System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting basic information of users and transmitting the information to a server; means for inputting medicine information of a medicine company and transmitting the information to the server; means for acquiring the basic information of the users and the medicine information and transmitting the information to a AI; means for performing evaluation based on the basic information of the users and the medicine information and returning the evaluation to the AI; and means for providing evaluation results from the AI to the users.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] The purpose of this invention is to resolve concerns about the effectiveness and safety of users who regularly take over-the-counter medicines, vitamins, herbal medicines, etc. when combining these medicines with medicines prescribed by a hospital. Conventional methods such as inquiring at pharmacies and existing applications are time-consuming and labor-intensive, and have limited versatility, so there is a need for a solution that is easier and more reliable for users. [Means for solving the problem]

[0005] The present invention solves the problem by using the following means. A system is provided that includes: a means for inputting basic user information and sending it to a server; a means for a pharmaceutical company to input drug information and send it to the server; a means for the user to input information about drugs they regularly take and send it to the server; a means for the server to acquire the user's basic information and drug information and pass it on to an AI; a means for the AI ​​to perform an evaluation based on the user's basic information and drug information and return it to the server; and a means for the server to provide the AI's evaluation results to the user. Furthermore, convenience is enhanced by using a mobile device as the means for inputting user and drug information, and the AI ​​evaluation means can suggest optimal drugs and supplements for specific symptoms, effectively resolving user concerns.

[0006] "Basic information of the user" refers to information about personal characteristics and health status, such as gender, age, height, weight, and symptoms.

[0007] A "server" is a computer system that stores, processes, and manages data from multiple devices and can exchange data with other systems as needed.

[0008] "Drug information" refers to detailed data about drugs, such as their names, ingredients, effects, and side effects.

[0009] "AI" is an abbreviation for artificial intelligence, a software that automatically makes decisions and predictions based on large amounts of data.

[0010] "Evaluation" refers to the AI ​​determining whether or not medications can be taken together and what the optimal combination is based on the user's basic information and medication information.

[0011] A "mobile device" is a portable electronic device such as a smartphone or tablet.

[0012] "Feedback" refers to the evaluation results and suggestions provided to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0021] [First embodiment]

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

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

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

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

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

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

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

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

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

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

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0034] This invention is a system that uses AI to evaluate interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations to help users take their medication safely and effectively.

[0035] System Overview

[0036] This system collects basic information about the user and medication information, and the AI ​​analyzes that data to provide feedback on whether medications can be taken together and what the optimal combination is. To achieve this, the system includes the following components:

[0037] 1. Server

[0038] It stores and manages user and drug information and works in conjunction with AI.

[0039] 2. Terminal

[0040] A device used to send basic information and medication information entered by users to a server, including smartphones and PCs.

[0041] 3. Generation AI

[0042] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[0043] 4. Pharmaceutical company data entry system

[0044] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[0045] Program processing flow

[0046] Program processing overview

[0047] This program performs processing in the following steps:

[0048] 1. User Registration

[0049] 2. Drug information registration

[0050] 3. User medication information input

[0051] 4. AI evaluation

[0052] 5. Providing Feedback

[0053] Natural language explanation of program processing

[0054] 1. User Registration

[0055] Using the terminal, the user inputs their basic information (gender, age, height, weight, symptoms, etc.), and the terminal then transmits the input information to the server.

[0056] The server stores the received information in a database.

[0057] 2. Drug information registration

[0058] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[0059] The server stores the received drug information in a database.

[0060] 3. User medication information input

[0061] Using the terminal, users enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily, and the terminal then sends the entered information to the server.

[0062] The server stores the received drug information in a database.

[0063] 4. AI evaluation

[0064] The server obtains the user's basic information and medication information and passes this data to the AI.

[0065] The generative AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medications or supplements for specific symptoms.

[0066] The server receives the evaluation results from the AI ​​and stores them in a database.

[0067] 5. Providing Feedback

[0068] The server generates feedback to provide the evaluation results to the user.

[0069] Using the terminal, the user checks the feedback.

[0070] Specific examples

[0071] scenario

[0072] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication from a hospital, the system operates as follows:

[0073] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[0074] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[0075] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[0076] 4. Evaluation by AI: The server passes the user's basic information and medication information to the AI, which analyzes them and makes an evaluation.

[0077] 5. Providing feedback: The server provides the user with the evaluation results from the AI, and the user can check the feedback to take the medicine with peace of mind.

[0078] This system allows users to easily and quickly obtain information on safe drug interactions, eliminating the need to research on their own.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[0082] Step 2:

[0083] The server stores the received user information in a database.

[0084] Step 3:

[0085] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[0086] Step 4:

[0087] The server stores the received medication information in a database.

[0088] Step 5:

[0089] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[0090] Step 6:

[0091] The server stores the received medication information in a database.

[0092] Step 7:

[0093] The server obtains the user's basic information and medication information and passes it to the generation AI.

[0094] Step 8:

[0095] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[0096] Step 9:

[0097] The generation AI returns the evaluation results to the server.

[0098] Step 10:

[0099] The server receives the evaluation results from the AI ​​and stores them in a database.

[0100] Step 11:

[0101] The server generates feedback to provide the evaluation results to the user.

[0102] Step 12:

[0103] Users can use the device to check feedback and use it as a reference for taking medication safely.

[0104] Example 1

[0105] 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."

[0106] In today's world, it is common for people to take multiple medications and supplements at the same time, but it is difficult to predict the side effects and diminished effectiveness of these medications. It is also difficult for users to determine for themselves whether the combination of medications prescribed by a medical institution, over-the-counter medications, and supplements is appropriate. Conventional methods cannot make such judgments quickly and accurately, potentially hindering the safe and effective use of medications.

[0107] 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.

[0108] In this invention, the server includes means for inputting basic information about the user and transmitting it to the server, means for the pharmaceutical company to input drug information and transmit it to the server, means for inputting information about drugs the user takes on a daily basis and transmitting it to the server, means for passing data to the generated AI, means for the generated AI to return evaluation results, and means for providing the evaluation results to the user. This allows the user to easily and quickly obtain information about safe drug interactions without having to do the effort of researching them themselves.

[0109] "Basic information of the user" refers to basic information about the user, such as gender, age, height, weight, symptoms, etc.

[0110] "Drug information" refers to detailed information about drugs, such as drug name, ingredients, efficacy, and side effects.

[0111] "Server" refers to a computer system that receives and stores input data, passes the data to the generated artificial intelligence as needed, and returns the evaluation results.

[0112] "Generated artificial intelligence" refers to an algorithm or system that analyzes the user's basic information and drug information provided by the server, and evaluates whether or not drugs can be taken together and what the optimal combination is.

[0113] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[0114] "Evaluation results" refer to the results of the analysis conducted by the generated artificial intelligence, such as whether or not a drug can be taken with other drugs, and suggestions for the most appropriate medications and supplements.

[0115] "Feedback" refers to information that is presented to the user as evaluation results and helps the user understand the content.

[0116] "Daily medication information" refers to information on over-the-counter medications, vitamins, herbal medicines, and medications prescribed by hospitals that users take daily.

[0117] This invention is a system that uses generated artificial intelligence to evaluate the interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations so that users can take their medicines safely and effectively.

[0118] System configuration

[0119] The system includes the following components:

[0120] 1. Server

[0121] The server stores and manages user information and drug information, and interacts with the generated artificial intelligence.

[0122] 2. Terminal

[0123] The terminal is a device used to send basic information and medication information entered by the user to the server, and includes smartphones, PCs, etc.

[0124] 3. Generated Artificial Intelligence

[0125] The generated artificial intelligence analyzes data based on the user's basic information and medication information, and evaluates the optimal drug combination.

[0126] 4. Pharmaceutical company data entry system

[0127] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[0128] Program processing overview

[0129] The program follows these steps:

[0130] 1. User Registration

[0131] The user uses the terminal to input their basic information (gender, age, height, weight, symptoms, etc.) and transmits the information to the server.

[0132] The server stores the received information in a database.

[0133] 2. Drug information registration

[0134] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[0135] The server stores the received drug information in a database.

[0136] 3. User medication information input

[0137] The user uses a terminal to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines that he or she takes on a daily basis, and sends the information to the server.

[0138] The server stores the received drug information in a database.

[0139] 4. Evaluation by generated artificial intelligence

[0140] The server obtains the user's basic information and medication information and passes this data to the generated artificial intelligence.

[0141] The generated AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medicines or supplements for specific symptoms.

[0142] The server receives the evaluation results from the generated artificial intelligence and stores them in a database.

[0143] 5. Providing Feedback

[0144] The server generates feedback to provide the evaluation results to the user.

[0145] The user uses the terminal to check the feedback.

[0146] Specific examples

[0147] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication, the system works as follows:

[0148] 1. User Registration:

[0149] The user enters basic information (gender: female, age: 45, weight: 60 kg, symptoms: chronic headache) and sends it to the server.

[0150] 2. Drug Information Registration:

[0151] A pharmaceutical company registers information about a newly developed drug (drug name: PainEase, ingredients: ibuprofen, efficacy: pain relief, side effect: stomach discomfort) on a server.

[0152] 3. User medication information input:

[0153] The user enters information about the vitamins they regularly take (vitamin D), over-the-counter medications (allergy medication "AllergyFix"), and any new medications they have been prescribed by their doctor ("PainEase").

[0154] 4. Evaluation by generated artificial intelligence:

[0155] The server passes the user's basic information and medication information to the generated AI, which then analyzes and evaluates them.

[0156] For example, evaluation results show that Vitamin D and AllergyFix are safe, but combining them with PainEase may increase stomach discomfort.

[0157] 5. Providing Feedback:

[0158] The server provides the evaluation results to the user, who then checks the feedback in a dedicated app to understand the timing and precautions for taking painkillers.

[0159] Prompt Sentence Examples

[0160] "Based on the following drug information, please evaluate the safe interactions between over-the-counter medications and medications prescribed by your doctor.

[0161] Basic information: Gender: Female Age: 45 Weight: 60kg

[0162] Symptoms: Chronic headache

[0163] Over-the-counter medications: Vitamin D supplements, allergy medications

[0164] Prescription Drugs: Newly Prescribed Pain Reliever Medications"

[0165] By inputting this prompt sentence into the generated artificial intelligence, a specific drug combination evaluation can be performed.

[0166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0167] Step 1:

[0168] User registration

[0169] The user inputs basic information such as gender, age, height, weight, symptoms, etc. using a terminal. For example, the user inputs the required information into a form on a dedicated application or website.

[0170] The terminal encodes the input information and transmits it to the server using a secure communication protocol (for example, HTTPS).

[0171] The server stores the received information in a database. Specifically, the server analyzes the received data and stores it in the database as a new record.

[0172] Input: Basic information such as gender, age, height, weight, symptoms, etc.

[0173] Output: A record of basic information stored in the database

[0174] Step 2:

[0175] Drug Information Registration

[0176] Pharmaceutical companies use the management screen to enter information about newly developed drugs (drug name, ingredients, efficacy, side effects, etc.) and send it to the server. For example, a pharmaceutical company employee enters detailed drug information on the management screen and presses the send button.

[0177] The server stores the received drug information in a database. Specifically, the server analyzes the received data and adds the information to the corresponding table in the database.

[0178] Input: Drug information such as drug name, ingredients, efficacy, side effects, etc.

[0179] Output: Medication records stored in a database

[0180] Step 3:

[0181] User drug information input

[0182] Users use their devices to enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily. For example, users can fill out a form on a dedicated application or website to enter this information.

[0183] The terminal encodes the input information and transmits it to the server.

[0184] The server stores the received drug information in a database. For example, the server analyzes the received data and stores it in a drug information table in the database.

[0185] Input: Information on over-the-counter medicines, vitamins, herbal medicines, and prescription medicines

[0186] Output: User's medication record stored in database

[0187] Step 4:

[0188] Evaluation by generated artificial intelligence

[0189] The server obtains the user's basic information and medication information and passes that data to the generated AI. Specifically, the server queries the relevant basic information and medication information from the database and sends a request to the generated AI's API.

[0190] The generated AI analyzes the data based on the received information and suggests drug interactions and the best medicines or supplements for specific symptoms. For example, the generated AI model analyzes the request and makes an evaluation using its internal algorithm.

[0191] The server receives the evaluation results from the generated artificial intelligence and stores them in a database. Specifically, the server receives the evaluation results returned from the generated artificial intelligence and stores them in a database in an appropriate format.

[0192] Input: Basic information and medication information

[0193] Output: Evaluation results generated by AI

[0194] Step 5:

[0195] Providing Feedback

[0196] The server generates feedback to provide the evaluation results to the user. Specifically, the server organizes the evaluation results and converts them into a report format that is easy for the user to understand.

[0197] The user uses the device to review the feedback, for example, the user opens an application or website and reviews the feedback provided.

[0198] Input: Evaluation results generated by AI

[0199] Output: Feedback provided to the user

[0200] (Application example 1)

[0201] 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."

[0202] When users purchase medicine, it is difficult to confirm whether it is safe to take it with other medicines or supplements they are already taking. In addition, there is no easy way for users considering purchasing medicine to understand interactions, making it difficult to select a safe and effective medicine.

[0203] 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.

[0204] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for inputting information on drugs the user is considering purchasing and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the generative AI model, means for the generative AI model to evaluate the user's basic information and drug information based on the user's basic information and drug information and return it to the server, and means for the server to provide the evaluation result from the generative AI model to the user. This allows the user to check in real time the interactions between the drug they are considering purchasing and drugs they are already taking and safely select a drug.

[0205] "Basic information" refers to personal information such as the user's age, gender, height, weight, medical history, etc.

[0206] "Drug information" refers to detailed data about drugs, such as drug name, ingredients, efficacy, and side effects.

[0207] A "generative AI model" is an artificial intelligence model that analyzes and evaluates based on the user's basic information and drug information.

[0208] "Mobile device" refers to a computing device that can be carried around and used, such as a smartphone or tablet.

[0209] "Barcode scanning" refers to the act or means of reading the barcode attached to a product using the camera function of a device, etc.

[0210] "Server" refers to a computer system that processes, stores, and provides information over a network.

[0211] "Feedback" refers to information and advice provided to users based on the evaluation results of the generative AI model.

[0212] "Pharmaceutical company" refers to a legal entity that manufactures and sells drugs.

[0213] "Drug interactions" refers to the interactions and safety of taking multiple medications or supplements at the same time.

[0214] This invention relates to a system for virtual stores that allows users to check in real time the interactions between drugs and supplements they are considering purchasing. This system uses the user's basic information and drug information to perform an evaluation using a generative AI model, and provides the evaluation results as feedback.

[0215] System Components

[0216] 1. Server

[0217] The server stores and manages basic user information and medication information, and links it with the generative AI model. SQL Server or similar can be used as the database.

[0218] 2. Mobile devices

[0219] A device that allows users to input and send their basic information and medication information. This applies to mobile devices such as smartphones and tablets.

[0220] 3. Generative AI Models

[0221] This is an artificial intelligence model that analyzes data based on the user's basic information and medication information, and evaluates whether or not medications can be taken together and suggests the most appropriate medication. The generative AI model runs on a server.

[0222] Specific operation of the system

[0223] The system works as follows:

[0224] 1. User Registration

[0225] A user uses a mobile device to input their basic information (age, sex, height, weight, medical history, etc.) and sends it to the server, which then stores the received information in a database.

[0226] 2. Drug information registration

[0227] Pharmaceutical companies register drug information (drug name, ingredients, efficacy, side effects, etc.) through the management screen and send it to the server. The server stores the received drug information in a database.

[0228] 3. User medication information input

[0229] Users enter information about the medications they take regularly or are considering purchasing into their mobile device by scanning barcodes or manually entering it, and then send it to the server, which receives it and stores it in a database.

[0230] 4. Evaluation using generative AI models

[0231] The server acquires the user's basic information and medication information and passes it to the generative AI model. The generative AI model analyzes this information and evaluates whether the medications are compatible and suggests the most appropriate medications. The evaluation results are returned to the server and stored in a database.

[0232] 5. Providing real-time feedback

[0233] The server generates feedback using the evaluation results from the generative AI model and sends it to the mobile device, where the user can check the content of the feedback on the screen.

[0234] Specific examples

[0235] For example, if a user tries to purchase a particular supplement from a virtual store, the system works as follows:

[0236] 1. User registration: A 40-year-old man installs the app and enters his basic information.

[0237] 2. Drug information registration: Information on newly developed drugs by pharmaceutical companies is registered on the server.

[0238] 3. User medication information entry: The user uses a mobile device to scan the barcode of the medication they are considering purchasing.

[0239] 4. Evaluation by generative AI model: The server obtains the user's basic information and drug information and passes it to the generative AI model, which analyzes the data and performs an evaluation.

[0240] 5. Providing real-time feedback: The server generates feedback based on the evaluation results of the generative AI model and sends it to the user's mobile device. The user confirms the feedback, such as "There is no problem with this combination. It is safe to take."

[0241] Prompt Sentence Examples

[0242] When users use the app, they are prompted with:

[0243] "Check whether your regular medications interact with the medication you are considering purchasing. Scan the barcode or enter the name of the medication."

[0244] Prompt sentence to input to the generative AI model:

[0245] User basic information: Age 40, Gender Male, Weight 70kg, Medical history Hypertension

[0246] Drugs under consideration for purchase: Loxoprofen, current medications: Vitamin C, prescription drug Losartan

[0247] Please rate this combination and what works best for you."

[0248] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0249] Step 1:

[0250] User Registration

[0251] Input: The user uses a mobile device to input basic information such as gender, age, height, weight, and medical history.

[0252] Data processing: The terminal converts the input information into an appropriate format and sends it to the server, for example, packaging the data in JSON format.

[0253] Output: The server receives the information and stores it in a database, which is used as input data for a generative AI model in a later step.

[0254] Step 2:

[0255] Drug Information Registration

[0256] Input: The pharmaceutical company inputs drug information such as the drug name, ingredients, efficacy, and side effects through the management screen.

[0257] Data processing: The drug company's terminal sends the entered drug information to the server. The data is packaged with the drug's identifier.

[0258] Output: The server stores the received drug information in a database. The stored information is used by the generative AI model for evaluation.

[0259] Step 3:

[0260] User drug information input

[0261] Input: The user inputs information about medications they take regularly or are considering purchasing into their mobile device, for example by scanning the medication barcode or manually entering the medication name.

[0262] Data processing: The terminal sends the entered medication information to the server. At this time, the data is saved in association with the user ID.

[0263] Output: The server receives the information and stores it in a database, which is used as input data for the generative AI model to evaluate.

[0264] Step 4:

[0265] Evaluation by generative AI model

[0266] Input: The server retrieves the user's basic information and medication information from the database. The retrieved information includes the user's basic information (age, gender, medical history, etc.), existing medication information, and newly entered medication information.

[0267] Data processing and calculation: The server passes the acquired data to the generative AI model. The generative AI model analyzes the data and determines whether or not drugs can be taken together and suggests the most appropriate medication. For example, it evaluates safety by taking into account the ingredients of the drug and the user's health condition.

[0268] Output: The generative AI model creates an evaluation result and returns it to the server. The evaluation result includes specific advice and points to note.

[0269] Step 5:

[0270] Providing real-time feedback

[0271] Input: The server receives the evaluation results returned by the generative AI model.

[0272] Data processing: The server converts the evaluation results into feedback data to be provided to the user, for example, in a format that is easy for the user to understand.

[0273] Output: The feedback data is sent to the mobile device and displayed to the user. The user can check the feedback on the mobile device screen and safely select a medication.

[0274] Specific examples

[0275] If the user is a 40-year-old man and the drug he is considering purchasing is loxoprofen:

[0276] 1. Step 1: User enters basic information, which is sent to the server and stored in the database.

[0277] 2. Step 2: The pharmaceutical company registers the information about rofecoxib, sends it to the server, and stores it in a database.

