, such as "I've been feeling stressed lately," via voice input. The entered data is temporarily stored on the device.

[1599] Input: User's symptoms, medications, allergy information, emotional state

[1600] Output: Saved user data

[1601] Step 2:

[1602] The device sends the stored user data to the server using the secure HTTPS protocol. The server receives and stores the information sent from the device.

[1603] Input: Saved user data

[1604] Output: User data stored on the server

[1605] Step 3:

[1606] The server analyzes the received information. First, it retrieves medication information corresponding to the symptoms from the database (e.g., acetaminophen or ibuprofen for headaches) and selects the most appropriate medication. Next, it converts the voice input data into text and uses an emotion engine to analyze the user's emotional state (e.g., stress, anxiety, calm).

[1607] Input: User data stored on the server

[1608] Output: Analysis results (suggested medication for symptoms and user's emotional state)

[1609] Step 4:

[1610] The server checks interactions between current medications (e.g., antihypertensives) and proposed over-the-counter drugs (e.g., acetaminophen, ibuprofen), queries an interaction database, and assesses the risk of interaction using a risk assessment algorithm.

[1611] Input: Analysis results (suggested medications), medication information

[1612] Output: Interaction check results (risk assessment)

[1613] Step 5:

[1614] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and uses a risk assessment model to select the most appropriate drug candidate.

[1615] Input: User's personal information, suggested medication

[1616] Output: Side effect risk assessment results

[1617] Step 6:

[1618] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide sedative medications and advice on how to relax.

[1619] Input: Side effect risk assessment results, emotion analysis results

[1620] Output: Tailored suggestions and advice

[1621] Step 7:

[1622] The server compiles the analysis results and generates information for the user, including suggestions for appropriate medications and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[1623] Input: Tailored suggestions and advice

[1624] Output: Generated suggestions and warning information

[1625] Step 8:

[1626] The server sends the generated information to the user's device using the secure HTTPS protocol, where it displays the received information to the user and, if necessary, uses a notification function to provide additional attention.

[1627] Input: Generated suggestions and warning information

[1628] Output: Information displayed to the user and any necessary notifications

[1629] (Application example 2)

[1630] 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."

[1631] Conventional drug management systems did not provide advice or suggestions based on the user's emotional state, and could only make uniform suggestions regardless of the user's mental state. This made it difficult to provide comprehensive health management that took into account mental factors such as stress and anxiety, and to suggest appropriate over-the-counter medications and dietary recommendations. Furthermore, drug-food interactions were often not sufficiently considered.

[1632] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a suggestion means that provides advice and dietary suggestions appropriate to the user's mental state based on emotion data analyzed using an emotion engine, a suggestion means that analyzes received information and suggests appropriate over-the-counter medications based on symptoms, and an interaction check means that checks interactions between medications currently being taken and the suggested over-the-counter medications. This enables comprehensive health management that takes the user's mental state into consideration.

[1633] The "input means" is a device through which the user inputs drug information, symptoms, allergy information, and emotions.

[1634] The "receiving means" is a device that receives information transmitted from the input means.

[1635] The "suggestion means" is a device that analyzes the received information and suggests appropriate over-the-counter medicines based on symptoms, as well as advice and diet appropriate to the patient's mental state based on emotional data.

[1636] An "interaction checking means" is a device that checks for interactions between a drug being taken and a proposed over-the-counter drug, as well as interactions with multiple drugs and foods.

[1637] The "side effect risk analysis means" is a device that analyzes the side effect risk based on the user's personal information.

[1638] An "emotion engine" is a program or device that analyzes emotional data such as voice input and understands the user's mental state.

[1639] The "information generating means" is a device that compiles the analysis results and generates information to be provided to the user.

[1640] The "transmitting means" is a device that transmits the information generated by the information generating means to the user's terminal.

[1641] The "display means" is a device that displays the information transmitted by the transmission means on the user terminal.

[1642] 1. Enter user information

[1643] 1.1 Application launch

[1644] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[1645] 1.2 Entering symptoms and medication information

[1646] The user enters their symptoms (e.g., headache), medications (e.g., antihypertensives), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[1647] 1.3 Data Transmission

[1648] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[1649] 2. Data processing and analysis

[1650] 2.1 Data Reception

[1651] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[1652] 2.2 Symptom-based medication suggestions

[1653] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the appropriate over-the-counter medications from a database and selects the best medication based on the symptoms.

[1654] 2.3 Emotion Analysis

[1655] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). It does this using an emotion engine.

[1656] 2.4 Drug interaction check

[1657] The server checks interactions between medications currently being taken (e.g., antihypertensives) and potential over-the-counter drugs (e.g., acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[1658] 2.5 Analysis of side effect risks

[1659] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[1660] 2.6 Adjusting Suggestions Based on Emotions

[1661] The server then adjusts the selection of suggested over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is under high stress, it will provide medications and advice with a calming effect. It will also suggest easy-to-digest meals.

[1662] 3. Returning the results

[1663] 3.1 Generating Results

[1664] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, but note that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the patient's emotional state.

[1665] 3.2 Data Transmission

[1666] The server sends the generated information to the user's terminal using a secure communication protocol.

[1667] 4. Display and notification of results

[1668] 4.1 Displaying the results

[1669] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[1670] 4.2 Caution Notice

[1671] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1672] Specific examples

[1673] User example: General user

[1674] 1. A regular user launches the app and wants to treat a headache.

[1675] 2. A general user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[1676] 3. The device sends this information to the server.

[1677] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1678] 5. The server checks for interactions with the blood pressure medication being taken and determines that acetaminophen is a safe option.

[1679] 6. The server analyzes the emotional data and recognizes that the general user is under high stress.

[1680] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1681] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[1682] 9. The device displays this information to the general user and also sends notifications if necessary.

[1683] This allows general users to choose and take appropriate medication with peace of mind, and also receive advice based on their own mental state.

[1684] Prompt Sentence Examples

[1685] "I'm feeling stressed at work. Can you suggest some foods that will help relieve stress?"

[1686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1687] Step 1:

[1688] The user launches the application on their smartphone or computer and inputs their symptoms, medication information, and allergy information. Using the input means, they enter this information into a form or record their emotional state using voice input. The input data is temporarily stored on the device.

[1689] input:

[1690] Symptoms (e.g. headache)

[1691] Medications you are taking (e.g. antihypertensive drugs)

[1692] Allergy information (e.g., no allergies)

[1693] Emotional information (e.g., voice input of stress)

[1694] output:

[1695] User information temporarily stored on the device

[1696] Step 2:

[1697] The terminal transmits the input user information and emotion data to the server using a secure communication protocol. The data arrives at the server using the transmission means.

[1698] input:

[1699] User information stored on the device

[1700] output:

[1701] User information sent to the server

[1702] Step 3:

[1703] The server receives the information transmitted from the terminal and prepares to analyze the received information using the receiving means.

[1704] input:

[1705] User information sent from the device

[1706] output:

[1707] Received information ready for analysis

[1708] Step 4:

[1709] The server analyzes the received information and suggests appropriate over-the-counter medications based on the patient's symptoms. It also retrieves information on the appropriate over-the-counter medications from a database and selects the best medication based on the patient's symptoms.

[1710] input:

[1711] Reception information (symptoms)

[1712] output:

[1713] Symptom-based over-the-counter medication suggestions

[1714] Step 5:

[1715] The server analyzes the emotion data, including the voice input data, and uses the emotion engine to recognize the user's mental state. Based on the analysis results, the server identifies the user's emotional state.

[1716] input:

[1717] Received information (emotion data)

[1718] output:

[1719] Analyzed emotional state (e.g., stress state)

[1720] Step 6:

[1721] The server uses an interaction checker to check for interactions between the currently taken medication and the potential over-the-counter medication, and evaluates the risk using an interaction risk assessment algorithm.

[1722] input:

[1723] Information about medications you are taking

[1724] Marketed drug candidate information

[1725] output:

[1726] Interaction risk assessment results

[1727] Step 7:

[1728] The server analyzes the risk of side effects based on the user's personal information, retrieves information from the database, and evaluates the risk of side effects for recommended over-the-counter drugs.

[1729] input:

[1730] Received information (personal information)

[1731] Marketed drug candidate information

[1732] output:

[1733] Side effect risk assessment results

[1734] Step 8:

[1735] The server then adjusts the suggested medicines and advice based on the analysis results of the emotion engine. For example, if the patient is under high stress, it will suggest medicines with a calming effect or foods that will help them relax.

[1736] input:

[1737] Analyzed emotional state

[1738] Symptom-based general drug information

[1739] output:

[1740] Tailored medication and dietary suggestions

[1741] Step 9:

[1742] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions, and sends the generated information to the user's terminal using the information generation means.

[1743] input:

[1744] Tailored Proposal Information

[1745] output:

[1746] Information sent to the user's device

[1747] Step 10:

[1748] The device displays the received information to the user, and uses notifications to provide the user with appropriate medications, advice, and additional precautions as needed.

[1749] input:

[1750] Information sent from the server

[1751] output:

[1752] Information and notifications displayed to users

[1753] Through these steps, users can choose and take the appropriate medication, and receive advice based on their mental state. This system utilizes generative AI models and an emotion engine to achieve more comprehensive health management.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] [Fourth embodiment]

[1758] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1759] 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.

[1760] 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).

[1761] 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.

[1762] 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.

[1763] 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).

[1764] 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.

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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."

[1771] This invention is a drug management system that utilizes generative AI, and by having users input information about the drugs they are taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, and analyzes the risk of side effects. This system is implemented as follows.

[1772] 1. Enter user information

[1773] 1.1 Application launch

[1774] The user launches the application on their smartphone or computer, and the home screen of the application displays a form for entering information about symptoms, medications they are taking, and allergies.

[1775] 1.2 Entering symptoms and medication information

[1776] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. The device temporarily stores the entered data and prepares it for transmission.

[1777] 1.3 Data Transmission

[1778] The terminal transmits the information entered by the user to the server using a secure communication protocol.

[1779] 2. Data processing and analysis

[1780] 2.1 Data Reception

[1781] The server receives the information sent from the device, including the user's symptoms, medications they are taking, and allergy information.

[1782] 2.2 Symptom-based medication suggestions

[1783] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[1784] 2.3 Drug interaction check

[1785] The server checks for interactions between the current medication (losartan) and the proposed over-the-counter medications (e.g., ibuprofen, acetaminophen). Using an interaction risk assessment algorithm, the server evaluates the interaction between each over-the-counter medication and losartan. The server determines that ibuprofen requires caution due to its high risk of interaction with losartan.

[1786] 2.4 Analysis of side effect risks

[1787] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[1788] 2.5 Prophylactic drug production

[1789] The server will suggest appropriate preventive medicines (herbal medicines or supplements) based on individual user information, past data, and current health status. It will then extract candidate preventive medicines from a database and present the most appropriate one.

[1790] 3. Returning the results

[1791] 3.1 Generating Results

[1792] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "care should be taken with ibuprofen" due to the risk of interactions with antihypertensive drugs.