[0278] 3. Step 3: The user scans the barcode of the loxoprofen, enters the information, and sends it to the server.

[0279] 4. Step 4: The server passes basic information and drug information to the generative AI model, which evaluates drug interactions.

[0280] 5. Step 5: The server converts the evaluation results into feedback and provides it to the user. The user confirms, "There is no problem with this combination. It is safe to take."

[0281] 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.

[0282] This invention is a system that combines AI and an emotion engine to evaluate drug interactions and make optimal suggestions based on the user's emotional state, so that users can take their medication safely and effectively.

[0283] System Overview

[0284] This system acquires basic information about the user and medication information, and the AI ​​analyzes this data. It also uses an emotion engine to recognize the user's emotions and adjusts the evaluation results and recommendations according to their emotional state. This allows it to provide optimal drug combinations and recommendations that take the user's emotions into account.

[0285] System Components

[0286] 1. Server

[0287] It stores and manages user and drug information, and works in conjunction with AI and emotion engines.

[0288] 2. Terminal

[0289] A device that allows users to enter basic information and medication information, including smartphones and computers.

[0290] 3. Generation AI

[0291] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[0292] 4. Emotion Engine

[0293] The system identifies the user's emotions based on facial recognition and voice data, and adjusts the evaluation results and suggestions accordingly.

[0294] 5. Pharmaceutical company data entry system

[0295] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[0296] Program processing flow

[0297] Program processing overview

[0298] This program performs processing in the following steps:

[0299] 1. User Registration

[0300] 2. Drug information registration

[0301] 3. User medication information input

[0302] 4. AI evaluation

[0303] 5. Emotion Recognition by Emotion Engine

[0304] 6. Adjusting the proposal

[0305] 7. Providing Feedback

[0306] Natural language explanation of program processing

[0307] 1. User Registration

[0308] Using the terminal, the user enters their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[0309] The server stores the received information in a database.

[0310] 2. Drug information registration

[0311] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[0312] The server stores the received drug information in a database.

[0313] 3. User medication information input

[0314] Using the device, users enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take daily, and the device then sends this information to a server.

[0315] The server stores the received drug information in a database.

[0316] 4. AI evaluation

[0317] The server obtains the user's basic information and medication information and passes this data to the generation AI.

[0318] The generative AI analyzes the data based on the information it receives, and suggests whether or not drugs can be taken together, as well as the best medications and supplements for specific symptoms.

[0319] The server receives the evaluation results from the AI ​​and stores them in a database.

[0320] 5. Emotion Recognition by Emotion Engine

[0321] The device collects the user's facial recognition data and voice data and sends it to the server.

[0322] The emotion engine uses this data to identify the user's emotions.

[0323] 6. Adjusting the proposal

[0324] The server adjusts the evaluation results and suggestions based on the emotional state from the emotion engine.

[0325] For example, if a user is feeling stressed, the system will suggest medications or supplements that have a relaxing effect.

[0326] 7. Providing Feedback

[0327] The server generates feedback to provide the adjusted evaluation results to the user.

[0328] Using the device, users can view feedback and use it as a reference for taking medication safely.

[0329] Specific examples

[0330] scenario

[0331] For example, if a user regularly takes over-the-counter vitamins and also takes a new prescription drug from a hospital, the system operates as follows.

[0332] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[0333] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[0334] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[0335] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which analyzes them and makes an evaluation.

[0336] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[0337] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[0338] 7. Providing feedback: The server provides the adjusted evaluation results to the user, and the user can check the feedback to take the medicine with peace of mind.

[0339] This system not only saves users the trouble of researching on their own and allows them to easily and quickly obtain information on safe drug combinations, but also provides optimal suggestions based on their emotional state.

[0340] The processing flow will be explained below.

[0341] Step 1:

[0342] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[0343] Step 2:

[0344] The server stores the received user information in a database.

[0345] Step 3:

[0346] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[0347] Step 4:

[0348] The server stores the received medication information in a database.

[0349] Step 5:

[0350] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[0351] Step 6:

[0352] The server stores the received medication information in a database.

[0353] Step 7:

[0354] The server obtains the user's basic information and medication information and passes it to the generation AI.

[0355] Step 8:

[0356] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[0357] Step 9:

[0358] The generation AI returns the evaluation results to the server.

[0359] Step 10:

[0360] The server receives the evaluation results from the AI ​​and stores them in a database.

[0361] Step 11:

[0362] The device collects the user's facial recognition data and voice data and sends it to the server.

[0363] Step 12:

[0364] The server passes facial recognition data and voice data to the emotion engine.

[0365] Step 13:

[0366] The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[0367] Step 14:

[0368] The server receives the emotion recognition results from the emotion engine and adjusts the evaluation results and suggestions. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[0369] Step 15:

[0370] The server generates feedback to provide the adjusted evaluation results to the user.

[0371] Step 16:

[0372] Users can use the device to check feedback and use it as a reference for taking medication safely.

[0373] Example 2

[0374] 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."

[0375] Conventional medication systems have made it difficult for users to obtain appropriate information for taking multiple medications safely and effectively. Furthermore, they do not take into account the user's emotional state, resulting in a poor user experience. The present invention aims to solve these problems and enable users to take their medications with peace of mind.

[0376] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information of the user and transmitting it to the server, a means for the provider to input pharmaceutical information and transmit it to the server, and a means for inputting information on drugs that the user takes on a daily basis and transmit it to the server. This makes it possible to manage the basic information of the user, pharmaceutical information, and information on individual drugs in an integrated manner, and to provide optimal drug combinations and suggestions by combining evaluations by a generative AI model and emotion recognition by an emotion engine.

[0377] A "user" is an individual who uses the system and enters their own basic information and information about the medications they are taking.

[0378] "Provider" refers to a corporation or organization that inputs pharmaceutical information, generally a drug manufacturer or medical institution.

[0379] "Basic information" refers to personal data such as the user's gender, age, height, weight, and symptoms.

[0380] "Pharmaceutical information" refers to detailed data such as the name of a drug, its ingredients, efficacy, and side effects.

[0381] "Pharmaceutical information" refers to information about medicines and supplements that users take on a daily basis.

[0382] A "server" refers to a computer system that stores and manages input information and analyzes it in cooperation with generative AI models and emotion engines.

[0383] A "generative AI model" refers to an artificial intelligence system that analyzes data based on the user's basic information and pharmaceutical information, and makes recommendations on optimal drug combinations.

[0384] An "emotion engine" is a system that identifies emotions based on a user's facial recognition data and voice data, and adjusts evaluation results and proposal content based on those results.

[0385] This invention is a drug combination evaluation system that combines a generative AI model and an emotion engine to help users take their medications safely and effectively. This system collects basic information, pharmacological information, and information on the drugs they regularly take, and analyzes this data to provide optimal drug combinations and recommendations.

[0386] System Components

[0387] 1. Server

[0388] Stores and manages basic and pharmaceutical information submitted by multiple users.

[0389] Data is shared with generative AI models and emotion engines.

[0390] Stores pharmaceutical information sent by the provider.

[0391] The evaluation results obtained from the generative AI model and the emotion evaluation results obtained from the emotion engine are stored in a database.

[0392] 2. Terminal

[0393] This includes devices such as smartphones and computers that allow users to enter basic and pharmaceutical information.

[0394] It has the ability to collect facial recognition data and voice data and send it to a server.

[0395] 3. Generative AI Models

[0396] The user's basic information and pharmaceutical information provided by the server are analyzed.

[0397] As a result of the analysis, the system makes recommendations for safe and effective drug combinations and optimal treatment for specific symptoms.

[0398] 4. Emotion Engine

[0399] The system identifies the user's emotions based on facial recognition data and voice data provided by the device.

[0400] Adjust the evaluation results and recommendations depending on your emotional state.

[0401] 5. Pharmaceutical Data Entry System

[0402] It provides a mechanism for providers to input pharmaceutical information and send it to the server.

[0403] System operation example

[0404] An example of the operation of this system is shown below.

[0405] 1. User Registration

[0406] The user enters their basic information (gender, age, height, weight, symptoms, etc.) using a smartphone, and the device sends this information to a server, which then stores the received information in a database.

[0407] 2. Drug information registration

[0408] Providers use the management screen to enter their company's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server, which then stores the received pharmaceutical information in a database.

[0409] 3. User medication information input

[0410] The user enters information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis, and the device sends this information to the server, which then stores the received information in a database.

[0411] 4. AI evaluation

[0412] The server acquires the user's basic information and pharmaceutical information and passes that data to a generative AI model. The generative AI model analyzes the data and suggests drug interactions and the best medications and supplements for specific symptoms. The server receives the evaluation results from the AI ​​and stores them in a database.

[0413] 5. Emotion Recognition by Emotion Engine

[0414] The device collects the user's facial recognition data and voice data and sends it to the server. The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[0415] 6. Adjusting the proposal

[0416] The server adjusts the evaluation results and recommendations of the generative AI model based on the results of the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[0417] Specific examples

[0418] A specific example of the system is shown below: When a user regularly takes over-the-counter vitamins and also takes a new medicine prescribed by a hospital, the system operates as follows.

[0419] The user enters basic information on their smartphone and sends it to the server.

[0420] The provider registers pharmaceutical information on the server.

[0421] The user enters information about the over-the-counter and prescription medications they regularly take and sends it to the server.

[0422] The server passes information to the generative AI model, which analyzes it and returns the evaluation results.

[0423] The terminal collects the user's emotional state and the emotion engine analyzes it.

[0424] The server adjusts the evaluation results and proposals based on the results of the emotion engine.

[0425] It provides users with tailored recommendations to help them take their medication safely and effectively.

[0426] Prompt Sentence Examples

[0427] The following prompt is given to the generative AI model:

[0428] The basic information of the user is as follows:

[0429] Gender: Male

[0430] Age: 35

[0431] Height: 175cm

[0432] Weight: 70kg

[0433] Symptoms: High blood pressure

[0434] Information about medications you are currently taking:

[0435] Over-the-counter medication: Vitamin C supplements

[0436] Prescription medication: Atenolol (hypertension medication)

[0437] The user's emotional state is as follows:

[0438] Stress level: High

[0439] Emotion: Anxiety

[0440] Based on the above information, please suggest the best medication and combination.

[0441] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] The user uses a smartphone or computer to enter their basic information (gender, age, height, weight, symptoms, etc.). The information is checked in real time by the device to ensure there are no omissions or errors. The confirmed information is sent from the device to a server, where it is stored in a database.

[0444] Input: User's basic information (gender, age, height, weight, symptoms, etc.)

[0445] Output: Basic information data stored on the server

[0446] Specific operation: The user enters information such as age and weight into the application form on their smartphone and presses the "Submit" button. The device then sends the information to the server, which then stores it in a database.

[0447] Step 2:

[0448] The provider enters pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) through a dedicated management screen. The entered information is sent by the terminal to the server, where it is stored in a database.

[0449] Input: Provider's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.)

[0450] Output: Pharmaceutical information data stored on the server

[0451] Specific operation: The provider enters information about the new drug on the management screen and presses the "Register" button. The terminal sends the information to the server, which stores it in a database.

[0452] Step 3:

[0453] Users use their smartphones or computers to enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take on a daily basis. The device checks the entered information in real time to ensure there are no omissions or errors. The confirmed information is sent to a server, which then stores it in a database.

[0454] Input: User's medication information (over-the-counter drugs, vitamins, herbal medicines, prescription drugs)

[0455] Output: Medication information data stored on the server

[0456] Specific operation: The user enters information about over-the-counter or prescription drugs into the application form and presses the "Submit" button. The device sends the information to the server, which stores it in a database.

[0457] Step 4:

[0458] The server acquires the user's stored basic information, pharmaceutical information, and medication information, and passes this data to the generative AI model. The generative AI model analyzes this data and suggests drug interactions and the best medications and supplements for specific symptoms. The evaluation results generated by the generative AI model are returned to the server, where they are stored in a database.

[0459] Input: Basic information, pharmaceutical information, medication information

[0460] Output: Evaluation results by generative AI model

[0461] How it works: The server retrieves the user's basic information, pharmaceutical information, and medication information from the database and inputs this information into the generative AI model. The generative AI model analyzes the data and generates an evaluation result that says, "There is no problem with taking vitamin C and atenolol together, but be careful of interactions with food," and returns this result to the server.

[0462] Step 5:

[0463] The user collects facial recognition data and voice data through a device (such as a smartphone), and this data is sent to a server. The server passes this data to an emotion engine, which identifies the user's emotion. The results generated by the emotion engine are returned to the server, where they are stored in a database.

[0464] Input: Face recognition data, voice data

[0465] Output: Emotion evaluation results by the emotion engine

[0466] Specific operation: The user turns their face towards the device camera, and the emotion engine determines that the user is currently feeling stressed. The result is sent to the server and stored in a database.

[0467] Step 6:

[0468] The server adjusts the generative AI model's evaluation results and suggestions based on the emotion evaluation results from the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect. The adjusted evaluation results and suggestions are stored in a database.

[0469] Input: Emotion evaluation results, evaluation results of generative AI model

[0470] Output: Adjusted proposal

[0471] Specific operation: If the emotion engine identifies that the user is feeling stressed, the server changes the evaluation result of the generative AI model to suggest a supplement that has a relaxing effect.

[0472] Step 7:

[0473] The server generates feedback to provide the user with the adjusted evaluation results. The device displays this feedback to the user, allowing the user to confirm the feedback. This allows the user to take their medication safely and effectively based on the provided information.

[0474] Input: Adjusted proposal

[0475] Output: Feedback provided to the user

[0476] Specific operation: The server generates feedback such as "The user has high blood pressure and stress, so we recommend that you also take supplements that are expected to have a relaxing effect," and sends it to the device. The user checks the feedback on their smartphone and presses the "I understand" button.

[0477] (Application example 2)

[0478] 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."

[0479] Conventional medication systems have the problem of being unable to consider the user's emotional state when evaluating interactions between multiple medications taken by the user, making it difficult to provide optimal recommendations for the user. Furthermore, simply providing safe drug combinations based on information entered by the user often results in insufficient recommendations. Therefore, there is a need for a system that can provide appropriate recommendations while taking the user's emotional state into account.

[0480] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0481] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the AI, means for the AI ​​to make an evaluation based on the user's basic information and drug information and return it to the server, means for the server to provide the user with the evaluation results from the AI, means for the terminal to collect the user's facial recognition data and voice data and for the emotion engine to identify emotions based on those, and means for the server to adjust the evaluation results and proposal content based on the emotional state from the emotion engine. This makes it possible to provide safe and effective drug combinations and optimal proposals while taking the user's emotional state into consideration.

[0482] "Basic information about the user" refers to the user's gender, age, height, weight, symptoms, and other information related to personal identification.

[0483] "Server" means a centralized computer system that receives, stores, and processes data submitted by users and pharmaceutical companies.

[0484] "Drug information" refers to the name of the drug, its ingredients, effects, side effects, and other details related to the drug.

[0485] "Information about medications taken by the user on a daily basis" refers to information about medications, vitamins, supplements, and herbal medicines that the user takes on a daily basis.

[0486] "AI" is an artificial intelligence system that analyzes data based on the user's basic information and drug information, and evaluates drug interactions.

[0487] The "evaluation results" are information obtained as a result of the AI's analysis, such as whether or not drugs can be taken together and suggestions for the most appropriate drugs and supplements.

[0488] A "terminal" is a device that users use to input basic information and medication information, such as a smartphone or computer.

[0489] "Facial recognition data" is feature data acquired based on a facial image of a user.

[0490] "Voice data" is characteristic data obtained by recording the user's voice.

[0491] The "Emotion Engine" is a system that identifies a user's emotional state based on facial recognition data and voice data.

[0492] "Adjusting the proposal content" means changing the AI's evaluation results and proposal content based on the user's emotional state identified by the emotion engine.

[0493] The present invention is a system that combines AI and an emotion engine to evaluate drug interactions and provide optimal recommendations based on the user's emotional state, so that users can take their medication safely and effectively. Specific embodiments of this system are described in detail below.

[0494] System Configuration

[0495] The system consists of the following main components:

[0496] 1. Server

[0497] 2. Device (smartphone, PC, etc.)

[0498] 3. AI (Generation AI)

[0499] 4. Emotion Engine

[0500] 5. Database

[0501] Hardware and software used

[0502] Hardware: Smartphone camera (for facial recognition), microphone (for voice data acquisition), server (for data storage and processing)

[0503] Software: OpenCV (face recognition), DeepFace (emotion analysis), requests (API communication), RESTful API server (combination evaluation)

[0504] Processing Description

[0505] Enter user and medication information

[0506] Users use a device such as a smartphone to enter their basic information (gender, age, height, weight, symptoms, etc.). The entered basic information is sent from the device to a server and stored in a database. Pharmaceutical companies use a management screen to enter their own drug information (drug name, ingredients, efficacy, side effects, etc.) and similarly send this to the server. This aggregates information about users and drugs.

[0507] Drug interaction evaluation

[0508] The server acquires the user's basic information and medication information and sends it to the generation AI. The generation AI evaluates drug interactions based on this information and returns the evaluation results to the server. The evaluation results are stored in a database.

[0509] emotion recognition

[0510] The user's facial recognition data and voice data are collected using the smartphone's camera and microphone. This data is sent to a server and analyzed by the emotion engine, which uses DeepFace to identify emotions from facial images and also analyzes voice data.

[0511] Adjusting the proposal

[0512] The server adjusts the evaluation results and recommendations based on the emotional state identified by the emotion engine. For example, if the user is feeling stressed, it will suggest medication or supplements that have a relaxing effect. The adjusted evaluation results are provided to the user, who can view them on their device as feedback.

[0513] Specific examples

[0514] For example, if a user is taking a new prescription medication while also taking over-the-counter vitamins, the system would operate as follows:

[0515] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[0516] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[0517] 3. Enter user medication information: Enter information about over-the-counter medications, vitamins, and prescription medications that users regularly take.

[0518] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which then analyzes them and makes an evaluation.

[0519] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[0520] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[0521] 7. Providing feedback: The server provides the adjusted evaluation results to the user, who can then check the feedback to feel more comfortable taking their medication.

[0522] Prompt Sentence Examples

[0523] "Based on the information entered by the user, the generative AI model evaluates drug interactions. The DeepFace library is used to recognize emotions from the user's facial image, and the emotional results are reflected in the evaluation results."

[0524] In this way, this system can suggest safe and effective drug combinations that take into account the user's emotional state.

[0525] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0526] Step 1:

[0527] Entering user information

[0528] Users use devices such as smartphones to input their basic information (gender, age, height, weight, symptoms, etc.). This basic information is sent to the server by the device. The input data is then saved in the server's database.

[0529] Step 2:

[0530] Entering drug information

[0531] Pharmaceutical companies use the management screen to input their drug information (drug name, ingredients, efficacy, side effects, etc.). This information is sent from the management screen to the server, where it is stored in the server's database.

[0532] Step 3:

[0533] Entering information about medications currently being used

[0534] Users use a device such as a smartphone to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis. This medication information is sent from the device to a server, where it is stored in the server's database.

[0535] Step 4:

[0536] Request for drug combination evaluation

[0537] The server retrieves the user's basic information and medication information from the database and sends this data to the generation AI. The AI ​​evaluates drug interactions based on the received data and returns the results to the server. The evaluation results include whether the drug interaction is appropriate and the risks of inappropriate drug interactions.

[0538] Step 5:

[0539] Emotion recognition data collection

[0540] Users use their smartphone's camera and microphone to collect facial recognition and voice data, which is then sent from the device to a server, which then passes the data to the emotion engine.

[0541] Step 6:

[0542] Emotional state analysis

[0543] The facial recognition data and voice data sent to the server are analyzed by an emotion engine (DeepFace) to identify the user's emotional state. The emotion engine determines the user's emotions (e.g., stress, anxiety, joy, etc.) based on the acquired data.