[1793] 3.2 Data Transmission

[1794] The server sends the generated information to the user's terminal using a secure communication protocol.

[1795] 4. Display and notification of results

[1796] 4.1 Displaying the results

[1797] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[1798] 4.2 Caution Notice

[1799] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1800] Specific examples

[1801] User example: Hanako

[1802] 1. Hanako launches the app and wants to treat her headache.

[1803] 2. Hanako enters "headache," "antihypertensive medication currently being taken: losartan," and "no allergies" into the form on the home screen.

[1804] 3. The device sends this information to the server.

[1805] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1806] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[1807] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1808] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[1809] 8. The device will display this information to Mary and send her notifications if necessary.

[1810] This will allow Hanako to choose and take the appropriate medication with peace of mind.

[1811] The processing flow will be explained below.

[1812] Step 1:

[1813] The user launches the application and the home screen appears.

[1814] Step 2:

[1815] The user enters their symptoms (e.g., headache), medications they are taking (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into the form.

[1816] Step 3:

[1817] The terminal temporarily stores the input data and prepares it for transmission.

[1818] Step 4:

[1819] The terminal transmits the user's input information to the server.

[1820] Step 5:

[1821] The server receives information from the device, including symptoms, medications being taken, and allergy information.

[1822] Step 6:

[1823] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[1824] Step 7:

[1825] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[1826] Step 8:

[1827] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[1828] Step 9:

[1829] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[1830] Step 10:

[1831] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[1832] Step 11:

[1833] The server compiles the analysis results and generates symptom-based medication suggestions, interaction check results, and information on side effect risks.

[1834] Step 12:

[1835] The server transmits the generated information to the terminal using a secure communication protocol.

[1836] Step 13:

[1837] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[1838] Step 14:

[1839] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1840] Example 1

[1841] 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."

[1842] Conventional drug management systems suggest appropriate over-the-counter medications and check for drug interactions based on the drug information and symptoms entered by the user, but they often do not adequately suggest preventative medications or provide additional warnings. As a result, users may not receive sufficient information about the risk of side effects and drug interactions, leading to inappropriate drug selection. Furthermore, there were few systems that took into account interactions with multiple drugs and foods. The purpose of this invention is to solve these problems and realize safe and effective drug management.

[1843] 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.

[1844] In this invention, the server includes an input means for the user to input information about medications being taken and symptoms, a preventive medicine generation means for generating an appropriate preventive medicine based on the received information and analysis results, and a notification means for notifying the user of additional precautions. This makes it possible to suggest safe and effective over-the-counter medications based on the information input by the user, analyze drug interactions and side effect risks, and even generate preventive medicines and provide warnings.

[1845] The "input means" is an interface that allows the user to input information about medications currently being taken, symptoms, allergies, and the like.

[1846] The "receiving means" is a function for receiving information sent from the input means.

[1847] The "suggestion means" is a function for analyzing the received information and suggesting appropriate over-the-counter medications based on the symptoms.

[1848] The "interaction checker" is a function for checking interactions between the medication being taken and proposed over-the-counter medications, multiple medications, and foods.

[1849] The "side effect risk analysis means" is a function for analyzing the risk of side effects based on the user's personal information.

[1850] The "prophylactic medicine generating means" is a function for generating an appropriate prophylactic medicine based on the received information and analysis results.

[1851] The "information generating means" is a function that compiles the analysis results and the generated preventive medicines, and generates information to be provided to the user.

[1852] The "transmission means" is a function for transmitting the information generated by the information generation means to the user's terminal.

[1853] The "display means" is a function for displaying the information transmitted by the transmission means on the user terminal.

[1854] "Notification means" is a function for notifying the user of additional precautions.

[1855] This invention is a drug management system that utilizes a generative AI model. By inputting information about the drugs a user is taking and their symptoms, the system suggests appropriate over-the-counter drugs, checks for drug interactions, analyzes the risk of side effects, and even generates preventative medications and provides necessary warnings. This system is implemented as follows.

[1856] Enter user information

[1857] The user launches the application on their smartphone or computer and enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into a form displayed on the home screen. The device temporarily stores this information and prepares it for transmission. When the user taps the send button, the input data is sent to the server using a secure communication protocol (e.g., HTTPS).

[1858] Data processing and analysis

[1859] The server receives user information sent from the device. Based on the received information, it suggests appropriate over-the-counter medications to address the patient's condition. Specifically, it retrieves information on the relevant over-the-counter medications from a database and selects the best medication based on the patient's symptoms. It then uses an interaction risk assessment algorithm to check for interactions between the current medication and the suggested over-the-counter medication. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). The side effect risk assessment module compares the results with the risk database to make an assessment.

[1860] The server also suggests appropriate preventive medicines (e.g., herbal medicines or supplements) based on the received information, past data, and current health status. Using a preventive medicine generation means, it retrieves candidate preventive medicines from the database and presents the most suitable one.

[1861] Returning the results

[1862] The server compiles the analysis results and generated preventive medication information and generates information to provide to the user, including recommended medications for symptoms and associated precautions. The generated information is then sent to the user's device using a secure communication protocol.

[1863] Displaying and notifying results

[1864] The device displays the received information to the user. Specifically, it displays "Acetaminophen is recommended. Consult your doctor before using ibuprofen." It also uses notification functionality to provide additional warnings to the user as needed, such as providing a link to purchase the recommended medication or other important information.

[1865] Overview of the hardware and software used

[1866] Hardware

[1867] Smartphones (general mobile devices)

[1868] A computer (typical desktop or laptop)

[1869] Server (cloud computing environment)

[1870] software

[1871] Mobile applications (general cross-platform development frameworks)

[1872] Web applications (general front-end frameworks)

[1873] Server-side software (popular back-end frameworks and scripting languages)

[1874] Database (general relational database management system)

[1875] Secure communication protocols (HTTPS, SSL / TLS)

[1876] Specific examples

[1877] User example: User A

[1878] 1. User A launches the app and wants to treat a headache.

[1879] 2. User A enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen.

[1880] 3. The device sends this information to the server.

[1881] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[1882] 5. The server checks for interactions with the blood pressure medication the patient is already taking and determines that acetaminophen is a safe option.

[1883] 6. The server analyzes the risk of side effects and confirms that there are no particular problems.

[1884] 7. The server summarizes the analysis results, generates the message "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor," and sends it to the terminal.

[1885] 8. The device displays this information to User A and also sends notifications if necessary.

[1886] This allows user A to select and take the appropriate medication with peace of mind.

[1887] Example prompt: "Enter your current symptoms and medications to identify potential interactions and risk of side effects. For example, 'I have a headache and am taking blood pressure medication. Can I take over-the-counter acetaminophen?'"

[1888] The above is a specific embodiment of this medicine management system.

[1889] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1890] Step 1:

[1891] The user launches the application. The app's home screen appears, providing a form for entering symptoms, medications, and allergies. The information entered is temporarily stored on the device for future submission.

[1892] Specific actions

[1893] The user taps the app icon.

[1894] The home screen will be displayed.

[1895] The input form will be displayed.

[1896] The user enters information using a keyboard or touch screen.

[1897] The terminal temporarily stores the input data in memory.

[1898] Input: User's symptoms, medications, and allergy information

[1899] Output: Temporarily saved input data

[1900] Step 2:

[1901] When the user taps the submit button, the entered information is sent to the server using a secure communication protocol (e.g., HTTPS).

[1902] Specific actions

[1903] The user taps the send button.

[1904] The terminal prepares the data for transmission and encrypts it.

[1905] The encrypted data is sent to the server.

[1906] Input: Temporarily saved input data, submit button tap event

[1907] Output: Encrypted data sent to the server

[1908] Step 3:

[1909] The server receives the information sent from the device. The received information includes the user's symptoms, medications they are taking, and allergy information. The received data is stored in a database for analysis.

[1910] Specific actions

[1911] A data reception module runs on the server.

[1912] The server decrypts the transmitted data.

[1913] The received data is stored in a database.

[1914] Input: Encrypted user data

[1915] Output: User data stored in the database

[1916] Step 4:

[1917] The server analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms. It also retrieves information on the corresponding over-the-counter medications from a database and selects the most appropriate medication.

[1918] Specific actions

[1919] The server uses the user's symptoms as a database search query.

[1920] Obtain a list of over-the-counter medications that address your symptoms from the database.

[1921] An algorithm selects the over-the-counter medication that best suits your symptoms.

[1922] Input: User's symptoms, medications, and allergy information stored in the database

[1923] Output: List of over-the-counter drugs and best over-the-counter drug information

[1924] Step 5:

[1925] The server uses an interaction risk assessment algorithm to check for interactions between current medications and proposed over-the-counter medications.

[1926] Specific actions

[1927] The server lists the medications you are taking and suggested over-the-counter medications.

[1928] Run the interaction risk assessment algorithm.

[1929] Generates an alert if a risk is detected.

[1930] Input: List of over-the-counter medications, information on medications currently being taken

[1931] Output: Interaction risk assessment results and warning information

[1932] Step 6:

[1933] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.), and evaluates the risk using a side effect risk assessment module.

[1934] Specific actions

[1935] The server passes user information to the side effect risk assessment module.

[1936] The side effect risk assessment module checks against the risk database.

[1937] Generate risk assessment results.

[1938] Input: User's personal information stored in the database

[1939] Output: Side effect risk assessment results

[1940] Step 7:

[1941] The server generates an appropriate preventive medicine based on the received information, past data, and current health status. Using the preventive medicine generation means, candidate preventive medicines are extracted and the most suitable one is presented.

[1942] Specific actions

[1943] The server uses historical data and current information to generate a list of preventative medication candidates.

[1944] The preventive medicine generating means selects the optimum preventive medicine.

[1945] Input: User information stored in the database, past data

[1946] Output: List of preventive drug candidates and information on the optimal preventive drug

[1947] Step 8:

[1948] The server compiles the analysis results and information about the generated preventive medications, and generates information to provide to the user, including recommended medications for the symptoms and associated precautions.

[1949] Specific actions

[1950] The server aggregates the results of each analysis.

[1951] Generate user-friendly information in the form of text, charts, etc.

[1952] Input: Analysis results, preventive medicine information

[1953] Output: Information provided to the user (recommended medications, precautions, etc.)

[1954] Step 9:

[1955] The server transmits the generated information to the user's terminal using a secure communication protocol.

[1956] Specific actions

[1957] The server encrypts the data and prepares it for transmission.

[1958] The encrypted data is sent to the terminal.

[1959] Input: Information to be provided to the user (recommended medications, precautions, etc.)

[1960] Output: Encrypted information sent to the user's device

[1961] Step 10:

[1962] The device displays the received information to the user, specifically, "Acetaminophen is recommended. Consult your doctor before using ibuprofen."

[1963] Specific actions

[1964] The terminal decrypts the received data.

[1965] Present the data in a format suitable for the user interface.