[0544] Step 7:

[0545] Adjustment of evaluation results

[0546] The server adjusts the AI's drug combination assessment results and recommendations based on the emotional state from the emotion engine. For example, if the user is feeling stressed, it will suggest medications or supplements with a relaxing effect. The adjusted results are the final feedback provided to the user.

[0547] Step 8:

[0548] Providing Feedback

[0549] The server returns the adjusted evaluation results to the device, and the user can check this feedback on their smartphone, etc. The feedback includes recommendations on the safety of drug interactions and the optimal medications and supplements based on their emotional state.

[0550] In this way, the system of the present invention processes the data entered at each step, provides safe and effective drug combinations, and makes optimal suggestions based on the user's emotional state.

[0551] 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.

[0552] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0553] 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.

[0554] [Second embodiment]

[0555] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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. 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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."

[0567] This invention is a system that uses AI to evaluate interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations to help users take their medication safely and effectively.

[0568] System Overview

[0569] This system collects basic information about the user and medication information, and the AI ​​analyzes that data to provide feedback on whether medications can be taken together and what the optimal combination is. To achieve this, the system includes the following components:

[0570] 1. Server

[0571] It stores and manages user and drug information and works in conjunction with AI.

[0572] 2. Terminal

[0573] A device used to send basic information and medication information entered by users to a server, including smartphones and PCs.

[0574] 3. Generation AI

[0575] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[0576] 4. Pharmaceutical company data entry system

[0577] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[0578] Program processing flow

[0579] Program processing overview

[0580] This program performs processing in the following steps:

[0581] 1. User Registration

[0582] 2. Drug information registration

[0583] 3. User medication information input

[0584] 4. AI evaluation

[0585] 5. Providing Feedback

[0586] Natural language explanation of program processing

[0587] 1. User Registration

[0588] Using the terminal, the user inputs their basic information (gender, age, height, weight, symptoms, etc.), and the terminal then transmits the input information to the server.

[0589] The server stores the received information in a database.

[0590] 2. Drug information registration

[0591] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[0592] The server stores the received drug information in a database.

[0593] 3. User medication information input

[0594] Using the terminal, users enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily, and the terminal then sends the entered information to the server.

[0595] The server stores the received drug information in a database.

[0596] 4. AI evaluation

[0597] The server obtains the user's basic information and medication information and passes this data to the AI.

[0598] The generative AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medications or supplements for specific symptoms.

[0599] The server receives the evaluation results from the AI ​​and stores them in a database.

[0600] 5. Providing Feedback

[0601] The server generates feedback to provide the evaluation results to the user.

[0602] Using the terminal, the user checks the feedback.

[0603] Specific examples

[0604] scenario

[0605] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication from a hospital, the system operates as follows:

[0606] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[0607] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[0608] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[0609] 4. Evaluation by AI: The server passes the user's basic information and medication information to the AI, which analyzes them and makes an evaluation.

[0610] 5. Providing feedback: The server provides the user with the evaluation results from the AI, and the user can check the feedback to take the medicine with peace of mind.

[0611] This system allows users to easily and quickly obtain information on safe drug interactions, eliminating the need to research on their own.

[0612] The processing flow will be explained below.

[0613] Step 1:

[0614] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[0615] Step 2:

[0616] The server stores the received user information in a database.

[0617] Step 3:

[0618] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[0619] Step 4:

[0620] The server stores the received medication information in a database.

[0621] Step 5:

[0622] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[0623] Step 6:

[0624] The server stores the received medication information in a database.

[0625] Step 7:

[0626] The server obtains the user's basic information and medication information and passes it to the generation AI.

[0627] Step 8:

[0628] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[0629] Step 9:

[0630] The generation AI returns the evaluation results to the server.

[0631] Step 10:

[0632] The server receives the evaluation results from the AI ​​and stores them in a database.

[0633] Step 11:

[0634] The server generates feedback to provide the evaluation results to the user.

[0635] Step 12:

[0636] Users can use the device to check feedback and use it as a reference for taking medication safely.

[0637] Example 1

[0638] 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."

[0639] In today's world, it is common for people to take multiple medications and supplements at the same time, but it is difficult to predict the side effects and diminished effectiveness of these medications. It is also difficult for users to determine for themselves whether the combination of medications prescribed by a medical institution, over-the-counter medications, and supplements is appropriate. Conventional methods cannot make such judgments quickly and accurately, potentially hindering the safe and effective use of medications.

[0640] 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.

[0641] In this invention, the server includes means for inputting basic information about the user and transmitting it to the server, means for the pharmaceutical company to input drug information and transmit it to the server, means for inputting information about drugs the user takes on a daily basis and transmitting it to the server, means for passing data to the generated AI, means for the generated AI to return evaluation results, and means for providing the evaluation results to the user. This allows the user to easily and quickly obtain information about safe drug interactions without having to do the effort of researching them themselves.

[0642] "Basic information of the user" refers to basic information about the user, such as gender, age, height, weight, symptoms, etc.

[0643] "Drug information" refers to detailed information about drugs, such as drug name, ingredients, efficacy, and side effects.

[0644] "Server" refers to a computer system that receives and stores input data, passes the data to the generated artificial intelligence as needed, and returns the evaluation results.

[0645] "Generated artificial intelligence" refers to an algorithm or system that analyzes the user's basic information and drug information provided by the server, and evaluates whether or not drugs can be taken together and what the optimal combination is.

[0646] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[0647] "Evaluation results" refer to the results of the analysis conducted by the generated artificial intelligence, such as whether or not a drug can be taken with other drugs, and suggestions for the most appropriate medications and supplements.

[0648] "Feedback" refers to information that is presented to the user as evaluation results and helps the user understand the content.

[0649] "Daily medication information" refers to information on over-the-counter medications, vitamins, herbal medicines, and medications prescribed by hospitals that users take daily.

[0650] This invention is a system that uses generated artificial intelligence to evaluate the interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations so that users can take their medicines safely and effectively.

[0651] System configuration

[0652] The system includes the following components:

[0653] 1. Server

[0654] The server stores and manages user information and drug information, and interacts with the generated artificial intelligence.

[0655] 2. Terminal

[0656] The terminal is a device used to send basic information and medication information entered by the user to the server, and includes smartphones, PCs, etc.

[0657] 3. Generated Artificial Intelligence

[0658] The generated artificial intelligence analyzes data based on the user's basic information and medication information, and evaluates the optimal drug combination.

[0659] 4. Pharmaceutical company data entry system

[0660] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[0661] Program processing overview

[0662] The program follows these steps:

[0663] 1. User Registration

[0664] The user uses the terminal to input their basic information (gender, age, height, weight, symptoms, etc.) and transmits the information to the server.

[0665] The server stores the received information in a database.

[0666] 2. Drug information registration

[0667] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[0668] The server stores the received drug information in a database.

[0669] 3. User medication information input

[0670] The user uses a terminal to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines that he or she takes on a daily basis, and sends the information to the server.

[0671] The server stores the received drug information in a database.

[0672] 4. Evaluation by generated artificial intelligence

[0673] The server obtains the user's basic information and medication information and passes this data to the generated artificial intelligence.

[0674] The generated AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medicines or supplements for specific symptoms.

[0675] The server receives the evaluation results from the generated artificial intelligence and stores them in a database.

[0676] 5. Providing Feedback

[0677] The server generates feedback to provide the evaluation results to the user.

[0678] The user uses the terminal to check the feedback.

[0679] Specific examples

[0680] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication, the system works as follows:

[0681] 1. User Registration:

[0682] The user enters basic information (gender: female, age: 45, weight: 60 kg, symptoms: chronic headache) and sends it to the server.

[0683] 2. Drug Information Registration:

[0684] A pharmaceutical company registers information about a newly developed drug (drug name: PainEase, ingredients: ibuprofen, efficacy: pain relief, side effect: stomach discomfort) on a server.

[0685] 3. User medication information input:

[0686] The user enters information about the vitamins they regularly take (vitamin D), over-the-counter medications (allergy medication "AllergyFix"), and any new medications they have been prescribed by their doctor ("PainEase").

[0687] 4. Evaluation by generated artificial intelligence:

[0688] The server passes the user's basic information and medication information to the generated AI, which then analyzes and evaluates them.

[0689] For example, evaluation results show that Vitamin D and AllergyFix are safe, but combining them with PainEase may increase stomach discomfort.

[0690] 5. Providing Feedback:

[0691] The server provides the evaluation results to the user, who then checks the feedback in a dedicated app to understand the timing and precautions for taking painkillers.

[0692] Prompt Sentence Examples

[0693] "Based on the following drug information, please evaluate the safe interactions between over-the-counter medications and medications prescribed by your doctor.

[0694] Basic information: Gender: Female Age: 45 Weight: 60kg

[0695] Symptoms: Chronic headache

[0696] Over-the-counter medications: Vitamin D supplements, allergy medications

[0697] Prescription Drugs: Newly Prescribed Pain Reliever Medications"

[0698] By inputting this prompt sentence into the generated artificial intelligence, a specific drug combination evaluation can be performed.

[0699] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0700] Step 1:

[0701] User registration

[0702] The user inputs basic information such as gender, age, height, weight, symptoms, etc. using a terminal. For example, the user inputs the required information into a form on a dedicated application or website.

[0703] The terminal encodes the input information and transmits it to the server using a secure communication protocol (for example, HTTPS).

[0704] The server stores the received information in a database. Specifically, the server analyzes the received data and stores it in the database as a new record.

[0705] Input: Basic information such as gender, age, height, weight, symptoms, etc.

[0706] Output: A record of basic information stored in the database

[0707] Step 2:

[0708] Drug Information Registration

[0709] Pharmaceutical companies use the management screen to enter information about newly developed drugs (drug name, ingredients, efficacy, side effects, etc.) and send it to the server. For example, a pharmaceutical company employee enters detailed drug information on the management screen and presses the send button.

[0710] The server stores the received drug information in a database. Specifically, the server analyzes the received data and adds the information to the corresponding table in the database.

[0711] Input: Drug information such as drug name, ingredients, efficacy, side effects, etc.

[0712] Output: Medication records stored in a database

[0713] Step 3:

[0714] User drug information input

[0715] Users use their devices to enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily. For example, users can fill out a form on a dedicated application or website to enter this information.

[0716] The terminal encodes the input information and transmits it to the server.

[0717] The server stores the received drug information in a database. For example, the server analyzes the received data and stores it in a drug information table in the database.

[0718] Input: Information on over-the-counter medicines, vitamins, herbal medicines, and prescription medicines

[0719] Output: User's medication record stored in database

[0720] Step 4:

[0721] Evaluation by generated artificial intelligence

[0722] The server obtains the user's basic information and medication information and passes that data to the generated AI. Specifically, the server queries the relevant basic information and medication information from the database and sends a request to the generated AI's API.

[0723] The generated AI analyzes the data based on the received information and suggests drug interactions and the best medicines or supplements for specific symptoms. For example, the generated AI model analyzes the request and makes an evaluation using its internal algorithm.

[0724] The server receives the evaluation results from the generated artificial intelligence and stores them in a database. Specifically, the server receives the evaluation results returned from the generated artificial intelligence and stores them in a database in an appropriate format.

[0725] Input: Basic information and medication information

[0726] Output: Evaluation results generated by AI

[0727] Step 5:

[0728] Providing Feedback

[0729] The server generates feedback to provide the evaluation results to the user. Specifically, the server organizes the evaluation results and converts them into a report format that is easy for the user to understand.

[0730] The user uses the device to review the feedback, for example, the user opens an application or website and reviews the feedback provided.

[0731] Input: Evaluation results generated by AI

[0732] Output: Feedback provided to the user

[0733] (Application example 1)

[0734] 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."

[0735] When users purchase medicine, it is difficult to confirm whether it is safe to take it with other medicines or supplements they are already taking. In addition, there is no easy way for users considering purchasing medicine to understand interactions, making it difficult to select a safe and effective medicine.

[0736] 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.

[0737] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for inputting information on drugs the user is considering purchasing and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the generative AI model, means for the generative AI model to evaluate the user's basic information and drug information based on the user's basic information and drug information and return it to the server, and means for the server to provide the evaluation result from the generative AI model to the user. This allows the user to check in real time the interactions between the drug they are considering purchasing and drugs they are already taking and safely select a drug.

[0738] "Basic information" refers to personal information such as the user's age, gender, height, weight, medical history, etc.

[0739] "Drug information" refers to detailed data about drugs, such as drug name, ingredients, efficacy, and side effects.

[0740] A "generative AI model" is an artificial intelligence model that analyzes and evaluates based on the user's basic information and drug information.

[0741] "Mobile device" refers to a computing device that can be carried around and used, such as a smartphone or tablet.

[0742] "Barcode scanning" refers to the act or means of reading the barcode attached to a product using the camera function of a device, etc.

[0743] "Server" refers to a computer system that processes, stores, and provides information over a network.

[0744] "Feedback" refers to information and advice provided to users based on the evaluation results of the generative AI model.

[0745] "Pharmaceutical company" refers to a legal entity that manufactures and sells drugs.

[0746] "Drug interactions" refers to the interactions and safety of taking multiple medications or supplements at the same time.

[0747] This invention relates to a system for virtual stores that allows users to check in real time the interactions between drugs and supplements they are considering purchasing. This system uses the user's basic information and drug information to perform an evaluation using a generative AI model, and provides the evaluation results as feedback.

[0748] System Components

[0749] 1. Server

[0750] The server stores and manages basic user information and medication information, and links it with the generative AI model. SQL Server or similar can be used as the database.

[0751] 2. Mobile devices

[0752] A device that allows users to input and send their basic information and medication information. This applies to mobile devices such as smartphones and tablets.

[0753] 3. Generative AI Models

[0754] This is an artificial intelligence model that analyzes data based on the user's basic information and medication information, and evaluates whether or not medications can be taken together and suggests the most appropriate medication. The generative AI model runs on a server.

[0755] Specific operation of the system

[0756] The system works as follows:

[0757] 1. User Registration

[0758] A user uses a mobile device to input their basic information (age, sex, height, weight, medical history, etc.) and sends it to the server, which then stores the received information in a database.

[0759] 2. Drug information registration

[0760] Pharmaceutical companies register drug information (drug name, ingredients, efficacy, side effects, etc.) through the management screen and send it to the server. The server stores the received drug information in a database.

[0761] 3. User medication information input

[0762] Users enter information about the medications they take regularly or are considering purchasing into their mobile device by scanning barcodes or manually entering it, and then send it to the server, which receives it and stores it in a database.

[0763] 4. Evaluation using generative AI models

[0764] The server acquires the user's basic information and medication information and passes it to the generative AI model. The generative AI model analyzes this information and evaluates whether the medications are compatible and suggests the most appropriate medications. The evaluation results are returned to the server and stored in a database.

[0765] 5. Providing real-time feedback

[0766] The server generates feedback using the evaluation results from the generative AI model and sends it to the mobile device, where the user can check the content of the feedback on the screen.

[0767] Specific examples

[0768] For example, if a user tries to purchase a particular supplement from a virtual store, the system works as follows:

[0769] 1. User registration: A 40-year-old man installs the app and enters his basic information.

[0770] 2. Drug information registration: Information on newly developed drugs by pharmaceutical companies is registered on the server.

[0771] 3. User medication information entry: The user uses a mobile device to scan the barcode of the medication they are considering purchasing.

[0772] 4. Evaluation by generative AI model: The server obtains the user's basic information and drug information and passes it to the generative AI model, which analyzes the data and performs an evaluation.

[0773] 5. Providing real-time feedback: The server generates feedback based on the evaluation results of the generative AI model and sends it to the user's mobile device. The user confirms the feedback, such as "There is no problem with this combination. It is safe to take."

[0774] Prompt Sentence Examples

[0775] When users use the app, they are prompted with:

[0776] "Check whether your regular medications interact with the medication you are considering purchasing. Scan the barcode or enter the name of the medication."

[0777] Prompt sentence to input to the generative AI model:

[0778] User basic information: Age 40, Gender Male, Weight 70kg, Medical history Hypertension

[0779] Drugs under consideration for purchase: Loxoprofen, current medications: Vitamin C, prescription drug Losartan

[0780] Please rate this combination and what works best for you."

[0781] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0782] Step 1:

[0783] User Registration

[0784] Input: The user uses a mobile device to input basic information such as gender, age, height, weight, and medical history.

[0785] Data processing: The terminal converts the input information into an appropriate format and sends it to the server, for example, packaging the data in JSON format.

[0786] Output: The server receives the information and stores it in a database, which is used as input data for a generative AI model in a later step.

[0787] Step 2:

[0788] Drug Information Registration

[0789] Input: The pharmaceutical company inputs drug information such as the drug name, ingredients, efficacy, and side effects through the management screen.

[0790] Data processing: The drug company's terminal sends the entered drug information to the server. The data is packaged with the drug's identifier.

[0791] Output: The server stores the received drug information in a database. The stored information is used by the generative AI model for evaluation.

[0792] Step 3:

[0793] User drug information input

[0794] Input: The user inputs information about medications they take regularly or are considering purchasing into their mobile device, for example by scanning the medication barcode or manually entering the medication name.

[0795] Data processing: The terminal sends the entered medication information to the server. At this time, the data is saved in association with the user ID.

[0796] Output: The server receives the information and stores it in a database, which is used as input data for the generative AI model to evaluate.

[0797] Step 4:

[0798] Evaluation by generative AI model

[0799] Input: The server retrieves the user's basic information and medication information from the database. The retrieved information includes the user's basic information (age, gender, medical history, etc.), existing medication information, and newly entered medication information.

[0800] Data processing and calculation: The server passes the acquired data to the generative AI model. The generative AI model analyzes the data and determines whether or not drugs can be taken together and suggests the most appropriate medication. For example, it evaluates safety by taking into account the ingredients of the drug and the user's health condition.

[0801] Output: The generative AI model creates an evaluation result and returns it to the server. The evaluation result includes specific advice and points to note.

[0802] Step 5:

[0803] Providing real-time feedback

[0804] Input: The server receives the evaluation results returned by the generative AI model.

[0805] Data processing: The server converts the evaluation results into feedback data to be provided to the user, for example, in a format that is easy for the user to understand.

[0806] Output: The feedback data is sent to the mobile device and displayed to the user. The user can check the feedback on the mobile device screen and safely select a medication.

[0807] Specific examples

[0808] If the user is a 40-year-old man and the drug he is considering purchasing is loxoprofen:

[0809] 1. Step 1: User enters basic information, which is sent to the server and stored in the database.

[0810] 2. Step 2: The pharmaceutical company registers the information about rofecoxib, sends it to the server, and stores it in a database.

[0811] 3. Step 3: The user scans the barcode of the loxoprofen, enters the information, and sends it to the server.

[0812] 4. Step 4: The server passes basic information and drug information to the generative AI model, which evaluates drug interactions.

[0813] 5. Step 5: The server converts the evaluation results into feedback and provides it to the user. The user confirms, "There is no problem with this combination. It is safe to take."

[0814] 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.

[0815] This invention is a system that combines AI and an emotion engine to evaluate drug interactions and make optimal suggestions based on the user's emotional state, so that users can take their medication safely and effectively.

[0816] System Overview

[0817] This system acquires basic information about the user and medication information, and the AI ​​analyzes this data. It also uses an emotion engine to recognize the user's emotions and adjusts the evaluation results and recommendations according to their emotional state. This allows it to provide optimal drug combinations and recommendations that take the user's emotions into account.

[0818] System Components

[0819] 1. Server

[0820] It stores and manages user and drug information, and works in conjunction with AI and emotion engines.

[0821] 2. Terminal

[0822] A device that allows users to enter basic information and medication information, including smartphones and computers.

[0823] 3. Generation AI

[0824] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[0825] 4. Emotion Engine

[0826] The system identifies the user's emotions based on facial recognition and voice data, and adjusts the evaluation results and suggestions accordingly.

[0827] 5. Pharmaceutical company data entry system

[0828] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[0829] Program processing flow

[0830] Program processing overview

[0831] This program performs processing in the following steps:

[0832] 1. User Registration

[0833] 2. Drug information registration

[0834] 3. User medication information input

[0835] 4. AI evaluation

[0836] 5. Emotion Recognition by Emotion Engine

[0837] 6. Adjusting the proposal

[0838] 7. Providing Feedback

[0839] Natural language explanation of program processing

[0840] 1. User Registration

[0841] Using the terminal, the user enters their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[0842] The server stores the received information in a database.

[0843] 2. Drug information registration

[0844] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[0845] The server stores the received drug information in a database.

[0846] 3. User medication information input

[0847] Using the device, users enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take daily, and the device then sends this information to a server.

[0848] The server stores the received drug information in a database.

[0849] 4. AI evaluation

[0850] The server obtains the user's basic information and medication information and passes this data to the generation AI.

[0851] The generative AI analyzes the data based on the information it receives, and suggests whether or not drugs can be taken together, as well as the best medications and supplements for specific symptoms.

[0852] The server receives the evaluation results from the AI ​​and stores them in a database.

[0853] 5. Emotion Recognition by Emotion Engine

[0854] The device collects the user's facial recognition data and voice data and sends it to the server.

[0855] The emotion engine uses this data to identify the user's emotions.

[0856] 6. Adjusting the proposal

[0857] The server adjusts the evaluation results and suggestions based on the emotional state from the emotion engine.

[0858] For example, if a user is feeling stressed, the system will suggest medications or supplements that have a relaxing effect.

[0859] 7. Providing Feedback

[0860] The server generates feedback to provide the adjusted evaluation results to the user.

[0861] Using the device, users can view feedback and use it as a reference for taking medication safely.

[0862] Specific examples

[0863] scenario

[0864] For example, if a user regularly takes over-the-counter vitamins and also takes a new prescription drug from a hospital, the system operates as follows.

[0865] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[0866] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[0867] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[0868] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which analyzes them and makes an evaluation.

[0869] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[0870] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[0871] 7. Providing feedback: The server provides the adjusted evaluation results to the user, and the user can check the feedback to take the medicine with peace of mind.

[0872] This system not only saves users the trouble of researching on their own and allows them to easily and quickly obtain information on safe drug combinations, but also provides optimal suggestions based on their emotional state.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[0876] Step 2:

[0877] The server stores the received user information in a database.

[0878] Step 3:

[0879] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[0880] Step 4:

[0881] The server stores the received medication information in a database.

[0882] Step 5:

[0883] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[0884] Step 6:

[0885] The server stores the received medication information in a database.

[0886] Step 7:

[0887] The server obtains the user's basic information and medication information and passes it to the generation AI.

[0888] Step 8:

[0889] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[0890] Step 9:

[0891] The generation AI returns the evaluation results to the server.

[0892] Step 10:

[0893] The server receives the evaluation results from the AI ​​and stores them in a database.

[0894] Step 11:

[0895] The device collects the user's facial recognition data and voice data and sends it to the server.

[0896] Step 12:

[0897] The server passes facial recognition data and voice data to the emotion engine.

[0898] Step 13:

[0899] The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[0900] Step 14:

[0901] The server receives the emotion recognition results from the emotion engine and adjusts the evaluation results and suggestions. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[0902] Step 15:

[0903] The server generates feedback to provide the adjusted evaluation results to the user.

[0904] Step 16:

[0905] Users can use the device to check feedback and use it as a reference for taking medication safely.

[0906] Example 2

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

[0908] Conventional medication systems have made it difficult for users to obtain appropriate information for taking multiple medications safely and effectively. Furthermore, they do not take into account the user's emotional state, resulting in a poor user experience. The present invention aims to solve these problems and enable users to take their medications with peace of mind.

[0909] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information of the user and transmitting it to the server, a means for the provider to input pharmaceutical information and transmit it to the server, and a means for inputting information on drugs that the user takes on a daily basis and transmit it to the server. This makes it possible to manage the basic information of the user, pharmaceutical information, and information on individual drugs in an integrated manner, and to provide optimal drug combinations and suggestions by combining evaluations by a generative AI model and emotion recognition by an emotion engine.

[0910] A "user" is an individual who uses the system and enters their own basic information and information about the medications they are taking.

[0911] "Provider" refers to a corporation or organization that inputs pharmaceutical information, generally a drug manufacturer or medical institution.

[0912] "Basic information" refers to personal data such as the user's gender, age, height, weight, and symptoms.

[0913] "Pharmaceutical information" refers to detailed data such as the name of a drug, its ingredients, efficacy, and side effects.

[0914] "Pharmaceutical information" refers to information about medicines and supplements that users take on a daily basis.

[0915] A "server" refers to a computer system that stores and manages input information and analyzes it in cooperation with generative AI models and emotion engines.

[0916] A "generative AI model" refers to an artificial intelligence system that analyzes data based on the user's basic information and pharmaceutical information, and makes recommendations on optimal drug combinations.

[0917] An "emotion engine" is a system that identifies emotions based on a user's facial recognition data and voice data, and adjusts evaluation results and proposal content based on those results.

[0918] This invention is a drug combination evaluation system that combines a generative AI model and an emotion engine to help users take their medications safely and effectively. This system collects basic information, pharmacological information, and information on the drugs they regularly take, and analyzes this data to provide optimal drug combinations and recommendations.

[0919] System Components

[0920] 1. Server

[0921] Stores and manages basic and pharmaceutical information submitted by multiple users.

[0922] Data is shared with generative AI models and emotion engines.

[0923] Stores pharmaceutical information sent by the provider.

[0924] The evaluation results obtained from the generative AI model and the emotion evaluation results obtained from the emotion engine are stored in a database.

[0925] 2. Terminal

[0926] This includes devices such as smartphones and computers that allow users to enter basic and pharmaceutical information.

[0927] It has the ability to collect facial recognition data and voice data and send it to a server.

[0928] 3. Generative AI Models

[0929] The user's basic information and pharmaceutical information provided by the server are analyzed.

[0930] As a result of the analysis, the system makes recommendations for safe and effective drug combinations and optimal treatment for specific symptoms.

[0931] 4. Emotion Engine

[0932] The system identifies the user's emotions based on facial recognition data and voice data provided by the device.

[0933] Adjust the evaluation results and recommendations depending on your emotional state.

[0934] 5. Pharmaceutical Data Entry System

[0935] It provides a mechanism for providers to input pharmaceutical information and send it to the server.

[0936] System operation example

[0937] An example of the operation of this system is shown below.

[0938] 1. User Registration

[0939] The user enters their basic information (gender, age, height, weight, symptoms, etc.) using a smartphone, and the device sends this information to a server, which then stores the received information in a database.

[0940] 2. Drug information registration

[0941] Providers use the management screen to enter their company's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server, which then stores the received pharmaceutical information in a database.

[0942] 3. User medication information input

[0943] The user enters information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis, and the device sends this information to the server, which then stores the received information in a database.

[0944] 4. AI evaluation

[0945] The server acquires the user's basic information and pharmaceutical information and passes that data to a generative AI model. The generative AI model analyzes the data and suggests drug interactions and the best medications and supplements for specific symptoms. The server receives the evaluation results from the AI ​​and stores them in a database.

[0946] 5. Emotion Recognition by Emotion Engine

[0947] The device collects the user's facial recognition data and voice data and sends it to the server. The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[0948] 6. Adjusting the proposal

[0949] The server adjusts the evaluation results and recommendations of the generative AI model based on the results of the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[0950] Specific examples

[0951] A specific example of the system is shown below: When a user regularly takes over-the-counter vitamins and also takes a new medicine prescribed by a hospital, the system operates as follows.

[0952] The user enters basic information on their smartphone and sends it to the server.

[0953] The provider registers pharmaceutical information on the server.

[0954] The user enters information about the over-the-counter and prescription medications they regularly take and sends it to the server.

[0955] The server passes information to the generative AI model, which analyzes it and returns the evaluation results.

[0956] The terminal collects the user's emotional state and the emotion engine analyzes it.

[0957] The server adjusts the evaluation results and proposals based on the results of the emotion engine.

[0958] It provides users with tailored recommendations to help them take their medication safely and effectively.

[0959] Prompt Sentence Examples

[0960] The following prompt is given to the generative AI model:

[0961] The basic information of the user is as follows:

[0962] Gender: Male

[0963] Age: 35

[0964] Height: 175cm

[0965] Weight: 70kg

[0966] Symptoms: High blood pressure

[0967] Information about medications you are currently taking:

[0968] Over-the-counter medication: Vitamin C supplements

[0969] Prescription medication: Atenolol (hypertension medication)

[0970] The user's emotional state is as follows:

[0971] Stress level: High

[0972] Emotion: Anxiety

[0973] Based on the above information, please suggest the best medication and combination.

[0974] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0975] Step 1:

[0976] The user uses a smartphone or computer to enter their basic information (gender, age, height, weight, symptoms, etc.). The information is checked in real time by the device to ensure there are no omissions or errors. The confirmed information is sent from the device to a server, where it is stored in a database.

[0977] Input: User's basic information (gender, age, height, weight, symptoms, etc.)

[0978] Output: Basic information data stored on the server

[0979] Specific operation: The user enters information such as age and weight into the application form on their smartphone and presses the "Submit" button. The device then sends the information to the server, which then stores it in a database.

[0980] Step 2:

[0981] The provider enters pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) through a dedicated management screen. The entered information is sent by the terminal to the server, where it is stored in a database.

[0982] Input: Provider's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.)

[0983] Output: Pharmaceutical information data stored on the server

[0984] Specific operation: The provider enters information about the new drug on the management screen and presses the "Register" button. The terminal sends the information to the server, which stores it in a database.

[0985] Step 3:

[0986] Users use their smartphones or computers to enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take on a daily basis. The device checks the entered information in real time to ensure there are no omissions or errors. The confirmed information is sent to a server, which then stores it in a database.

[0987] Input: User's medication information (over-the-counter drugs, vitamins, herbal medicines, prescription drugs)

[0988] Output: Medication information data stored on the server

[0989] Specific operation: The user enters information about over-the-counter or prescription drugs into the application form and presses the "Submit" button. The device sends the information to the server, which stores it in a database.

[0990] Step 4:

[0991] The server acquires the user's stored basic information, pharmaceutical information, and medication information, and passes this data to the generative AI model. The generative AI model analyzes this data and suggests drug interactions and the best medications and supplements for specific symptoms. The evaluation results generated by the generative AI model are returned to the server, where they are stored in a database.

[0992] Input: Basic information, pharmaceutical information, medication information

[0993] Output: Evaluation results by generative AI model

[0994] How it works: The server retrieves the user's basic information, pharmaceutical information, and medication information from the database and inputs this information into the generative AI model. The generative AI model analyzes the data and generates an evaluation result that says, "There is no problem with taking vitamin C and atenolol together, but be careful of interactions with food," and returns this result to the server.

[0995] Step 5:

[0996] The user collects facial recognition data and voice data through a device (such as a smartphone), and this data is sent to a server. The server passes this data to an emotion engine, which identifies the user's emotion. The results generated by the emotion engine are returned to the server, where they are stored in a database.

[0997] Input: Face recognition data, voice data

[0998] Output: Emotion evaluation results by the emotion engine

[0999] Specific operation: The user turns their face towards the device camera, and the emotion engine determines that the user is currently feeling stressed. The result is sent to the server and stored in a database.

[1000] Step 6:

[1001] The server adjusts the generative AI model's evaluation results and suggestions based on the emotion evaluation results from the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect. The adjusted evaluation results and suggestions are stored in a database.

[1002] Input: Emotion evaluation results, evaluation results of generative AI model

[1003] Output: Adjusted proposal

[1004] Specific operation: If the emotion engine identifies that the user is feeling stressed, the server changes the evaluation result of the generative AI model to suggest a supplement that has a relaxing effect.

[1005] Step 7:

[1006] The server generates feedback to provide the user with the adjusted evaluation results. The device displays this feedback to the user, allowing the user to confirm the feedback. This allows the user to take their medication safely and effectively based on the provided information.

[1007] Input: Adjusted proposal

[1008] Output: Feedback provided to the user

[1009] Specific operation: The server generates feedback such as "The user has high blood pressure and stress, so we recommend that you also take supplements that are expected to have a relaxing effect," and sends it to the device. The user checks the feedback on their smartphone and presses the "I understand" button.

[1010] (Application example 2)

[1011] 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."

[1012] Conventional medication systems have the problem of being unable to consider the user's emotional state when evaluating interactions between multiple medications taken by the user, making it difficult to provide optimal recommendations for the user. Furthermore, simply providing safe drug combinations based on information entered by the user often results in insufficient recommendations. Therefore, there is a need for a system that can provide appropriate recommendations while taking the user's emotional state into account.

[1013] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1014] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the AI, means for the AI ​​to make an evaluation based on the user's basic information and drug information and return it to the server, means for the server to provide the user with the evaluation results from the AI, means for the terminal to collect the user's facial recognition data and voice data and for the emotion engine to identify emotions based on those, and means for the server to adjust the evaluation results and proposal content based on the emotional state from the emotion engine. This makes it possible to provide safe and effective drug combinations and optimal proposals while taking the user's emotional state into consideration.

[1015] "Basic information about the user" refers to the user's gender, age, height, weight, symptoms, and other information related to personal identification.

[1016] "Server" means a centralized computer system that receives, stores, and processes data submitted by users and pharmaceutical companies.

[1017] "Drug information" refers to the name of the drug, its ingredients, effects, side effects, and other details related to the drug.

[1018] "Information about medications taken by the user on a daily basis" refers to information about medications, vitamins, supplements, and herbal medicines that the user takes on a daily basis.

[1019] "AI" is an artificial intelligence system that analyzes data based on the user's basic information and drug information, and evaluates drug interactions.

[1020] The "evaluation results" are information obtained as a result of the AI's analysis, such as whether or not drugs can be taken together and suggestions for the most appropriate drugs and supplements.

[1021] A "terminal" is a device that users use to input basic information and medication information, such as a smartphone or computer.

[1022] "Facial recognition data" is feature data acquired based on a facial image of a user.

[1023] "Voice data" is characteristic data obtained by recording the user's voice.

[1024] The "Emotion Engine" is a system that identifies a user's emotional state based on facial recognition data and voice data.

[1025] "Adjusting the proposal content" means changing the AI's evaluation results and proposal content based on the user's emotional state identified by the emotion engine.

[1026] The present invention is a system that combines AI and an emotion engine to evaluate drug interactions and provide optimal recommendations based on the user's emotional state, so that users can take their medication safely and effectively. Specific embodiments of this system are described in detail below.

[1027] System Configuration

[1028] The system consists of the following main components:

[1029] 1. Server

[1030] 2. Device (smartphone, PC, etc.)

[1031] 3. AI (Generation AI)

[1032] 4. Emotion Engine

[1033] 5. Database

[1034] Hardware and software used

[1035] Hardware: Smartphone camera (for facial recognition), microphone (for voice data acquisition), server (for data storage and processing)

[1036] Software: OpenCV (face recognition), DeepFace (emotion analysis), requests (API communication), RESTful API server (combination evaluation)

[1037] Processing Description

[1038] Enter user and medication information

[1039] Users use a device such as a smartphone to enter their basic information (gender, age, height, weight, symptoms, etc.). The entered basic information is sent from the device to a server and stored in a database. Pharmaceutical companies use a management screen to enter their own drug information (drug name, ingredients, efficacy, side effects, etc.) and similarly send this to the server. This aggregates information about users and drugs.

[1040] Drug interaction evaluation

[1041] The server acquires the user's basic information and medication information and sends it to the generation AI. The generation AI evaluates drug interactions based on this information and returns the evaluation results to the server. The evaluation results are stored in a database.

[1042] emotion recognition

[1043] The user's facial recognition data and voice data are collected using the smartphone's camera and microphone. This data is sent to a server and analyzed by the emotion engine, which uses DeepFace to identify emotions from facial images and also analyzes voice data.

[1044] Adjusting the proposal

[1045] The server adjusts the evaluation results and recommendations based on the emotional state identified by the emotion engine. For example, if the user is feeling stressed, it will suggest medication or supplements that have a relaxing effect. The adjusted evaluation results are provided to the user, who can view them on their device as feedback.

[1046] Specific examples

[1047] For example, if a user is taking a new prescription medication while also taking over-the-counter vitamins, the system would operate as follows:

[1048] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[1049] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[1050] 3. Enter user medication information: Enter information about over-the-counter medications, vitamins, and prescription medications that users regularly take.

[1051] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which then analyzes them and makes an evaluation.

[1052] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[1053] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[1054] 7. Providing feedback: The server provides the adjusted evaluation results to the user, who can then check the feedback to feel more comfortable taking their medication.

[1055] Prompt Sentence Examples

[1056] "Based on the information entered by the user, the generative AI model evaluates drug interactions. The DeepFace library is used to recognize emotions from the user's facial image, and the emotional results are reflected in the evaluation results."

[1057] In this way, this system can suggest safe and effective drug combinations that take into account the user's emotional state.

[1058] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1059] Step 1:

[1060] Entering user information

[1061] Users use devices such as smartphones to input their basic information (gender, age, height, weight, symptoms, etc.). This basic information is sent to the server by the device. The input data is then saved in the server's database.

[1062] Step 2:

[1063] Entering drug information

[1064] Pharmaceutical companies use the management screen to input their drug information (drug name, ingredients, efficacy, side effects, etc.). This information is sent from the management screen to the server, where it is stored in the server's database.

[1065] Step 3:

[1066] Entering information about medications currently being used

[1067] Users use a device such as a smartphone to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis. This medication information is sent from the device to a server, where it is stored in the server's database.

[1068] Step 4:

[1069] Request for drug combination evaluation

[1070] The server retrieves the user's basic information and medication information from the database and sends this data to the generation AI. The AI ​​evaluates drug interactions based on the received data and returns the results to the server. The evaluation results include whether the drug interaction is appropriate and the risks of inappropriate drug interactions.

[1071] Step 5:

[1072] Emotion recognition data collection

[1073] Users use their smartphone's camera and microphone to collect facial recognition and voice data, which is then sent from the device to a server, which then passes the data to the emotion engine.

[1074] Step 6:

[1075] Emotional state analysis

[1076] The facial recognition data and voice data sent to the server are analyzed by an emotion engine (DeepFace) to identify the user's emotional state. The emotion engine determines the user's emotions (e.g., stress, anxiety, joy, etc.) based on the acquired data.

[1077] Step 7:

[1078] Adjustment of evaluation results

[1079] The server adjusts the AI's drug combination assessment results and recommendations based on the emotional state from the emotion engine. For example, if the user is feeling stressed, it will suggest medications or supplements with a relaxing effect. The adjusted results are the final feedback provided to the user.

[1080] Step 8:

[1081] Providing Feedback

[1082] The server returns the adjusted evaluation results to the device, and the user can check this feedback on their smartphone, etc. The feedback includes recommendations on the safety of drug interactions and the optimal medications and supplements based on their emotional state.

[1083] In this way, the system of the present invention processes the data entered at each step, provides safe and effective drug combinations, and makes optimal suggestions based on the user's emotional state.

[1084] 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.

[1085] 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.

[1086] 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.

[1087] [Third embodiment]

[1088] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1089] 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.

[1090] 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).

[1091] 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.

[1092] 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.

[1093] 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).

[1094] 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. 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.

[1095] 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.

[1096] 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.

[1097] 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.

[1098] 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.

[1099] 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."

[1100] This invention is a system that uses AI to evaluate interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations to help users take their medication safely and effectively.

[1101] System Overview

[1102] This system collects basic information about the user and medication information, and the AI ​​analyzes that data to provide feedback on whether medications can be taken together and what the optimal combination is. To achieve this, the system includes the following components:

[1103] 1. Server

[1104] It stores and manages user and drug information and works in conjunction with AI.