[1966] Input: Encrypted information

[1967] Output: Information displayed to the user

[1968] Step 11:

[1969] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[1970] Specific actions

[1971] The device generates a notification.

[1972] Notify the user in the form of a popup or message.

[1973] Input: Notification information to provide to users

[1974] Output: Warnings and links notified to the user

[1975] The above are the specific processing steps of the medicine management system of the present invention.

[1976] (Application example 1)

[1977] 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."

[1978] Currently, drug selection, interaction checks, and side effect risk analysis must be done individually, which is cumbersome for users. Even when an appropriate drug is presented, the purchasing process is often time-consuming and laborious. This makes it difficult to obtain over-the-counter drugs quickly and safely.

[1979] 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.

[1980] In this invention, the server includes an input means for inputting information about drugs being taken by the user and symptoms, a receiving means for receiving information transmitted from the input means, a suggestion means for analyzing the received information and suggesting appropriate over-the-counter drugs based on the symptoms, an interaction checking means for checking interactions between the drugs being taken and the suggested over-the-counter drugs, a side effect risk analysis means for analyzing the risk of side effects based on the user's personal information, an information generating means for summarizing the analysis results and providing them to the user, a transmitting means for transmitting the information generated by the information generating means to the user's terminal, a display means for displaying the information transmitted by the transmitting means on the user's terminal, and an electronic payment means for quickly and safely purchasing the suggested over-the-counter drugs. This enables a unified process from drug selection, interaction checking, and side effect risk analysis to the prompt purchase of appropriate over-the-counter drugs.

[1981] "Input means" refers to a device or interface that allows a user to input information such as information about medications being taken and symptoms.

[1982] The "receiving means" is a device or system that allows the server to receive the information transmitted from the input means.

[1983] The "suggestion means" is a device or system that analyzes the received information and suggests appropriate over-the-counter medications based on the user's symptoms.

[1984] An "interaction checking means" is a device or system for checking interactions between medications currently being taken and proposed over-the-counter medications and conducting risk assessments.

[1985] "Side effect risk analysis means" refers to a device or system for analyzing and evaluating side effect risks based on the user's personal information.

[1986] The "information generating means" is a device or system that generates information to be provided to users based on the analysis results.

[1987] The "transmission means" is a device or system for transmitting the information generated by the information generation means to the user's terminal.

[1988] "Display means" refers to a device or interface for displaying the transmitted information on the user's terminal.

[1989] "Electronic payment method" means an electronic payment method or system for the fast and secure purchase of the proposed over-the-counter drug.

[1990] This invention is a drug management system that utilizes generative AI, and can suggest appropriate over-the-counter drugs, check for drug interactions, and analyze the risk of side effects based on the drug information and symptoms entered by the user. It also has an electronic payment method for quickly and safely purchasing the suggested over-the-counter drugs.

[1991] Overall system configuration

[1992] The system consists of the following elements:

[1993] 1. Input method: An interface (e.g., smartphone app) through which users can input information about symptoms and medications they are taking.

[1994] 2. Receiving means: A server system (using a Flask application) that receives information sent from the input means.

[1995] 3. Recommendation method: A system that analyzes the received information and suggests over-the-counter medications that address the symptoms.

[1996] 4. Interaction Checking Measures: A system to check for interactions between medications currently being taken and proposed over-the-counter medications.

[1997] 5. Side effect risk analysis means: A system that analyzes the risk of side effects based on the user's personal information.

[1998] 6. Information generation means: A system that generates information to be provided to users based on the analysis results.

[1999] 7. Transmission means: A system that transmits the information generated by the information generation means to the user's terminal.

[2000] 8. Display means: An interface that displays the transmitted information on the user's terminal.

[2001] 9. Electronic Payment Methods: An electronic payment system for fast and secure purchase of proposed over-the-counter medications.

[2002] Data processing and analysis description

[2003] The server receives the drug information and symptoms sent by the user and analyzes that information using a generative AI model. Specifically, it retrieves appropriate over-the-counter drugs from a database based on the symptoms and checks for interactions between each drug and any other medications the user is currently taking. It also analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.).

[2004] The information generation means compiles these analysis results and generates information including recommended medicines and precautions for the user. The generated information is sent to the user's terminal via a secure communication protocol.

[2005] The electronic payment method provides a payment interface that includes multiple payment methods (credit cards, electronic money, QR code payment, etc.) so that users can instantly purchase the suggested over-the-counter medications.

[2006] Examples of concrete examples and prompts

[2007] As a concrete example, let's say a user wants to treat a "headache." In this case, the user launches the app and enters "headache," "current antihypertensive medication: Losartan," and "no allergies." The device sends this information to the server, which analyzes it and notifies the user that acetaminophen is recommended. The user can also quickly complete the purchase process within the app.

[2008] An example prompt has the following format:

[2009] "Please enter your symptoms and medications:"

[2010] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2011] Step 1:

[2012] The user launches the smartphone app and inputs symptoms, medication information, and allergy information. The user enters this information into the input form and presses the send button, temporarily saving the data on the device. The input data includes symptoms (e.g., headache), medications being taken (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies). The output is the input data temporarily saved on the device.

[2013] Step 2:

[2014] The terminal sends the information entered by the user to the server using a secure communication protocol (e.g., TLS / SSL). At this time, the terminal converts the temporarily saved data into JSON format and sends it as an HTTPS request. The input is the temporarily saved data, and the output is the data sent to the server.

[2015] Step 3:

[2016] The server receives the information sent from the device and stores it in a database. At the same time, it begins analyzing the received data. Specifically, it retrieves a list of over-the-counter medications that match the symptoms from the database and proceeds with the analysis. The input is the data sent from the device, and the output is the list of over-the-counter medications from the database used for analysis.