[1105] 2. Terminal

[1106] A device used to send basic information and medication information entered by users to a server, including smartphones and PCs.

[1107] 3. Generation AI

[1108] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[1109] 4. Pharmaceutical company data entry system

[1110] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[1111] Program processing flow

[1112] Program processing overview

[1113] This program performs processing in the following steps:

[1114] 1. User Registration

[1115] 2. Drug information registration

[1116] 3. User medication information input

[1117] 4. AI evaluation

[1118] 5. Providing Feedback

[1119] Natural language explanation of program processing

[1120] 1. User Registration

[1121] Using the terminal, the user inputs their basic information (gender, age, height, weight, symptoms, etc.), and the terminal then transmits the input information to the server.

[1122] The server stores the received information in a database.

[1123] 2. Drug information registration

[1124] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[1125] The server stores the received drug information in a database.

[1126] 3. User medication information input

[1127] Using the terminal, users enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily, and the terminal then sends the entered information to the server.

[1128] The server stores the received drug information in a database.

[1129] 4. AI evaluation

[1130] The server obtains the user's basic information and medication information and passes this data to the AI.

[1131] The generative AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medications or supplements for specific symptoms.

[1132] The server receives the evaluation results from the AI ​​and stores them in a database.

[1133] 5. Providing Feedback

[1134] The server generates feedback to provide the evaluation results to the user.

[1135] Using the terminal, the user checks the feedback.

[1136] Specific examples

[1137] scenario

[1138] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication from a hospital, the system operates as follows:

[1139] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[1140] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[1141] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[1142] 4. Evaluation by AI: The server passes the user's basic information and medication information to the AI, which analyzes them and makes an evaluation.

[1143] 5. Providing feedback: The server provides the user with the evaluation results from the AI, and the user can check the feedback to take the medicine with peace of mind.

[1144] This system allows users to easily and quickly obtain information on safe drug interactions, eliminating the need to research on their own.

[1145] The processing flow will be explained below.

[1146] Step 1:

[1147] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[1148] Step 2:

[1149] The server stores the received user information in a database.

[1150] Step 3:

[1151] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[1152] Step 4:

[1153] The server stores the received medication information in a database.

[1154] Step 5:

[1155] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[1156] Step 6:

[1157] The server stores the received medication information in a database.

[1158] Step 7:

[1159] The server obtains the user's basic information and medication information and passes it to the generation AI.

[1160] Step 8:

[1161] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[1162] Step 9:

[1163] The generation AI returns the evaluation results to the server.

[1164] Step 10:

[1165] The server receives the evaluation results from the AI ​​and stores them in a database.

[1166] Step 11:

[1167] The server generates feedback to provide the evaluation results to the user.

[1168] Step 12:

[1169] Users can use the device to check feedback and use it as a reference for taking medication safely.

[1170] Example 1

[1171] 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."

[1172] In today's world, it is common for people to take multiple medications and supplements at the same time, but it is difficult to predict the side effects and diminished effectiveness of these medications. It is also difficult for users to determine for themselves whether the combination of medications prescribed by a medical institution, over-the-counter medications, and supplements is appropriate. Conventional methods cannot make such judgments quickly and accurately, potentially hindering the safe and effective use of medications.

[1173] 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.

[1174] In this invention, the server includes means for inputting basic information about the user and transmitting it to the server, means for the pharmaceutical company to input drug information and transmit it to the server, means for inputting information about drugs the user takes on a daily basis and transmitting it to the server, means for passing data to the generated AI, means for the generated AI to return evaluation results, and means for providing the evaluation results to the user. This allows the user to easily and quickly obtain information about safe drug interactions without having to do the effort of researching them themselves.

[1175] "Basic information of the user" refers to basic information about the user, such as gender, age, height, weight, symptoms, etc.

[1176] "Drug information" refers to detailed information about drugs, such as drug name, ingredients, efficacy, and side effects.

[1177] "Server" refers to a computer system that receives and stores input data, passes the data to the generated artificial intelligence as needed, and returns the evaluation results.

[1178] "Generated artificial intelligence" refers to an algorithm or system that analyzes the user's basic information and drug information provided by the server, and evaluates whether or not drugs can be taken together and what the optimal combination is.

[1179] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[1180] "Evaluation results" refer to the results of the analysis conducted by the generated artificial intelligence, such as whether or not a drug can be taken with other drugs, and suggestions for the most appropriate medications and supplements.

[1181] "Feedback" refers to information that is presented to the user as evaluation results and helps the user understand the content.

[1182] "Daily medication information" refers to information on over-the-counter medications, vitamins, herbal medicines, and medications prescribed by hospitals that users take daily.

[1183] This invention is a system that uses generated artificial intelligence to evaluate the interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations so that users can take their medicines safely and effectively.

[1184] System configuration

[1185] The system includes the following components:

[1186] 1. Server

[1187] The server stores and manages user information and drug information, and interacts with the generated artificial intelligence.

[1188] 2. Terminal

[1189] The terminal is a device used to send basic information and medication information entered by the user to the server, and includes smartphones, PCs, etc.

[1190] 3. Generated Artificial Intelligence

[1191] The generated artificial intelligence analyzes data based on the user's basic information and medication information, and evaluates the optimal drug combination.

[1192] 4. Pharmaceutical company data entry system

[1193] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[1194] Program processing overview

[1195] The program follows these steps:

[1196] 1. User Registration

[1197] The user uses the terminal to input their basic information (gender, age, height, weight, symptoms, etc.) and transmits the information to the server.

[1198] The server stores the received information in a database.

[1199] 2. Drug information registration

[1200] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[1201] The server stores the received drug information in a database.

[1202] 3. User medication information input

[1203] The user uses a terminal to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines that he or she takes on a daily basis, and sends the information to the server.

[1204] The server stores the received drug information in a database.

[1205] 4. Evaluation by generated artificial intelligence

[1206] The server obtains the user's basic information and medication information and passes this data to the generated artificial intelligence.

[1207] The generated AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medicines or supplements for specific symptoms.

[1208] The server receives the evaluation results from the generated artificial intelligence and stores them in a database.

[1209] 5. Providing Feedback

[1210] The server generates feedback to provide the evaluation results to the user.

[1211] The user uses the terminal to check the feedback.

[1212] Specific examples

[1213] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication, the system works as follows:

[1214] 1. User Registration:

[1215] The user enters basic information (gender: female, age: 45, weight: 60 kg, symptoms: chronic headache) and sends it to the server.

[1216] 2. Drug Information Registration:

[1217] A pharmaceutical company registers information about a newly developed drug (drug name: PainEase, ingredients: ibuprofen, efficacy: pain relief, side effect: stomach discomfort) on a server.

[1218] 3. User medication information input:

[1219] The user enters information about the vitamins they regularly take (vitamin D), over-the-counter medications (allergy medication "AllergyFix"), and any new medications they have been prescribed by their doctor ("PainEase").

[1220] 4. Evaluation by generated artificial intelligence:

[1221] The server passes the user's basic information and medication information to the generated AI, which then analyzes and evaluates them.

[1222] For example, evaluation results show that Vitamin D and AllergyFix are safe, but combining them with PainEase may increase stomach discomfort.

[1223] 5. Providing Feedback:

[1224] The server provides the evaluation results to the user, who then checks the feedback in a dedicated app to understand the timing and precautions for taking painkillers.

[1225] Prompt Sentence Examples

[1226] "Based on the following drug information, please evaluate the safe interactions between over-the-counter medications and medications prescribed by your doctor.

[1227] Basic information: Gender: Female Age: 45 Weight: 60kg

[1228] Symptoms: Chronic headache

[1229] Over-the-counter medications: Vitamin D supplements, allergy medications

[1230] Prescription Drugs: Newly Prescribed Pain Reliever Medications"

[1231] By inputting this prompt sentence into the generated artificial intelligence, a specific drug combination evaluation can be performed.

[1232] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1233] Step 1:

[1234] User registration

[1235] The user inputs basic information such as gender, age, height, weight, symptoms, etc. using a terminal. For example, the user inputs the required information into a form on a dedicated application or website.

[1236] The terminal encodes the input information and transmits it to the server using a secure communication protocol (for example, HTTPS).

[1237] The server stores the received information in a database. Specifically, the server analyzes the received data and stores it in the database as a new record.

[1238] Input: Basic information such as gender, age, height, weight, symptoms, etc.

[1239] Output: A record of basic information stored in the database

[1240] Step 2:

[1241] Drug Information Registration

[1242] Pharmaceutical companies use the management screen to enter information about newly developed drugs (drug name, ingredients, efficacy, side effects, etc.) and send it to the server. For example, a pharmaceutical company employee enters detailed drug information on the management screen and presses the send button.

[1243] The server stores the received drug information in a database. Specifically, the server analyzes the received data and adds the information to the corresponding table in the database.

[1244] Input: Drug information such as drug name, ingredients, efficacy, side effects, etc.

[1245] Output: Medication records stored in a database

[1246] Step 3:

[1247] User drug information input

[1248] Users use their devices to enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily. For example, users can fill out a form on a dedicated application or website to enter this information.

[1249] The terminal encodes the input information and transmits it to the server.

[1250] The server stores the received drug information in a database. For example, the server analyzes the received data and stores it in a drug information table in the database.

[1251] Input: Information on over-the-counter medicines, vitamins, herbal medicines, and prescription medicines

[1252] Output: User's medication record stored in database

[1253] Step 4:

[1254] Evaluation by generated artificial intelligence

[1255] The server obtains the user's basic information and medication information and passes that data to the generated AI. Specifically, the server queries the relevant basic information and medication information from the database and sends a request to the generated AI's API.

[1256] The generated AI analyzes the data based on the received information and suggests drug interactions and the best medicines or supplements for specific symptoms. For example, the generated AI model analyzes the request and makes an evaluation using its internal algorithm.

[1257] The server receives the evaluation results from the generated artificial intelligence and stores them in a database. Specifically, the server receives the evaluation results returned from the generated artificial intelligence and stores them in a database in an appropriate format.

[1258] Input: Basic information and medication information

[1259] Output: Evaluation results generated by AI

[1260] Step 5:

[1261] Providing Feedback

[1262] The server generates feedback to provide the evaluation results to the user. Specifically, the server organizes the evaluation results and converts them into a report format that is easy for the user to understand.

[1263] The user uses the device to review the feedback, for example, the user opens an application or website and reviews the feedback provided.

[1264] Input: Evaluation results generated by AI

[1265] Output: Feedback provided to the user

[1266] (Application example 1)

[1267] 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."

[1268] When users purchase medicine, it is difficult to confirm whether it is safe to take it with other medicines or supplements they are already taking. In addition, there is no easy way for users considering purchasing medicine to understand interactions, making it difficult to select a safe and effective medicine.

[1269] 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.

[1270] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for inputting information on drugs the user is considering purchasing and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the generative AI model, means for the generative AI model to evaluate the user's basic information and drug information based on the user's basic information and drug information and return it to the server, and means for the server to provide the evaluation result from the generative AI model to the user. This allows the user to check in real time the interactions between the drug they are considering purchasing and drugs they are already taking and safely select a drug.

[1271] "Basic information" refers to personal information such as the user's age, gender, height, weight, medical history, etc.

[1272] "Drug information" refers to detailed data about drugs, such as drug name, ingredients, efficacy, and side effects.

[1273] A "generative AI model" is an artificial intelligence model that analyzes and evaluates based on the user's basic information and drug information.

[1274] "Mobile device" refers to a computing device that can be carried around and used, such as a smartphone or tablet.

[1275] "Barcode scanning" refers to the act or means of reading the barcode attached to a product using the camera function of a device, etc.

[1276] "Server" refers to a computer system that processes, stores, and provides information over a network.

[1277] "Feedback" refers to information and advice provided to users based on the evaluation results of the generative AI model.

[1278] "Pharmaceutical company" refers to a legal entity that manufactures and sells drugs.

[1279] "Drug interactions" refers to the interactions and safety of taking multiple medications or supplements at the same time.

[1280] This invention relates to a system for virtual stores that allows users to check in real time the interactions between drugs and supplements they are considering purchasing. This system uses the user's basic information and drug information to perform an evaluation using a generative AI model, and provides the evaluation results as feedback.

[1281] System Components

[1282] 1. Server

[1283] The server stores and manages basic user information and medication information, and links it with the generative AI model. SQL Server or similar can be used as the database.

[1284] 2. Mobile devices

[1285] A device that allows users to input and send their basic information and medication information. This applies to mobile devices such as smartphones and tablets.

[1286] 3. Generative AI Models

[1287] This is an artificial intelligence model that analyzes data based on the user's basic information and medication information, and evaluates whether or not medications can be taken together and suggests the most appropriate medication. The generative AI model runs on a server.

[1288] Specific operation of the system

[1289] The system works as follows:

[1290] 1. User Registration

[1291] A user uses a mobile device to input their basic information (age, sex, height, weight, medical history, etc.) and sends it to the server, which then stores the received information in a database.

[1292] 2. Drug information registration

[1293] Pharmaceutical companies register drug information (drug name, ingredients, efficacy, side effects, etc.) through the management screen and send it to the server. The server stores the received drug information in a database.

[1294] 3. User medication information input

[1295] Users enter information about the medications they take regularly or are considering purchasing into their mobile device by scanning barcodes or manually entering it, and then send it to the server, which receives it and stores it in a database.

[1296] 4. Evaluation using generative AI models

[1297] The server acquires the user's basic information and medication information and passes it to the generative AI model. The generative AI model analyzes this information and evaluates whether the medications are compatible and suggests the most appropriate medications. The evaluation results are returned to the server and stored in a database.

[1298] 5. Providing real-time feedback

[1299] The server generates feedback using the evaluation results from the generative AI model and sends it to the mobile device, where the user can check the content of the feedback on the screen.

[1300] Specific examples

[1301] For example, if a user tries to purchase a particular supplement from a virtual store, the system works as follows:

[1302] 1. User registration: A 40-year-old man installs the app and enters his basic information.

[1303] 2. Drug information registration: Information on newly developed drugs by pharmaceutical companies is registered on the server.

[1304] 3. User medication information entry: The user uses a mobile device to scan the barcode of the medication they are considering purchasing.

[1305] 4. Evaluation by generative AI model: The server obtains the user's basic information and drug information and passes it to the generative AI model, which analyzes the data and performs an evaluation.

[1306] 5. Providing real-time feedback: The server generates feedback based on the evaluation results of the generative AI model and sends it to the user's mobile device. The user confirms the feedback, such as "There is no problem with this combination. It is safe to take."

[1307] Prompt Sentence Examples

[1308] When users use the app, they are prompted with:

[1309] "Check whether your regular medications interact with the medication you are considering purchasing. Scan the barcode or enter the name of the medication."

[1310] Prompt sentence to input to the generative AI model:

[1311] User basic information: Age 40, Gender Male, Weight 70kg, Medical history Hypertension

[1312] Drugs under consideration for purchase: Loxoprofen, current medications: Vitamin C, prescription drug Losartan

[1313] Please rate this combination and what works best for you."

[1314] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1315] Step 1:

[1316] User Registration

[1317] Input: The user uses a mobile device to input basic information such as gender, age, height, weight, and medical history.

[1318] Data processing: The terminal converts the input information into an appropriate format and sends it to the server, for example, packaging the data in JSON format.

[1319] Output: The server receives the information and stores it in a database, which is used as input data for a generative AI model in a later step.

[1320] Step 2:

[1321] Drug Information Registration

[1322] Input: The pharmaceutical company inputs drug information such as the drug name, ingredients, efficacy, and side effects through the management screen.

[1323] Data processing: The drug company's terminal sends the entered drug information to the server. The data is packaged with the drug's identifier.

[1324] Output: The server stores the received drug information in a database. The stored information is used by the generative AI model for evaluation.

[1325] Step 3:

[1326] User drug information input

[1327] Input: The user inputs information about medications they take regularly or are considering purchasing into their mobile device, for example by scanning the medication barcode or manually entering the medication name.

[1328] Data processing: The terminal sends the entered medication information to the server. At this time, the data is saved in association with the user ID.

[1329] Output: The server receives the information and stores it in a database, which is used as input data for the generative AI model to evaluate.

[1330] Step 4:

[1331] Evaluation by generative AI model

[1332] Input: The server retrieves the user's basic information and medication information from the database. The retrieved information includes the user's basic information (age, gender, medical history, etc.), existing medication information, and newly entered medication information.

[1333] Data processing and calculation: The server passes the acquired data to the generative AI model. The generative AI model analyzes the data and determines whether or not drugs can be taken together and suggests the most appropriate medication. For example, it evaluates safety by taking into account the ingredients of the drug and the user's health condition.

[1334] Output: The generative AI model creates an evaluation result and returns it to the server. The evaluation result includes specific advice and points to note.

[1335] Step 5:

[1336] Providing real-time feedback

[1337] Input: The server receives the evaluation results returned by the generative AI model.

[1338] Data processing: The server converts the evaluation results into feedback data to be provided to the user, for example, in a format that is easy for the user to understand.

[1339] Output: The feedback data is sent to the mobile device and displayed to the user. The user can check the feedback on the mobile device screen and safely select a medication.

[1340] Specific examples

[1341] If the user is a 40-year-old man and the drug he is considering purchasing is loxoprofen:

[1342] 1. Step 1: User enters basic information, which is sent to the server and stored in the database.

[1343] 2. Step 2: The pharmaceutical company registers the information about rofecoxib, sends it to the server, and stores it in a database.

[1344] 3. Step 3: The user scans the barcode of the loxoprofen, enters the information, and sends it to the server.

[1345] 4. Step 4: The server passes basic information and drug information to the generative AI model, which evaluates drug interactions.

[1346] 5. Step 5: The server converts the evaluation results into feedback and provides it to the user. The user confirms, "There is no problem with this combination. It is safe to take."

[1347] 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.

[1348] This invention is a system that combines AI and an emotion engine to evaluate drug interactions and make optimal suggestions based on the user's emotional state, so that users can take their medication safely and effectively.

[1349] System Overview

[1350] This system acquires basic information about the user and medication information, and the AI ​​analyzes this data. It also uses an emotion engine to recognize the user's emotions and adjusts the evaluation results and recommendations according to their emotional state. This allows it to provide optimal drug combinations and recommendations that take the user's emotions into account.

[1351] System Components

[1352] 1. Server

[1353] It stores and manages user and drug information, and works in conjunction with AI and emotion engines.

[1354] 2. Terminal

[1355] A device that allows users to enter basic information and medication information, including smartphones and computers.

[1356] 3. Generation AI

[1357] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[1358] 4. Emotion Engine

[1359] The system identifies the user's emotions based on facial recognition and voice data, and adjusts the evaluation results and suggestions accordingly.

[1360] 5. Pharmaceutical company data entry system

[1361] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[1362] Program processing flow

[1363] Program processing overview

[1364] This program performs processing in the following steps:

[1365] 1. User Registration

[1366] 2. Drug information registration

[1367] 3. User medication information input

[1368] 4. AI evaluation

[1369] 5. Emotion Recognition by Emotion Engine

[1370] 6. Adjusting the proposal

[1371] 7. Providing Feedback

[1372] Natural language explanation of program processing

[1373] 1. User Registration

[1374] Using the terminal, the user enters their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[1375] The server stores the received information in a database.

[1376] 2. Drug information registration

[1377] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[1378] The server stores the received drug information in a database.

[1379] 3. User medication information input

[1380] Using the device, users enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take daily, and the device then sends this information to a server.

[1381] The server stores the received drug information in a database.

[1382] 4. AI evaluation

[1383] The server obtains the user's basic information and medication information and passes this data to the generation AI.

[1384] The generative AI analyzes the data based on the information it receives, and suggests whether or not drugs can be taken together, as well as the best medications and supplements for specific symptoms.

[1385] The server receives the evaluation results from the AI ​​and stores them in a database.

[1386] 5. Emotion Recognition by Emotion Engine

[1387] The device collects the user's facial recognition data and voice data and sends it to the server.