[2017] Step 4:

[2018] The server uses a generative AI model to suggest appropriate over-the-counter medications based on the received data (user symptoms, medications being taken, and allergy information). This generative AI model analyzes over-the-counter medication information corresponding to symptoms in the database and makes an appropriate selection. The input is symptoms and medication information, and the output is a list of suggested over-the-counter medications.

[2019] Step 5:

[2020] The server checks interactions between the proposed over-the-counter drugs and the medications the user is currently taking. To do this, it uses a pre-built interaction checking algorithm to evaluate the interaction risk between each over-the-counter drug and the medication the user is currently taking. The input is the list of proposed over-the-counter drugs and information about the medications the user is currently taking, and the output is the evaluation result of the interaction risk.

[2021] Step 6:

[2022] The server analyzes the risk of side effects based on the user's personal information (age, gender, medical history, etc.). It obtains information about side effects from the database and performs a risk assessment based on that information. The input is personal information and drug information, and the output is the assessment result of the side effect risk.

[2023] Step 7:

[2024] The server generates information to provide to users based on these analysis results. The generated information includes suggested over-the-counter medications and precautions. The information generation means uses a generative AI model to appropriately summarize the analysis results and convert them into a format optimized for user viewing. The input is the analysis results, and the output is the generated information.

[2025] Step 8:

[2026] The server sends the generated information to the user's device using a secure communication protocol. At this time, the generated information is sent in an appropriate format (e.g., JSON format). The input is the generated information, and the output is the sent information. TLS / SSL is used for transmission to ensure data security.

[2027] Step 9:

[2028] The device displays the received information to the user, specifically, the suggested over-the-counter drug information and warnings using an appropriate interface, and sends notifications as needed. The input is the received information, and the output is the displayed information and sent notifications.

[2029] Step 10:

[2030] The user then goes through the process of purchasing the over-the-counter medication suggested within the app. The payment is completed quickly and securely using electronic payment methods. Various payment methods (credit cards, electronic money, QR code payment, etc.) are integrated into the interface. The input is the intention to purchase and payment information, and the output is a notification that the purchase has been completed.

[2031] 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.

[2032] The present invention is a system that provides advice and drug suggestions according to the user's mental state by combining a drug management system that utilizes generative AI with an emotion engine that recognizes the user's emotions. This system is implemented as follows.

[2033] 1. Enter user information

[2034] 1.1 Application launch

[2035] The user launches the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[2036] 1.2 Entering symptoms and medication information

[2037] The user enters their symptoms (e.g., headache), medications (e.g., the antihypertensive drug losartan), and allergy information (e.g., no allergies) into a form on the home screen. They also provide additional information, including their emotional state, through voice input. The device temporarily stores the entered data and prepares it for transmission.

[2038] 1.3 Data Transmission

[2039] The terminal transmits the user's input information and emotion data to the server using a secure communication protocol.

[2040] 2. Data processing and analysis

[2041] 2.1 Data Reception

[2042] The server receives information sent from the device, including symptoms, medications being taken, allergy information, and emotional data.

[2043] 2.2 Symptom-based medication suggestions

[2044] The server analyzes the received information and suggests appropriate over-the-counter medications for the symptoms (e.g., headache). It retrieves information about the corresponding over-the-counter medications from a database and selects the best medication based on the symptoms.

[2045] 2.3 Emotion Analysis

[2046] The server analyzes the emotional data, including the voice input data, to recognize the user's mental state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[2047] 2.4 Drug interaction check

[2048] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[2049] 2.5 Analysis of side effect risks

[2050] The server analyzes the risk of side effects for recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), obtains information on the risk of side effects from the database, and performs an evaluation.

[2051] 2.6 Adjusting Suggestions Based on Emotions

[2052] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if the patient is experiencing high stress, it will provide medications and advice that have a calming effect.

[2053] 3. Returning the results

[2054] 3.1 Generating Results

[2055] The server compiles the analysis results and generates information including appropriate medication suggestions and precautions. It selects "acetaminophen" as the recommended medication for the symptoms, and notes that "ibuprofen requires caution" due to the risk of interactions with antihypertensive drugs. It also includes advice based on the patient's emotional state.

[2056] 3.2 Data Transmission

[2057] The server sends the generated information to the user's terminal using a secure communication protocol.

[2058] 4. Display and notification of results

[2059] 4.1 Displaying the results

[2060] The device displays the received information to the user, such as "Acetaminophen is recommended. Consult your doctor before using ibuprofen" and "Based on your current stress level, please try to relax."

[2061] 4.2 Caution Notice

[2062] If necessary, the device will use a notification function to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[2063] Specific examples

[2064] User example: Hanako

[2065] 1. Hanako launches the app and wants to treat her headache.

[2066] 2. Hanako enters "headache," "hypertensive medication currently being taken: Losartan," and "no allergies" into the form on the home screen. She also uses voice input to say, "I've been feeling stressed lately."

[2067] 3. The device sends this information to the server.

[2068] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[2069] 5. The server checks for interactions with losartan and determines that acetaminophen is a safe option.

[2070] 6. The server analyzes the emotional data and recognizes that Hanako is under a lot of stress.

[2071] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[2072] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[2073] 9. The device will display this information to Mary and send her notifications if necessary.

[2074] This allows Hanako to choose and take the appropriate medication with peace of mind, and also receive advice tailored to her own mental state.

[2075] The processing flow will be explained below.