[1388] The emotion engine uses this data to identify the user's emotions.

[1389] 6. Adjusting the proposal

[1390] The server adjusts the evaluation results and suggestions based on the emotional state from the emotion engine.

[1391] For example, if a user is feeling stressed, the system will suggest medications or supplements that have a relaxing effect.

[1392] 7. Providing Feedback

[1393] The server generates feedback to provide the adjusted evaluation results to the user.

[1394] Using the device, users can view feedback and use it as a reference for taking medication safely.

[1395] Specific examples

[1396] scenario

[1397] For example, if a user regularly takes over-the-counter vitamins and also takes a new prescription drug from a hospital, the system operates as follows.

[1398] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[1399] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[1400] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[1401] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which analyzes them and makes an evaluation.

[1402] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[1403] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[1404] 7. Providing feedback: The server provides the adjusted evaluation results to the user, and the user can check the feedback to take the medicine with peace of mind.

[1405] This system not only saves users the trouble of researching on their own and allows them to easily and quickly obtain information on safe drug combinations, but also provides optimal suggestions based on their emotional state.

[1406] The processing flow will be explained below.

[1407] Step 1:

[1408] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[1409] Step 2:

[1410] The server stores the received user information in a database.

[1411] Step 3:

[1412] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[1413] Step 4:

[1414] The server stores the received medication information in a database.

[1415] Step 5:

[1416] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[1417] Step 6:

[1418] The server stores the received medication information in a database.

[1419] Step 7:

[1420] The server obtains the user's basic information and medication information and passes it to the generation AI.

[1421] Step 8:

[1422] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[1423] Step 9:

[1424] The generation AI returns the evaluation results to the server.

[1425] Step 10:

[1426] The server receives the evaluation results from the AI ​​and stores them in a database.

[1427] Step 11:

[1428] The device collects the user's facial recognition data and voice data and sends it to the server.

[1429] Step 12:

[1430] The server passes facial recognition data and voice data to the emotion engine.

[1431] Step 13:

[1432] The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[1433] Step 14:

[1434] The server receives the emotion recognition results from the emotion engine and adjusts the evaluation results and suggestions. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[1435] Step 15:

[1436] The server generates feedback to provide the adjusted evaluation results to the user.

[1437] Step 16:

[1438] Users can use the device to check feedback and use it as a reference for taking medication safely.

[1439] Example 2

[1440] 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."

[1441] Conventional medication systems have made it difficult for users to obtain appropriate information for taking multiple medications safely and effectively. Furthermore, they do not take into account the user's emotional state, resulting in a poor user experience. The present invention aims to solve these problems and enable users to take their medications with peace of mind.

[1442] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information of the user and transmitting it to the server, a means for the provider to input pharmaceutical information and transmit it to the server, and a means for inputting information on drugs that the user takes on a daily basis and transmit it to the server. This makes it possible to manage the basic information of the user, pharmaceutical information, and information on individual drugs in an integrated manner, and to provide optimal drug combinations and suggestions by combining evaluations by a generative AI model and emotion recognition by an emotion engine.

[1443] A "user" is an individual who uses the system and enters their own basic information and information about the medications they are taking.

[1444] "Provider" refers to a corporation or organization that inputs pharmaceutical information, generally a drug manufacturer or medical institution.

[1445] "Basic information" refers to personal data such as the user's gender, age, height, weight, and symptoms.

[1446] "Pharmaceutical information" refers to detailed data such as the name of a drug, its ingredients, efficacy, and side effects.

[1447] "Pharmaceutical information" refers to information about medicines and supplements that users take on a daily basis.

[1448] A "server" refers to a computer system that stores and manages input information and analyzes it in cooperation with generative AI models and emotion engines.

[1449] A "generative AI model" refers to an artificial intelligence system that analyzes data based on the user's basic information and pharmaceutical information, and makes recommendations on optimal drug combinations.

[1450] An "emotion engine" is a system that identifies emotions based on a user's facial recognition data and voice data, and adjusts evaluation results and proposal content based on those results.

[1451] This invention is a drug combination evaluation system that combines a generative AI model and an emotion engine to help users take their medications safely and effectively. This system collects basic information, pharmacological information, and information on the drugs they regularly take, and analyzes this data to provide optimal drug combinations and recommendations.

[1452] System Components

[1453] 1. Server

[1454] Stores and manages basic and pharmaceutical information submitted by multiple users.

[1455] Data is shared with generative AI models and emotion engines.

[1456] Stores pharmaceutical information sent by the provider.

[1457] The evaluation results obtained from the generative AI model and the emotion evaluation results obtained from the emotion engine are stored in a database.

[1458] 2. Terminal

[1459] This includes devices such as smartphones and computers that allow users to enter basic and pharmaceutical information.

[1460] It has the ability to collect facial recognition data and voice data and send it to a server.

[1461] 3. Generative AI Models

[1462] The user's basic information and pharmaceutical information provided by the server are analyzed.

[1463] As a result of the analysis, the system makes recommendations for safe and effective drug combinations and optimal treatment for specific symptoms.

[1464] 4. Emotion Engine

[1465] The system identifies the user's emotions based on facial recognition data and voice data provided by the device.

[1466] Adjust the evaluation results and recommendations depending on your emotional state.

[1467] 5. Pharmaceutical Data Entry System

[1468] It provides a mechanism for providers to input pharmaceutical information and send it to the server.

[1469] System operation example

[1470] An example of the operation of this system is shown below.

[1471] 1. User Registration

[1472] The user enters their basic information (gender, age, height, weight, symptoms, etc.) using a smartphone, and the device sends this information to a server, which then stores the received information in a database.

[1473] 2. Drug information registration

[1474] Providers use the management screen to enter their company's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server, which then stores the received pharmaceutical information in a database.

[1475] 3. User medication information input

[1476] The user enters information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis, and the device sends this information to the server, which then stores the received information in a database.

[1477] 4. AI evaluation

[1478] The server acquires the user's basic information and pharmaceutical information and passes that data to a generative AI model. The generative AI model analyzes the data and suggests drug interactions and the best medications and supplements for specific symptoms. The server receives the evaluation results from the AI ​​and stores them in a database.

[1479] 5. Emotion Recognition by Emotion Engine

[1480] The device collects the user's facial recognition data and voice data and sends it to the server. The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[1481] 6. Adjusting the proposal

[1482] The server adjusts the evaluation results and recommendations of the generative AI model based on the results of the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[1483] Specific examples

[1484] A specific example of the system is shown below: When a user regularly takes over-the-counter vitamins and also takes a new medicine prescribed by a hospital, the system operates as follows.

[1485] The user enters basic information on their smartphone and sends it to the server.

[1486] The provider registers pharmaceutical information on the server.

[1487] The user enters information about the over-the-counter and prescription medications they regularly take and sends it to the server.

[1488] The server passes information to the generative AI model, which analyzes it and returns the evaluation results.

[1489] The terminal collects the user's emotional state and the emotion engine analyzes it.

[1490] The server adjusts the evaluation results and proposals based on the results of the emotion engine.

[1491] It provides users with tailored recommendations to help them take their medication safely and effectively.

[1492] Prompt Sentence Examples

[1493] The following prompt is given to the generative AI model:

[1494] The basic information of the user is as follows:

[1495] Gender: Male

[1496] Age: 35

[1497] Height: 175cm

[1498] Weight: 70kg

[1499] Symptoms: High blood pressure

[1500] Information about medications you are currently taking:

[1501] Over-the-counter medication: Vitamin C supplements

[1502] Prescription medication: Atenolol (hypertension medication)

[1503] The user's emotional state is as follows:

[1504] Stress level: High

[1505] Emotion: Anxiety

[1506] Based on the above information, please suggest the best medication and combination.

[1507] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1508] Step 1:

[1509] The user uses a smartphone or computer to enter their basic information (gender, age, height, weight, symptoms, etc.). The information is checked in real time by the device to ensure there are no omissions or errors. The confirmed information is sent from the device to a server, where it is stored in a database.

[1510] Input: User's basic information (gender, age, height, weight, symptoms, etc.)

[1511] Output: Basic information data stored on the server

[1512] Specific operation: The user enters information such as age and weight into the application form on their smartphone and presses the "Submit" button. The device then sends the information to the server, which then stores it in a database.

[1513] Step 2:

[1514] The provider enters pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) through a dedicated management screen. The entered information is sent by the terminal to the server, where it is stored in a database.

[1515] Input: Provider's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.)

[1516] Output: Pharmaceutical information data stored on the server

[1517] Specific operation: The provider enters information about the new drug on the management screen and presses the "Register" button. The terminal sends the information to the server, which stores it in a database.

[1518] Step 3:

[1519] Users use their smartphones or computers to enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take on a daily basis. The device checks the entered information in real time to ensure there are no omissions or errors. The confirmed information is sent to a server, which then stores it in a database.

[1520] Input: User's medication information (over-the-counter drugs, vitamins, herbal medicines, prescription drugs)

[1521] Output: Medication information data stored on the server

[1522] Specific operation: The user enters information about over-the-counter or prescription drugs into the application form and presses the "Submit" button. The device sends the information to the server, which stores it in a database.

[1523] Step 4:

[1524] The server acquires the user's stored basic information, pharmaceutical information, and medication information, and passes this data to the generative AI model. The generative AI model analyzes this data and suggests drug interactions and the best medications and supplements for specific symptoms. The evaluation results generated by the generative AI model are returned to the server, where they are stored in a database.

[1525] Input: Basic information, pharmaceutical information, medication information

[1526] Output: Evaluation results by generative AI model

[1527] How it works: The server retrieves the user's basic information, pharmaceutical information, and medication information from the database and inputs this information into the generative AI model. The generative AI model analyzes the data and generates an evaluation result that says, "There is no problem with taking vitamin C and atenolol together, but be careful of interactions with food," and returns this result to the server.

[1528] Step 5:

[1529] The user collects facial recognition data and voice data through a device (such as a smartphone), and this data is sent to a server. The server passes this data to an emotion engine, which identifies the user's emotion. The results generated by the emotion engine are returned to the server, where they are stored in a database.

[1530] Input: Face recognition data, voice data

[1531] Output: Emotion evaluation results by the emotion engine

[1532] Specific operation: The user turns their face towards the device camera, and the emotion engine determines that the user is currently feeling stressed. The result is sent to the server and stored in a database.

[1533] Step 6:

[1534] The server adjusts the generative AI model's evaluation results and suggestions based on the emotion evaluation results from the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect. The adjusted evaluation results and suggestions are stored in a database.

[1535] Input: Emotion evaluation results, evaluation results of generative AI model

[1536] Output: Adjusted proposal

[1537] Specific operation: If the emotion engine identifies that the user is feeling stressed, the server changes the evaluation result of the generative AI model to suggest a supplement that has a relaxing effect.

[1538] Step 7:

[1539] The server generates feedback to provide the user with the adjusted evaluation results. The device displays this feedback to the user, allowing the user to confirm the feedback. This allows the user to take their medication safely and effectively based on the provided information.

[1540] Input: Adjusted proposal

[1541] Output: Feedback provided to the user

[1542] Specific operation: The server generates feedback such as "The user has high blood pressure and stress, so we recommend that you also take supplements that are expected to have a relaxing effect," and sends it to the device. The user checks the feedback on their smartphone and presses the "I understand" button.

[1543] (Application example 2)

[1544] 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."

[1545] Conventional medication systems have the problem of being unable to consider the user's emotional state when evaluating interactions between multiple medications taken by the user, making it difficult to provide optimal recommendations for the user. Furthermore, simply providing safe drug combinations based on information entered by the user often results in insufficient recommendations. Therefore, there is a need for a system that can provide appropriate recommendations while taking the user's emotional state into account.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1547] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the AI, means for the AI ​​to make an evaluation based on the user's basic information and drug information and return it to the server, means for the server to provide the user with the evaluation results from the AI, means for the terminal to collect the user's facial recognition data and voice data and for the emotion engine to identify emotions based on those, and means for the server to adjust the evaluation results and proposal content based on the emotional state from the emotion engine. This makes it possible to provide safe and effective drug combinations and optimal proposals while taking the user's emotional state into consideration.

[1548] "Basic information about the user" refers to the user's gender, age, height, weight, symptoms, and other information related to personal identification.

[1549] "Server" means a centralized computer system that receives, stores, and processes data submitted by users and pharmaceutical companies.

[1550] "Drug information" refers to the name of the drug, its ingredients, effects, side effects, and other details related to the drug.

[1551] "Information about medications taken by the user on a daily basis" refers to information about medications, vitamins, supplements, and herbal medicines that the user takes on a daily basis.

[1552] "AI" is an artificial intelligence system that analyzes data based on the user's basic information and drug information, and evaluates drug interactions.

[1553] The "evaluation results" are information obtained as a result of the AI's analysis, such as whether or not drugs can be taken together and suggestions for the most appropriate drugs and supplements.

[1554] A "terminal" is a device that users use to input basic information and medication information, such as a smartphone or computer.

[1555] "Facial recognition data" is feature data acquired based on a facial image of a user.

[1556] "Voice data" is characteristic data obtained by recording the user's voice.

[1557] The "Emotion Engine" is a system that identifies a user's emotional state based on facial recognition data and voice data.

[1558] "Adjusting the proposal content" means changing the AI's evaluation results and proposal content based on the user's emotional state identified by the emotion engine.

[1559] The present invention is a system that combines AI and an emotion engine to evaluate drug interactions and provide optimal recommendations based on the user's emotional state, so that users can take their medication safely and effectively. Specific embodiments of this system are described in detail below.

[1560] System Configuration

[1561] The system consists of the following main components:

[1562] 1. Server

[1563] 2. Device (smartphone, PC, etc.)

[1564] 3. AI (Generation AI)

[1565] 4. Emotion Engine

[1566] 5. Database

[1567] Hardware and software used

[1568] Hardware: Smartphone camera (for facial recognition), microphone (for voice data acquisition), server (for data storage and processing)

[1569] Software: OpenCV (face recognition), DeepFace (emotion analysis), requests (API communication), RESTful API server (combination evaluation)

[1570] Processing Description

[1571] Enter user and medication information

[1572] Users use a device such as a smartphone to enter their basic information (gender, age, height, weight, symptoms, etc.). The entered basic information is sent from the device to a server and stored in a database. Pharmaceutical companies use a management screen to enter their own drug information (drug name, ingredients, efficacy, side effects, etc.) and similarly send this to the server. This aggregates information about users and drugs.

[1573] Drug interaction evaluation

[1574] The server acquires the user's basic information and medication information and sends it to the generation AI. The generation AI evaluates drug interactions based on this information and returns the evaluation results to the server. The evaluation results are stored in a database.

[1575] emotion recognition

[1576] The user's facial recognition data and voice data are collected using the smartphone's camera and microphone. This data is sent to a server and analyzed by the emotion engine, which uses DeepFace to identify emotions from facial images and also analyzes voice data.

[1577] Adjusting the proposal

[1578] The server adjusts the evaluation results and recommendations based on the emotional state identified by the emotion engine. For example, if the user is feeling stressed, it will suggest medication or supplements that have a relaxing effect. The adjusted evaluation results are provided to the user, who can view them on their device as feedback.

[1579] Specific examples

[1580] For example, if a user is taking a new prescription medication while also taking over-the-counter vitamins, the system would operate as follows:

[1581] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[1582] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[1583] 3. Enter user medication information: Enter information about over-the-counter medications, vitamins, and prescription medications that users regularly take.

[1584] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which then analyzes them and makes an evaluation.

[1585] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[1586] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[1587] 7. Providing feedback: The server provides the adjusted evaluation results to the user, who can then check the feedback to feel more comfortable taking their medication.

[1588] Prompt Sentence Examples

[1589] "Based on the information entered by the user, the generative AI model evaluates drug interactions. The DeepFace library is used to recognize emotions from the user's facial image, and the emotional results are reflected in the evaluation results."

[1590] In this way, this system can suggest safe and effective drug combinations that take into account the user's emotional state.

[1591] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1592] Step 1:

[1593] Entering user information

[1594] Users use devices such as smartphones to input their basic information (gender, age, height, weight, symptoms, etc.). This basic information is sent to the server by the device. The input data is then saved in the server's database.

[1595] Step 2:

[1596] Entering drug information

[1597] Pharmaceutical companies use the management screen to input their drug information (drug name, ingredients, efficacy, side effects, etc.). This information is sent from the management screen to the server, where it is stored in the server's database.

[1598] Step 3:

[1599] Entering information about medications currently being used

[1600] Users use a device such as a smartphone to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis. This medication information is sent from the device to a server, where it is stored in the server's database.

[1601] Step 4:

[1602] Request for drug combination evaluation

[1603] The server retrieves the user's basic information and medication information from the database and sends this data to the generation AI. The AI ​​evaluates drug interactions based on the received data and returns the results to the server. The evaluation results include whether the drug interaction is appropriate and the risks of inappropriate drug interactions.

[1604] Step 5:

[1605] Emotion recognition data collection

[1606] Users use their smartphone's camera and microphone to collect facial recognition and voice data, which is then sent from the device to a server, which then passes the data to the emotion engine.

[1607] Step 6:

[1608] Emotional state analysis

[1609] The facial recognition data and voice data sent to the server are analyzed by an emotion engine (DeepFace) to identify the user's emotional state. The emotion engine determines the user's emotions (e.g., stress, anxiety, joy, etc.) based on the acquired data.

[1610] Step 7:

[1611] Adjustment of evaluation results

[1612] The server adjusts the AI's drug combination assessment results and recommendations based on the emotional state from the emotion engine. For example, if the user is feeling stressed, it will suggest medications or supplements with a relaxing effect. The adjusted results are the final feedback provided to the user.

[1613] Step 8:

[1614] Providing Feedback

[1615] The server returns the adjusted evaluation results to the device, and the user can check this feedback on their smartphone, etc. The feedback includes recommendations on the safety of drug interactions and the optimal medications and supplements based on their emotional state.

[1616] In this way, the system of the present invention processes the data entered at each step, provides safe and effective drug combinations, and makes optimal suggestions based on the user's emotional state.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] [Fourth embodiment]

[1621] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1622] 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.

[1623] 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).

[1624] 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.

[1625] 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.

[1626] 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).

[1627] 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. 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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."

[1634] This invention is a system that uses AI to evaluate interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations to help users take their medication safely and effectively.

[1635] System Overview

[1636] This system collects basic information about the user and medication information, and the AI ​​analyzes that data to provide feedback on whether medications can be taken together and what the optimal combination is. To achieve this, the system includes the following components:

[1637] 1. Server

[1638] It stores and manages user and drug information and works in conjunction with AI.

[1639] 2. Terminal

[1640] A device used to send basic information and medication information entered by users to a server, including smartphones and PCs.

[1641] 3. Generation AI

[1642] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[1643] 4. Pharmaceutical company data entry system

[1644] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[1645] Program processing flow

[1646] Program processing overview

[1647] This program performs processing in the following steps:

[1648] 1. User Registration

[1649] 2. Drug information registration

[1650] 3. User medication information input

[1651] 4. AI evaluation

[1652] 5. Providing Feedback

[1653] Natural language explanation of program processing

[1654] 1. User Registration

[1655] Using the terminal, the user inputs their basic information (gender, age, height, weight, symptoms, etc.), and the terminal then transmits the input information to the server.

[1656] The server stores the received information in a database.

[1657] 2. Drug information registration

[1658] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[1659] The server stores the received drug information in a database.

[1660] 3. User medication information input

[1661] Using the terminal, users enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily, and the terminal then sends the entered information to the server.

[1662] The server stores the received drug information in a database.

[1663] 4. AI evaluation

[1664] The server obtains the user's basic information and medication information and passes this data to the AI.

[1665] The generative AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medications or supplements for specific symptoms.

[1666] The server receives the evaluation results from the AI ​​and stores them in a database.