[2076] Step 1:

[2077] When a user launches the application, they are presented with a home screen, which includes a form where they can enter information about their symptoms, medications, and allergies. Additionally, a voice input button is provided to visualize their emotional state.

[2078] Step 2:

[2079] The user inputs symptoms into the form. For example, the user inputs "headache." The user also selects "Losartan, an antihypertensive drug" as the medication currently being taken, and selects "No allergies" as the allergy information.

[2080] Step 3:

[2081] The user presses the voice input button and provides an emotional state through voice input, for example, saying, "I've been feeling stressed lately."

[2082] Step 4:

[2083] The device temporarily stores the entered data (symptoms, medications, allergy information, and voice data) and prepares it for transmission.

[2084] Step 5:

[2085] The device sends the user's input information and voice data to the server, using a secure communication protocol to keep the data safe.

[2086] Step 6:

[2087] The server receives information from the device, including symptoms, medications being taken, allergy information, and emotional data.

[2088] Step 7:

[2089] The server consults a database to retrieve a list of over-the-counter medications that address the user's symptoms, such as acetaminophen and ibuprofen for headaches.

[2090] Step 8:

[2091] The server checks interactions between the medication being taken (losartan) and potential over-the-counter drugs (acetaminophen, ibuprofen), and evaluates the risk using an interaction risk assessment algorithm.

[2092] Step 9:

[2093] Based on the interaction evaluation results, the server determines that caution is required because ibuprofen is likely to interact with losartan, and selects acetaminophen as a recommended candidate.

[2094] Step 10:

[2095] The server analyzes the voice input data and recognizes the user's emotional state (e.g., stress, anxiety, calm). This is done by the server's emotion engine.

[2096] Step 11:

[2097] The server analyzes the risk of side effects for the recommended over-the-counter drugs based on the user's personal information (e.g., age, medical history, etc.), including information on side effects from a database.

[2098] Step 12:

[2099] The server makes the final decision that acetaminophen is appropriate based on the results of the side effect risk analysis.

[2100] Step 13:

[2101] The server then adjusts the selection of over-the-counter medications and advice based on the analysis results of the emotion engine. For example, if stress levels are high, it will provide medications and advice with a calming effect.

[2102] Step 14:

[2103] The server compiles the analysis results and generates information including appropriate medication suggestions, precautions, and emotion-based advice.

[2104] Step 15:

[2105] The server sends the generated information to the user's terminal using a secure communication protocol.

[2106] Step 16:

[2107] The device displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[2108] Step 17:

[2109] The device will use notifications as needed to provide additional attention to the user, such as providing links to purchase recommended medications or other important information.

[2110] Example 2

[2111] 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."

[2112] Conventional drug management systems suggest appropriate over-the-counter medications based on the user's current medication information and symptoms, but they are unable to provide advice that takes into account the user's emotional state. As a result, it is difficult for users to receive appropriate advice when they are stressed or mentally unstable. In addition, there is a need to improve safety due to insufficient analysis of drug interactions and side effect risks.

[2113] 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.

[2114] In this invention, the server includes input means for inputting information about medications the user is taking, symptoms, and emotional state, receiving means for receiving information transmitted from the input means, suggestion means for analyzing the received information and suggesting appropriate medications based on the symptoms, interaction checking means for checking interactions between the medications the user is taking and the suggested medications, side effect risk analysis means for analyzing the risk of side effects based on the user's personal information and emotional state, information generation means for summarizing the analysis results and providing them to the user, transmission means for transmitting the information generated by the information generation means to the user's terminal, and display means for displaying the information transmitted by the transmission means on the user's terminal. This enables accurate advice and medication suggestions based on the user's emotional state, thereby improving safety and psychological support.

[2115] "User" refers to a person who uses the system to input their symptoms, information about the medications they are taking, and their emotional state, and receives appropriate medication suggestions and advice.

[2116] "Drug information" refers to detailed information such as the name, dosage, and usage instructions of the medications the user is currently taking.

[2117] "Symptom" means information that describes a physical or mental illness or condition that a user is currently experiencing.

[2118] "Emotional state" refers to information that indicates a user's current state of mind or emotion, such as stress, anxiety, or calmness.

[2119] "Input means" refers to the interface through which users input their medication information, symptoms, and emotional state into the system. Specifically, this includes form entry and voice input functions.

[2120] The "receiving means" refers to a function that allows the system to acquire information sent from the input means.

[2121] The "suggestion means" refers to a function for analyzing the information acquired by the receiving means and suggesting an appropriate medication based on the symptoms of the user.

[2122] "Interaction checking means" refers to a function for analyzing interactions between the medications the user is taking and the proposed medication and assessing the risks.

[2123] "Side effect risk analysis means" refers to a function for assessing the side effect risk of a proposed drug based on the user's personal information and emotional state.

[2124] "Information generation means" refers to the function by which the system compiles the analysis results and generates information to be provided to users.

[2125] The "transmission means" refers to a function for transmitting the information generated by the information generation means to the user's terminal.

[2126] The "display means" refers to a function for displaying the information transmitted by the transmission means on the user terminal.

[2127] "Database" refers to an information management system that stores information on drugs, interactions, risk of side effects, etc., and makes it accessible to the system as needed.

[2128] "HTTPS" is a protocol for securely sending and receiving data over the Internet, and refers to technology that ensures data protection.

[2129] MODE FOR CARRYING OUT THE INVENTION

[2130] This invention is a system that provides advice and drug suggestions based on the user's mental state by combining a drug management system that utilizes a generative AI model with an emotion engine that recognizes the user's emotions. This system is implemented as an application that can be accessed from a smartphone or computer.