[1667] 5. Providing Feedback

[1668] The server generates feedback to provide the evaluation results to the user.

[1669] Using the terminal, the user checks the feedback.

[1670] Specific examples

[1671] scenario

[1672] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication from a hospital, the system operates as follows:

[1673] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[1674] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[1675] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[1676] 4. Evaluation by AI: The server passes the user's basic information and medication information to the AI, which analyzes them and makes an evaluation.

[1677] 5. Providing feedback: The server provides the user with the evaluation results from the AI, and the user can check the feedback to take the medicine with peace of mind.

[1678] This system allows users to easily and quickly obtain information on safe drug interactions, eliminating the need to research on their own.

[1679] The processing flow will be explained below.

[1680] Step 1:

[1681] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[1682] Step 2:

[1683] The server stores the received user information in a database.

[1684] Step 3:

[1685] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[1686] Step 4:

[1687] The server stores the received medication information in a database.

[1688] Step 5:

[1689] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[1690] Step 6:

[1691] The server stores the received medication information in a database.

[1692] Step 7:

[1693] The server obtains the user's basic information and medication information and passes it to the generation AI.

[1694] Step 8:

[1695] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[1696] Step 9:

[1697] The generation AI returns the evaluation results to the server.

[1698] Step 10:

[1699] The server receives the evaluation results from the AI ​​and stores them in a database.

[1700] Step 11:

[1701] The server generates feedback to provide the evaluation results to the user.

[1702] Step 12:

[1703] Users can use the device to check feedback and use it as a reference for taking medication safely.

[1704] Example 1

[1705] 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."

[1706] In today's world, it is common for people to take multiple medications and supplements at the same time, but it is difficult to predict the side effects and diminished effectiveness of these medications. It is also difficult for users to determine for themselves whether the combination of medications prescribed by a medical institution, over-the-counter medications, and supplements is appropriate. Conventional methods cannot make such judgments quickly and accurately, potentially hindering the safe and effective use of medications.

[1707] 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.

[1708] In this invention, the server includes means for inputting basic information about the user and transmitting it to the server, means for the pharmaceutical company to input drug information and transmit it to the server, means for inputting information about drugs the user takes on a daily basis and transmitting it to the server, means for passing data to the generated AI, means for the generated AI to return evaluation results, and means for providing the evaluation results to the user. This allows the user to easily and quickly obtain information about safe drug interactions without having to do the effort of researching them themselves.

[1709] "Basic information of the user" refers to basic information about the user, such as gender, age, height, weight, symptoms, etc.

[1710] "Drug information" refers to detailed information about drugs, such as drug name, ingredients, efficacy, and side effects.

[1711] "Server" refers to a computer system that receives and stores input data, passes the data to the generated artificial intelligence as needed, and returns the evaluation results.

[1712] "Generated artificial intelligence" refers to an algorithm or system that analyzes the user's basic information and drug information provided by the server, and evaluates whether or not drugs can be taken together and what the optimal combination is.

[1713] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[1714] "Evaluation results" refer to the results of the analysis conducted by the generated artificial intelligence, such as whether or not a drug can be taken with other drugs, and suggestions for the most appropriate medications and supplements.

[1715] "Feedback" refers to information that is presented to the user as evaluation results and helps the user understand the content.

[1716] "Daily medication information" refers to information on over-the-counter medications, vitamins, herbal medicines, and medications prescribed by hospitals that users take daily.

[1717] This invention is a system that uses generated artificial intelligence to evaluate the interactions between drugs prescribed by a hospital and over-the-counter drugs, vitamins, herbal medicines, etc., and suggests optimal combinations so that users can take their medicines safely and effectively.

[1718] System configuration

[1719] The system includes the following components:

[1720] 1. Server

[1721] The server stores and manages user information and drug information, and interacts with the generated artificial intelligence.

[1722] 2. Terminal

[1723] The terminal is a device used to send basic information and medication information entered by the user to the server, and includes smartphones, PCs, etc.

[1724] 3. Generated Artificial Intelligence

[1725] The generated artificial intelligence analyzes data based on the user's basic information and medication information, and evaluates the optimal drug combination.

[1726] 4. Pharmaceutical company data entry system

[1727] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[1728] Program processing overview

[1729] The program follows these steps:

[1730] 1. User Registration

[1731] The user uses the terminal to input their basic information (gender, age, height, weight, symptoms, etc.) and transmits the information to the server.

[1732] The server stores the received information in a database.

[1733] 2. Drug information registration

[1734] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[1735] The server stores the received drug information in a database.

[1736] 3. User medication information input

[1737] The user uses a terminal to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines that he or she takes on a daily basis, and sends the information to the server.

[1738] The server stores the received drug information in a database.

[1739] 4. Evaluation by generated artificial intelligence

[1740] The server obtains the user's basic information and medication information and passes this data to the generated artificial intelligence.

[1741] The generated AI analyzes and evaluates the data based on the information it receives, specifically determining whether or not drugs can be taken together and suggesting the best medicines or supplements for specific symptoms.

[1742] The server receives the evaluation results from the generated artificial intelligence and stores them in a database.

[1743] 5. Providing Feedback

[1744] The server generates feedback to provide the evaluation results to the user.

[1745] The user uses the terminal to check the feedback.

[1746] Specific examples

[1747] For example, if a user regularly takes over-the-counter vitamins and then takes a new prescription medication, the system works as follows:

[1748] 1. User Registration:

[1749] The user enters basic information (gender: female, age: 45, weight: 60 kg, symptoms: chronic headache) and sends it to the server.

[1750] 2. Drug Information Registration:

[1751] A pharmaceutical company registers information about a newly developed drug (drug name: PainEase, ingredients: ibuprofen, efficacy: pain relief, side effect: stomach discomfort) on a server.

[1752] 3. User medication information input:

[1753] The user enters information about the vitamins they regularly take (vitamin D), over-the-counter medications (allergy medication "AllergyFix"), and any new medications they have been prescribed by their doctor ("PainEase").

[1754] 4. Evaluation by generated artificial intelligence:

[1755] The server passes the user's basic information and medication information to the generated AI, which then analyzes and evaluates them.

[1756] For example, evaluation results show that Vitamin D and AllergyFix are safe, but combining them with PainEase may increase stomach discomfort.

[1757] 5. Providing Feedback:

[1758] The server provides the evaluation results to the user, who then checks the feedback in a dedicated app to understand the timing and precautions for taking painkillers.

[1759] Prompt Sentence Examples

[1760] "Based on the following drug information, please evaluate the safe interactions between over-the-counter medications and medications prescribed by your doctor.

[1761] Basic information: Gender: Female Age: 45 Weight: 60kg

[1762] Symptoms: Chronic headache

[1763] Over-the-counter medications: Vitamin D supplements, allergy medications

[1764] Prescription Drugs: Newly Prescribed Pain Reliever Medications"

[1765] By inputting this prompt sentence into the generated artificial intelligence, a specific drug combination evaluation can be performed.

[1766] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1767] Step 1:

[1768] User registration

[1769] The user inputs basic information such as gender, age, height, weight, symptoms, etc. using a terminal. For example, the user inputs the required information into a form on a dedicated application or website.

[1770] The terminal encodes the input information and transmits it to the server using a secure communication protocol (for example, HTTPS).

[1771] The server stores the received information in a database. Specifically, the server analyzes the received data and stores it in the database as a new record.

[1772] Input: Basic information such as gender, age, height, weight, symptoms, etc.

[1773] Output: A record of basic information stored in the database

[1774] Step 2:

[1775] Drug Information Registration

[1776] Pharmaceutical companies use the management screen to enter information about newly developed drugs (drug name, ingredients, efficacy, side effects, etc.) and send it to the server. For example, a pharmaceutical company employee enters detailed drug information on the management screen and presses the send button.

[1777] The server stores the received drug information in a database. Specifically, the server analyzes the received data and adds the information to the corresponding table in the database.

[1778] Input: Drug information such as drug name, ingredients, efficacy, side effects, etc.

[1779] Output: Medication records stored in a database

[1780] Step 3:

[1781] User drug information input

[1782] Users use their devices to enter information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take daily. For example, users can fill out a form on a dedicated application or website to enter this information.

[1783] The terminal encodes the input information and transmits it to the server.

[1784] The server stores the received drug information in a database. For example, the server analyzes the received data and stores it in a drug information table in the database.

[1785] Input: Information on over-the-counter medicines, vitamins, herbal medicines, and prescription medicines

[1786] Output: User's medication record stored in database

[1787] Step 4:

[1788] Evaluation by generated artificial intelligence

[1789] The server obtains the user's basic information and medication information and passes that data to the generated AI. Specifically, the server queries the relevant basic information and medication information from the database and sends a request to the generated AI's API.

[1790] The generated AI analyzes the data based on the received information and suggests drug interactions and the best medicines or supplements for specific symptoms. For example, the generated AI model analyzes the request and makes an evaluation using its internal algorithm.

[1791] The server receives the evaluation results from the generated artificial intelligence and stores them in a database. Specifically, the server receives the evaluation results returned from the generated artificial intelligence and stores them in a database in an appropriate format.

[1792] Input: Basic information and medication information

[1793] Output: Evaluation results generated by AI

[1794] Step 5:

[1795] Providing Feedback

[1796] The server generates feedback to provide the evaluation results to the user. Specifically, the server organizes the evaluation results and converts them into a report format that is easy for the user to understand.

[1797] The user uses the device to review the feedback, for example, the user opens an application or website and reviews the feedback provided.

[1798] Input: Evaluation results generated by AI

[1799] Output: Feedback provided to the user

[1800] (Application example 1)

[1801] 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."

[1802] When users purchase medicine, it is difficult to confirm whether it is safe to take it with other medicines or supplements they are already taking. In addition, there is no easy way for users considering purchasing medicine to understand interactions, making it difficult to select a safe and effective medicine.

[1803] 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.

[1804] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for inputting information on drugs the user is considering purchasing and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the generative AI model, means for the generative AI model to evaluate the user's basic information and drug information based on the user's basic information and drug information and return it to the server, and means for the server to provide the evaluation result from the generative AI model to the user. This allows the user to check in real time the interactions between the drug they are considering purchasing and drugs they are already taking and safely select a drug.

[1805] "Basic information" refers to personal information such as the user's age, gender, height, weight, medical history, etc.

[1806] "Drug information" refers to detailed data about drugs, such as drug name, ingredients, efficacy, and side effects.

[1807] A "generative AI model" is an artificial intelligence model that analyzes and evaluates based on the user's basic information and drug information.

[1808] "Mobile device" refers to a computing device that can be carried around and used, such as a smartphone or tablet.

[1809] "Barcode scanning" refers to the act or means of reading the barcode attached to a product using the camera function of a device, etc.

[1810] "Server" refers to a computer system that processes, stores, and provides information over a network.

[1811] "Feedback" refers to information and advice provided to users based on the evaluation results of the generative AI model.

[1812] "Pharmaceutical company" refers to a legal entity that manufactures and sells drugs.

[1813] "Drug interactions" refers to the interactions and safety of taking multiple medications or supplements at the same time.

[1814] This invention relates to a system for virtual stores that allows users to check in real time the interactions between drugs and supplements they are considering purchasing. This system uses the user's basic information and drug information to perform an evaluation using a generative AI model, and provides the evaluation results as feedback.

[1815] System Components

[1816] 1. Server

[1817] The server stores and manages basic user information and medication information, and links it with the generative AI model. SQL Server or similar can be used as the database.

[1818] 2. Mobile devices

[1819] A device that allows users to input and send their basic information and medication information. This applies to mobile devices such as smartphones and tablets.

[1820] 3. Generative AI Models

[1821] This is an artificial intelligence model that analyzes data based on the user's basic information and medication information, and evaluates whether or not medications can be taken together and suggests the most appropriate medication. The generative AI model runs on a server.

[1822] Specific operation of the system

[1823] The system works as follows:

[1824] 1. User Registration

[1825] A user uses a mobile device to input their basic information (age, sex, height, weight, medical history, etc.) and sends it to the server, which then stores the received information in a database.

[1826] 2. Drug information registration

[1827] Pharmaceutical companies register drug information (drug name, ingredients, efficacy, side effects, etc.) through the management screen and send it to the server. The server stores the received drug information in a database.

[1828] 3. User medication information input

[1829] Users enter information about the medications they take regularly or are considering purchasing into their mobile device by scanning barcodes or manually entering it, and then send it to the server, which receives it and stores it in a database.

[1830] 4. Evaluation using generative AI models

[1831] The server acquires the user's basic information and medication information and passes it to the generative AI model. The generative AI model analyzes this information and evaluates whether the medications are compatible and suggests the most appropriate medications. The evaluation results are returned to the server and stored in a database.

[1832] 5. Providing real-time feedback

[1833] The server generates feedback using the evaluation results from the generative AI model and sends it to the mobile device, where the user can check the content of the feedback on the screen.

[1834] Specific examples

[1835] For example, if a user tries to purchase a particular supplement from a virtual store, the system works as follows:

[1836] 1. User registration: A 40-year-old man installs the app and enters his basic information.

[1837] 2. Drug information registration: Information on newly developed drugs by pharmaceutical companies is registered on the server.

[1838] 3. User medication information entry: The user uses a mobile device to scan the barcode of the medication they are considering purchasing.

[1839] 4. Evaluation by generative AI model: The server obtains the user's basic information and drug information and passes it to the generative AI model, which analyzes the data and performs an evaluation.

[1840] 5. Providing real-time feedback: The server generates feedback based on the evaluation results of the generative AI model and sends it to the user's mobile device. The user confirms the feedback, such as "There is no problem with this combination. It is safe to take."

[1841] Prompt Sentence Examples

[1842] When users use the app, they are prompted with:

[1843] "Check whether your regular medications interact with the medication you are considering purchasing. Scan the barcode or enter the name of the medication."

[1844] Prompt sentence to input to the generative AI model:

[1845] User basic information: Age 40, Gender Male, Weight 70kg, Medical history Hypertension

[1846] Drugs under consideration for purchase: Loxoprofen, current medications: Vitamin C, prescription drug Losartan

[1847] Please rate this combination and what works best for you."

[1848] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1849] Step 1:

[1850] User Registration

[1851] Input: The user uses a mobile device to input basic information such as gender, age, height, weight, and medical history.

[1852] Data processing: The terminal converts the input information into an appropriate format and sends it to the server, for example, packaging the data in JSON format.

[1853] Output: The server receives the information and stores it in a database, which is used as input data for a generative AI model in a later step.

[1854] Step 2:

[1855] Drug Information Registration

[1856] Input: The pharmaceutical company inputs drug information such as the drug name, ingredients, efficacy, and side effects through the management screen.

[1857] Data processing: The drug company's terminal sends the entered drug information to the server. The data is packaged with the drug's identifier.

[1858] Output: The server stores the received drug information in a database. The stored information is used by the generative AI model for evaluation.

[1859] Step 3:

[1860] User drug information input

[1861] Input: The user inputs information about medications they take regularly or are considering purchasing into their mobile device, for example by scanning the medication barcode or manually entering the medication name.

[1862] Data processing: The terminal sends the entered medication information to the server. At this time, the data is saved in association with the user ID.

[1863] Output: The server receives the information and stores it in a database, which is used as input data for the generative AI model to evaluate.

[1864] Step 4:

[1865] Evaluation by generative AI model

[1866] Input: The server retrieves the user's basic information and medication information from the database. The retrieved information includes the user's basic information (age, gender, medical history, etc.), existing medication information, and newly entered medication information.

[1867] Data processing and calculation: The server passes the acquired data to the generative AI model. The generative AI model analyzes the data and determines whether or not drugs can be taken together and suggests the most appropriate medication. For example, it evaluates safety by taking into account the ingredients of the drug and the user's health condition.

[1868] Output: The generative AI model creates an evaluation result and returns it to the server. The evaluation result includes specific advice and points to note.

[1869] Step 5:

[1870] Providing real-time feedback

[1871] Input: The server receives the evaluation results returned by the generative AI model.

[1872] Data processing: The server converts the evaluation results into feedback data to be provided to the user, for example, in a format that is easy for the user to understand.

[1873] Output: The feedback data is sent to the mobile device and displayed to the user. The user can check the feedback on the mobile device screen and safely select a medication.

[1874] Specific examples

[1875] If the user is a 40-year-old man and the drug he is considering purchasing is loxoprofen:

[1876] 1. Step 1: User enters basic information, which is sent to the server and stored in the database.

[1877] 2. Step 2: The pharmaceutical company registers the information about rofecoxib, sends it to the server, and stores it in a database.

[1878] 3. Step 3: The user scans the barcode of the loxoprofen, enters the information, and sends it to the server.

[1879] 4. Step 4: The server passes basic information and drug information to the generative AI model, which evaluates drug interactions.

[1880] 5. Step 5: The server converts the evaluation results into feedback and provides it to the user. The user confirms, "There is no problem with this combination. It is safe to take."

[1881] 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.

[1882] This invention is a system that combines AI and an emotion engine to evaluate drug interactions and make optimal suggestions based on the user's emotional state, so that users can take their medication safely and effectively.

[1883] System Overview

[1884] This system acquires basic information about the user and medication information, and the AI ​​analyzes this data. It also uses an emotion engine to recognize the user's emotions and adjusts the evaluation results and recommendations according to their emotional state. This allows it to provide optimal drug combinations and recommendations that take the user's emotions into account.

[1885] System Components

[1886] 1. Server

[1887] It stores and manages user and drug information, and works in conjunction with AI and emotion engines.

[1888] 2. Terminal

[1889] A device that allows users to enter basic information and medication information, including smartphones and computers.

[1890] 3. Generation AI

[1891] Data is analyzed based on the user's basic information and medication information to evaluate the optimal drug combination.

[1892] 4. Emotion Engine

[1893] The system identifies the user's emotions based on facial recognition and voice data, and adjusts the evaluation results and suggestions accordingly.

[1894] 5. Pharmaceutical company data entry system

[1895] A system that allows pharmaceutical companies to enter their own drug information and send it to a server.

[1896] Program processing flow

[1897] Program processing overview

[1898] This program performs processing in the following steps:

[1899] 1. User Registration

[1900] 2. Drug information registration

[1901] 3. User medication information input

[1902] 4. AI evaluation

[1903] 5. Emotion Recognition by Emotion Engine

[1904] 6. Adjusting the proposal

[1905] 7. Providing Feedback

[1906] Natural language explanation of program processing

[1907] 1. User Registration

[1908] Using the terminal, the user enters their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[1909] The server stores the received information in a database.

[1910] 2. Drug information registration

[1911] Pharmaceutical companies use the management screen to enter their drug information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server.

[1912] The server stores the received drug information in a database.

[1913] 3. User medication information input

[1914] Using the device, users enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take daily, and the device then sends this information to a server.

[1915] The server stores the received drug information in a database.

[1916] 4. AI evaluation

[1917] The server obtains the user's basic information and medication information and passes this data to the generation AI.

[1918] The generative AI analyzes the data based on the information it receives, and suggests whether or not drugs can be taken together, as well as the best medications and supplements for specific symptoms.

[1919] The server receives the evaluation results from the AI ​​and stores them in a database.

[1920] 5. Emotion Recognition by Emotion Engine

[1921] The device collects the user's facial recognition data and voice data and sends it to the server.

[1922] The emotion engine uses this data to identify the user's emotions.

[1923] 6. Adjusting the proposal

[1924] The server adjusts the evaluation results and suggestions based on the emotional state from the emotion engine.

[1925] For example, if a user is feeling stressed, the system will suggest medications or supplements that have a relaxing effect.

[1926] 7. Providing Feedback

[1927] The server generates feedback to provide the adjusted evaluation results to the user.

[1928] Using the device, users can view feedback and use it as a reference for taking medication safely.

[1929] Specific examples

[1930] scenario

[1931] For example, if a user regularly takes over-the-counter vitamins and also takes a new prescription drug from a hospital, the system operates as follows.