[2131] 1. Enter user information

[2132] 1.1 Application launch

[2133] Users launch the application on their smartphone or computer. The home screen displays a form for entering information about symptoms, medications, and allergies. Voice input is also available, allowing the emotion engine to analyze the user's emotions.

[2134] 1.2 Entering symptoms and medication information

[2135] The user enters their symptoms (e.g., headache), medications they are taking (e.g., antihypertensives), and allergy information (e.g., no allergies) into the form. They also use voice input to provide emotional status information, such as "I've been feeling stressed lately." The device temporarily stores the entered data and prepares it for transmission.

[2136] 1.3 Data Transmission

[2137] The device transmits the information and emotion data entered by the user to the server using secure communication technology such as the HTTPS protocol.

[2138] 2. Data processing and analysis

[2139] 2.1 Data Reception

[2140] The server receives the information sent from the terminal, including the user's symptoms, medications being taken, allergy information, and emotional data.

[2141] 2.2 Symptom-based medication suggestions

[2142] The server analyzes the received information and suggests appropriate medications for the user's symptoms. For example, it retrieves information on medications for "headache" (e.g., acetaminophen, ibuprofen) from a database and selects the most appropriate medication.

[2143] 2.3 Emotion Analysis

[2144] The server uses an emotion engine to analyze emotion data, including voice input data, and recognize the user's mental state (e.g., stress, anxiety, calm).

[2145] 2.4 Drug interaction check

[2146] The server checks for interactions between current medications (e.g., antihypertensives) and proposed over-the-counter medications (e.g., acetaminophen, ibuprofen), and assesses the risk using a risk assessment algorithm.

[2147] 2.5 Analysis of side effect risks

[2148] The server analyzes the risk of side effects based on the user's personal information (e.g., age, medical history, etc.), obtains information about the risk of side effects from the database, and makes an assessment.

[2149] 2.6 Adjusting Suggestions Based on Emotions

[2150] The server then selects over-the-counter medications and adjusts the advice based on the analysis results of the emotion engine. For example, if stress levels are high, the server will provide advice on medications with a calming effect or to promote relaxation.

[2151] 3. Returning the results

[2152] 3.1 Generating Results

[2153] The server compiles the analysis results and generates information including appropriate medication suggestions and cautions. For example, it may select "acetaminophen" as the recommended medication for the symptoms, and state, "Consult a doctor before using ibuprofen" due to the risk of interactions with antihypertensive drugs. It may also include advice based on the user's emotional state.

[2154] 3.2 Data Transmission

[2155] The server sends the generated information to the user's device using secure communication technology such as the HTTPS protocol.

[2156] 4. Display and notification of results

[2157] 4.1 Displaying the results

[2158] The device then displays the received information to the user, for example, "Acetaminophen is recommended. Consult your doctor before using ibuprofen. Also, based on your current stress level, try to relax."

[2159] 4.2 Caution Notice

[2160] If necessary, the device will use notifications to provide additional user attention, such as providing links to purchase recommended medications or other important information.

[2161] Specific examples

[2162] User example: A user

[2163] 1. A user launches the app and wants to treat a headache.

[2164] 2. The user enters "headache," "antihypertensive medication currently being taken," and "no allergies" into the form on the home screen. They also use voice input to say, "I've been feeling stressed lately."

[2165] 3. The device sends this information to the server.

[2166] 4. The server retrieves a list of over-the-counter headache medications (e.g., acetaminophen, ibuprofen) from the database.

[2167] 5. The server checks for interactions with antihypertensive medications and determines that acetaminophen is a safe option.

[2168] 6. The server analyzes the emotional data and recognizes that the user is under high stress.

[2169] 7. The server analyzes the risk of side effects and confirms that there are no particular problems.

[2170] 8. The server summarizes the analysis results and generates the message, "Acetaminophen is recommended. If you are using ibuprofen, consult your doctor. Also, based on your current stress level, try to relax." and sends this to the device.

[2171] 9. The device displays this information to the user and also sends notifications if necessary.

[2172] This example allows users to choose and take appropriate medication with peace of mind, and also receive advice tailored to their own mental state.

[2173] Example prompts for generative AI models

[2174] Input prompt: "Please enter your headache symptoms and tell me about over-the-counter medications that can be taken with your current blood pressure medication. If you've been under a lot of stress lately, what medication would be appropriate?"

[2175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2176] Step 1:

[2177] The user launches the application on their smartphone or computer. The home screen displays a form for entering symptoms, medication information, and allergy information. The user enters their symptoms (e.g., headache), medications (e.g., antihypertensive drugs), and allergy information (e.g., no allergies) into the form, and provides their emotional state, such as "I've been feeling stressed lately," via voice input. The entered data is temporarily stored on the device.

[2178] Input: Use...

Claims

1. An input means for a user to input information about medications being taken and symptoms; a receiving means for receiving information transmitted from the input means; a suggestion means for analyzing the received information and suggesting an appropriate over-the-counter medication based on the symptoms; an interaction checker to check for interactions between medications currently being taken and proposed over-the-counter medications; A side effect risk analysis means for analyzing the side effect risk based on the user's personal information; an information generating means for summarizing the analysis results and providing them to a user; a transmitting means for transmitting the information generated by the information generating means to a user terminal; A system including a display means for displaying the information transmitted by the transmission means on a user terminal.

2. 2. The system according to claim 1, wherein said input means also allows the user to input allergy information.

3. 2. The system according to claim 1, wherein said interaction checking means also checks interactions with a plurality of drugs and foods.

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