[1932] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[1933] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[1934] 3. Entering user's medication information: The user enters information about over-the-counter medications, vitamins, and prescription medications from the hospital that they regularly take.

[1935] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which analyzes them and makes an evaluation.

[1936] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[1937] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[1938] 7. Providing feedback: The server provides the adjusted evaluation results to the user, and the user can check the feedback to take the medicine with peace of mind.

[1939] This system not only saves users the trouble of researching on their own and allows them to easily and quickly obtain information on safe drug combinations, but also provides optimal suggestions based on their emotional state.

[1940] The processing flow will be explained below.

[1941] Step 1:

[1942] The user uses a device such as a smartphone or PC to enter their basic information (gender, age, height, weight, symptoms, etc.), which is then sent to the server.

[1943] Step 2:

[1944] The server stores the received user information in a database.

[1945] Step 3:

[1946] Pharmaceutical companies enter their drug information (drug name, ingredients, efficacy, side effects, etc.) into the management screen. After input, the pharmaceutical company sends this information to the server.

[1947] Step 4:

[1948] The server stores the received medication information in a database.

[1949] Step 5:

[1950] The user inputs information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis into the terminal, which then sends this information to the server.

[1951] Step 6:

[1952] The server stores the received medication information in a database.

[1953] Step 7:

[1954] The server obtains the user's basic information and medication information and passes it to the generation AI.

[1955] Step 8:

[1956] The AI ​​analyzes and evaluates the user's basic information and medication information, specifically suggesting whether medications can be taken together and which medications or supplements are best suited to specific symptoms.

[1957] Step 9:

[1958] The generation AI returns the evaluation results to the server.

[1959] Step 10:

[1960] The server receives the evaluation results from the AI ​​and stores them in a database.

[1961] Step 11:

[1962] The device collects the user's facial recognition data and voice data and sends it to the server.

[1963] Step 12:

[1964] The server passes facial recognition data and voice data to the emotion engine.

[1965] Step 13:

[1966] The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[1967] Step 14:

[1968] The server receives the emotion recognition results from the emotion engine and adjusts the evaluation results and suggestions. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[1969] Step 15:

[1970] The server generates feedback to provide the adjusted evaluation results to the user.

[1971] Step 16:

[1972] Users can use the device to check feedback and use it as a reference for taking medication safely.

[1973] Example 2

[1974] 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."

[1975] Conventional medication systems have made it difficult for users to obtain appropriate information for taking multiple medications safely and effectively. Furthermore, they do not take into account the user's emotional state, resulting in a poor user experience. The present invention aims to solve these problems and enable users to take their medications with peace of mind.

[1976] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information of the user and transmitting it to the server, a means for the provider to input pharmaceutical information and transmit it to the server, and a means for inputting information on drugs that the user takes on a daily basis and transmit it to the server. This makes it possible to manage the basic information of the user, pharmaceutical information, and information on individual drugs in an integrated manner, and to provide optimal drug combinations and suggestions by combining evaluations by a generative AI model and emotion recognition by an emotion engine.

[1977] A "user" is an individual who uses the system and enters their own basic information and information about the medications they are taking.

[1978] "Provider" refers to a corporation or organization that inputs pharmaceutical information, generally a drug manufacturer or medical institution.

[1979] "Basic information" refers to personal data such as the user's gender, age, height, weight, and symptoms.

[1980] "Pharmaceutical information" refers to detailed data such as the name of a drug, its ingredients, efficacy, and side effects.

[1981] "Pharmaceutical information" refers to information about medicines and supplements that users take on a daily basis.

[1982] A "server" refers to a computer system that stores and manages input information and analyzes it in cooperation with generative AI models and emotion engines.

[1983] A "generative AI model" refers to an artificial intelligence system that analyzes data based on the user's basic information and pharmaceutical information, and makes recommendations on optimal drug combinations.

[1984] An "emotion engine" is a system that identifies emotions based on a user's facial recognition data and voice data, and adjusts evaluation results and proposal content based on those results.

[1985] This invention is a drug combination evaluation system that combines a generative AI model and an emotion engine to help users take their medications safely and effectively. This system collects basic information, pharmacological information, and information on the drugs they regularly take, and analyzes this data to provide optimal drug combinations and recommendations.

[1986] System Components

[1987] 1. Server

[1988] Stores and manages basic and pharmaceutical information submitted by multiple users.

[1989] Data is shared with generative AI models and emotion engines.

[1990] Stores pharmaceutical information sent by the provider.

[1991] The evaluation results obtained from the generative AI model and the emotion evaluation results obtained from the emotion engine are stored in a database.

[1992] 2. Terminal

[1993] This includes devices such as smartphones and computers that allow users to enter basic and pharmaceutical information.

[1994] It has the ability to collect facial recognition data and voice data and send it to a server.

[1995] 3. Generative AI Models

[1996] The user's basic information and pharmaceutical information provided by the server are analyzed.

[1997] As a result of the analysis, the system makes recommendations for safe and effective drug combinations and optimal treatment for specific symptoms.

[1998] 4. Emotion Engine

[1999] The system identifies the user's emotions based on facial recognition data and voice data provided by the device.

[2000] Adjust the evaluation results and recommendations depending on your emotional state.

[2001] 5. Pharmaceutical Data Entry System

[2002] It provides a mechanism for providers to input pharmaceutical information and send it to the server.

[2003] System operation example

[2004] An example of the operation of this system is shown below.

[2005] 1. User Registration

[2006] The user enters their basic information (gender, age, height, weight, symptoms, etc.) using a smartphone, and the device sends this information to a server, which then stores the received information in a database.

[2007] 2. Drug information registration

[2008] Providers use the management screen to enter their company's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) and send it to the server, which then stores the received pharmaceutical information in a database.

[2009] 3. User medication information input

[2010] The user enters information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis, and the device sends this information to the server, which then stores the received information in a database.

[2011] 4. AI evaluation

[2012] The server acquires the user's basic information and pharmaceutical information and passes that data to a generative AI model. The generative AI model analyzes the data and suggests drug interactions and the best medications and supplements for specific symptoms. The server receives the evaluation results from the AI ​​and stores them in a database.

[2013] 5. Emotion Recognition by Emotion Engine

[2014] The device collects the user's facial recognition data and voice data and sends it to the server. The emotion engine uses this data to identify the user's emotions and returns the results to the server.

[2015] 6. Adjusting the proposal

[2016] The server adjusts the evaluation results and recommendations of the generative AI model based on the results of the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect.

[2017] Specific examples

[2018] A specific example of the system is shown below: When a user regularly takes over-the-counter vitamins and also takes a new medicine prescribed by a hospital, the system operates as follows.

[2019] The user enters basic information on their smartphone and sends it to the server.

[2020] The provider registers pharmaceutical information on the server.

[2021] The user enters information about the over-the-counter and prescription medications they regularly take and sends it to the server.

[2022] The server passes information to the generative AI model, which analyzes it and returns the evaluation results.

[2023] The terminal collects the user's emotional state and the emotion engine analyzes it.

[2024] The server adjusts the evaluation results and proposals based on the results of the emotion engine.

[2025] It provides users with tailored recommendations to help them take their medication safely and effectively.

[2026] Prompt Sentence Examples

[2027] The following prompt is given to the generative AI model:

[2028] The basic information of the user is as follows:

[2029] Gender: Male

[2030] Age: 35

[2031] Height: 175cm

[2032] Weight: 70kg

[2033] Symptoms: High blood pressure

[2034] Information about medications you are currently taking:

[2035] Over-the-counter medication: Vitamin C supplements

[2036] Prescription medication: Atenolol (hypertension medication)

[2037] The user's emotional state is as follows:

[2038] Stress level: High

[2039] Emotion: Anxiety

[2040] Based on the above information, please suggest the best medication and combination.

[2041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2042] Step 1:

[2043] The user uses a smartphone or computer to enter their basic information (gender, age, height, weight, symptoms, etc.). The information is checked in real time by the device to ensure there are no omissions or errors. The confirmed information is sent from the device to a server, where it is stored in a database.

[2044] Input: User's basic information (gender, age, height, weight, symptoms, etc.)

[2045] Output: Basic information data stored on the server

[2046] Specific operation: The user enters information such as age and weight into the application form on their smartphone and presses the "Submit" button. The device then sends the information to the server, which then stores it in a database.

[2047] Step 2:

[2048] The provider enters pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.) through a dedicated management screen. The entered information is sent by the terminal to the server, where it is stored in a database.

[2049] Input: Provider's pharmaceutical information (drug name, ingredients, efficacy, side effects, etc.)

[2050] Output: Pharmaceutical information data stored on the server

[2051] Specific operation: The provider enters information about the new drug on the management screen and presses the "Register" button. The terminal sends the information to the server, which stores it in a database.

[2052] Step 3:

[2053] Users use their smartphones or computers to enter information about the over-the-counter medications, vitamins, herbal medicines, and prescription medications they take on a daily basis. The device checks the entered information in real time to ensure there are no omissions or errors. The confirmed information is sent to a server, which then stores it in a database.

[2054] Input: User's medication information (over-the-counter drugs, vitamins, herbal medicines, prescription drugs)

[2055] Output: Medication information data stored on the server

[2056] Specific operation: The user enters information about over-the-counter or prescription drugs into the application form and presses the "Submit" button. The device sends the information to the server, which stores it in a database.

[2057] Step 4:

[2058] The server acquires the user's stored basic information, pharmaceutical information, and medication information, and passes this data to the generative AI model. The generative AI model analyzes this data and suggests drug interactions and the best medications and supplements for specific symptoms. The evaluation results generated by the generative AI model are returned to the server, where they are stored in a database.

[2059] Input: Basic information, pharmaceutical information, medication information

[2060] Output: Evaluation results by generative AI model

[2061] How it works: The server retrieves the user's basic information, pharmaceutical information, and medication information from the database and inputs this information into the generative AI model. The generative AI model analyzes the data and generates an evaluation result that says, "There is no problem with taking vitamin C and atenolol together, but be careful of interactions with food," and returns this result to the server.

[2062] Step 5:

[2063] The user collects facial recognition data and voice data through a device (such as a smartphone), and this data is sent to a server. The server passes this data to an emotion engine, which identifies the user's emotion. The results generated by the emotion engine are returned to the server, where they are stored in a database.

[2064] Input: Face recognition data, voice data

[2065] Output: Emotion evaluation results by the emotion engine

[2066] Specific operation: The user turns their face towards the device camera, and the emotion engine determines that the user is currently feeling stressed. The result is sent to the server and stored in a database.

[2067] Step 6:

[2068] The server adjusts the generative AI model's evaluation results and suggestions based on the emotion evaluation results from the emotion engine. For example, if the user is feeling stressed, it will suggest medicines or supplements that have a relaxing effect. The adjusted evaluation results and suggestions are stored in a database.

[2069] Input: Emotion evaluation results, evaluation results of generative AI model

[2070] Output: Adjusted proposal

[2071] Specific operation: If the emotion engine identifies that the user is feeling stressed, the server changes the evaluation result of the generative AI model to suggest a supplement that has a relaxing effect.

[2072] Step 7:

[2073] The server generates feedback to provide the user with the adjusted evaluation results. The device displays this feedback to the user, allowing the user to confirm the feedback. This allows the user to take their medication safely and effectively based on the provided information.

[2074] Input: Adjusted proposal

[2075] Output: Feedback provided to the user

[2076] Specific operation: The server generates feedback such as "The user has high blood pressure and stress, so we recommend that you also take supplements that are expected to have a relaxing effect," and sends it to the device. The user checks the feedback on their smartphone and presses the "I understand" button.

[2077] (Application example 2)

[2078] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2079] Conventional medication systems have the problem of being unable to consider the user's emotional state when evaluating interactions between multiple medications taken by the user, making it difficult to provide optimal recommendations for the user. Furthermore, simply providing safe drug combinations based on information entered by the user often results in insufficient recommendations. Therefore, there is a need for a system that can provide appropriate recommendations while taking the user's emotional state into account.

[2080] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2081] In this invention, the server includes means for inputting basic information of a user and transmitting it to the server, means for a pharmaceutical company to input drug information and transmit it to the server, means for inputting information on drugs the user regularly takes and transmit it to the server, means for the server to acquire the user's basic information and drug information and pass it to the AI, means for the AI ​​to make an evaluation based on the user's basic information and drug information and return it to the server, means for the server to provide the user with the evaluation results from the AI, means for the terminal to collect the user's facial recognition data and voice data and for the emotion engine to identify emotions based on those, and means for the server to adjust the evaluation results and proposal content based on the emotional state from the emotion engine. This makes it possible to provide safe and effective drug combinations and optimal proposals while taking the user's emotional state into consideration.

[2082] "Basic information about the user" refers to the user's gender, age, height, weight, symptoms, and other information related to personal identification.

[2083] "Server" means a centralized computer system that receives, stores, and processes data submitted by users and pharmaceutical companies.

[2084] "Drug information" refers to the name of the drug, its ingredients, effects, side effects, and other details related to the drug.

[2085] "Information about medications taken by the user on a daily basis" refers to information about medications, vitamins, supplements, and herbal medicines that the user takes on a daily basis.

[2086] "AI" is an artificial intelligence system that analyzes data based on the user's basic information and drug information, and evaluates drug interactions.

[2087] The "evaluation results" are information obtained as a result of the AI's analysis, such as whether or not drugs can be taken together and suggestions for the most appropriate drugs and supplements.

[2088] A "terminal" is a device that users use to input basic information and medication information, such as a smartphone or computer.

[2089] "Facial recognition data" is feature data acquired based on a facial image of a user.

[2090] "Voice data" is characteristic data obtained by recording the user's voice.

[2091] The "Emotion Engine" is a system that identifies a user's emotional state based on facial recognition data and voice data.

[2092] "Adjusting the proposal content" means changing the AI's evaluation results and proposal content based on the user's emotional state identified by the emotion engine.

[2093] The present invention is a system that combines AI and an emotion engine to evaluate drug interactions and provide optimal recommendations based on the user's emotional state, so that users can take their medication safely and effectively. Specific embodiments of this system are described in detail below.

[2094] System Configuration

[2095] The system consists of the following main components:

[2096] 1. Server

[2097] 2. Device (smartphone, PC, etc.)

[2098] 3. AI (Generation AI)

[2099] 4. Emotion Engine

[2100] 5. Database

[2101] Hardware and software used

[2102] Hardware: Smartphone camera (for facial recognition), microphone (for voice data acquisition), server (for data storage and processing)

[2103] Software: OpenCV (face recognition), DeepFace (emotion analysis), requests (API communication), RESTful API server (combination evaluation)

[2104] Processing Description

[2105] Enter user and medication information

[2106] Users use a device such as a smartphone to enter their basic information (gender, age, height, weight, symptoms, etc.). The entered basic information is sent from the device to a server and stored in a database. Pharmaceutical companies use a management screen to enter their own drug information (drug name, ingredients, efficacy, side effects, etc.) and similarly send this to the server. This aggregates information about users and drugs.

[2107] Drug interaction evaluation

[2108] The server acquires the user's basic information and medication information and sends it to the generation AI. The generation AI evaluates drug interactions based on this information and returns the evaluation results to the server. The evaluation results are stored in a database.

[2109] emotion recognition

[2110] The user's facial recognition data and voice data are collected using the smartphone's camera and microphone. This data is sent to a server and analyzed by the emotion engine, which uses DeepFace to identify emotions from facial images and also analyzes voice data.

[2111] Adjusting the proposal

[2112] The server adjusts the evaluation results and recommendations based on the emotional state identified by the emotion engine. For example, if the user is feeling stressed, it will suggest medication or supplements that have a relaxing effect. The adjusted evaluation results are provided to the user, who can view them on their device as feedback.

[2113] Specific examples

[2114] For example, if a user is taking a new prescription medication while also taking over-the-counter vitamins, the system would operate as follows:

[2115] 1. User registration: The user enters basic information such as gender, age, weight, and symptoms, and sends it to the server.

[2116] 2. Drug information registration: Information on newly developed drugs is registered on the server by pharmaceutical companies.

[2117] 3. Enter user medication information: Enter information about over-the-counter medications, vitamins, and prescription medications that users regularly take.

[2118] 4. Evaluation by AI: The server passes the user's basic information and medication information to the generation AI, which then analyzes them and makes an evaluation.

[2119] 5. Emotion recognition by emotion engine: The emotion engine recognizes the user's emotional state (e.g., stress or anxiety) through the device.

[2120] 6. Adjustment of proposal content: Based on the results of the emotion engine, the server adjusts the evaluation results and proposal content.

[2121] 7. Providing feedback: The server provides the adjusted evaluation results to the user, who can then check the feedback to feel more comfortable taking their medication.

[2122] Prompt Sentence Examples

[2123] "Based on the information entered by the user, the generative AI model evaluates drug interactions. The DeepFace library is used to recognize emotions from the user's facial image, and the emotional results are reflected in the evaluation results."

[2124] In this way, this system can suggest safe and effective drug combinations that take into account the user's emotional state.

[2125] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2126] Step 1:

[2127] Entering user information

[2128] Users use devices such as smartphones to input their basic information (gender, age, height, weight, symptoms, etc.). This basic information is sent to the server by the device. The input data is then saved in the server's database.

[2129] Step 2:

[2130] Entering drug information

[2131] Pharmaceutical companies use the management screen to input their drug information (drug name, ingredients, efficacy, side effects, etc.). This information is sent from the management screen to the server, where it is stored in the server's database.

[2132] Step 3:

[2133] Entering information about medications currently being used

[2134] Users use a device such as a smartphone to input information about the over-the-counter medicines, vitamins, herbal medicines, and prescription medicines they take on a daily basis. This medication information is sent from the device to a server, where it is stored in the server's database.

[2135] Step 4:

[2136] Request for drug combination evaluation

[2137] The server retrieves the user's basic information and medication information from the database and sends this data to the generation AI. The AI ​​evaluates drug interactions based on the received data and returns the results to the server. The evaluation results include whether the drug interaction is appropriate and the risks of inappropriate drug interactions.

[2138] Step 5:

[2139] Emotion recognition data collection

[2140] Users use their smartphone's camera and microphone to collect facial recognition and voice data, which is then sent from the device to a server, which then passes the data to the emotion engine.

[2141] Step 6:

[2142] Emotional state analysis

[2143] The facial recognition data and voice data sent to the server are analyzed by an emotion engine (DeepFace) to identify the user's emotional state. The emotion engine determines the user's emotions (e.g., stress, anxiety, joy, etc.) based on the acquired data.

[2144] Step 7:

[2145] Adjustment of evaluation results

[2146] The server adjusts the AI's drug combination assessment results and recommendations based on the emotional state from the emotion engine. For example, if the user is feeling stressed, it will suggest medications or supplements with a relaxing effect. The adjusted results are the final feedback provided to the user.

[2147] Step 8:

[2148] Providing Feedback

[2149] The server returns the adjusted evaluation results to the device, and the user can check this feedback on their smartphone, etc. The feedback includes recommendations on the safety of drug interactions and the optimal medications and supplements based on their emotional state.

[2150] In this way, the system of the present invention processes the data entered at each step, provides safe and effective drug combinations, and makes optimal suggestions based on the user's emotional state.

[2151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2152] 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.

[2153] 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 robot 414.

[2154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2155] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2161] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2162] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2166] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2167] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical...

Claims

1. A means for inputting basic information of a user and transmitting it to a server; A means for a pharmaceutical company to input drug information and transmit it to a server; A means for inputting information on medications taken by a user on a daily basis and transmitting the information to a server; The server acquires basic information about the user and medication information and passes it to the AI. The AI ​​will evaluate the user's basic information and drug information and return the results to the server. A means for the server to provide the user with the evaluation results from the AI; A system including:

2. 2. The system according to claim 1, wherein a mobile terminal is used as a means for inputting user and drug information.

3. The system of claim 1, wherein the AI ​​evaluation means suggests optimal medications and supplements for specific symptoms.

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