System

Abstract

To provide a system.SOLUTION: A system includes: an emotion engine which recognizes a user's emotion; selection means for selecting a candidate medicine for improving the user's physical condition, on the basis of information including the user's health status, the user's prescribed medicine, the user's age, and the user's gender, and the emotion, in consideration of the combination with the prescribed medicine, by inputting a specific prompt sentence into an AI model; and means for displaying the selected medicine and information indicating a stock status of the selected medicine at a physical store on the user's terminal.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] Users who cannot go to the hospital have difficulty determining which medicine to choose when they are feeling unwell or unable to go to the hospital. In particular, it is necessary to make decisions that take into account interactions between over-the-counter and prescribed medicines, the state of local medical care, and the user's living situation (e.g., elderly people, dual-income households, etc.). [Means for solving the problem]

[0005] The system of the present disclosure includes an emotion engine that recognizes a user's emotions, a selection means that selects candidate drugs that will improve the user's physical condition based on information including the user's health condition, the user's prescribed drugs, the user's age, the user's gender, and the emotion, taking into consideration drug interactions with the prescribed drugs, by inputting a specific prompt sentence into a generative AI model, and a means that displays the selected drug and information indicating the stock status of the selected drug at a physical store on the user's terminal. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that includes AI that determines the user's health condition and the appropriate medication to use when they are feeling unwell. In this system, the AI ​​determines the appropriate medication to use based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.).

[0029] "Example 2"

[0030] Additionally, the AI ​​takes into account interactions between over-the-counter and prescription medications. For example, if a user is already taking a specific prescription drug, the AI ​​will avoid over-the-counter medications that may interact with the prescription drug.

[0031] "Example 3"

[0032] The AI ​​also makes decisions that take into account the state of local medical care and the user's lifestyle. For example, if the medical services and types of medicine available vary by region, the AI ​​will make decisions based on that information. The AI ​​will also suggest medication options that are appropriate for the user's lifestyle, such as if the user is elderly or if both partners are busy working.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.).

[0036] Step 2: Based on the information received, the AI ​​determines the appropriate medication.

[0037] Step 3: Present the AI's decision to the user.

[0038] "Example 2"

[0039] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.) and over-the-counter medication information.

[0040] Step 2: Based on the information received, the AI ​​makes a decision taking into account interactions between over-the-counter and prescribed medications.

[0041] Step 3: Present the AI's decision to the user.

[0042] "Example 3"

[0043] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.), the local medical situation, and the user's living situation.

[0044] Step 2: Based on the information received, the AI ​​makes a decision taking into account the local medical situation and the user's living situation.

[0045] Step 3: Present the AI's decision to the user.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form 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."

[0048] Conventional drug selection systems have difficulty selecting the appropriate drug based on the user's health condition or when feeling unwell, and a particular issue is that they do not take into account interactions between over-the-counter and prescription drugs, the state of local medical care, or the user's living situation. This increases the risk that users will select the wrong drug, so a more accurate drug selection system is needed.

[0049] 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.

[0050] In this invention, the server includes means for a user to input information about their health condition or poor physical condition, means for a terminal to send the input information to the server, means for the server to receive the input information and store it in a database, means for the server to input the stored data into an AI model, means for the AI ​​model to determine an appropriate medication, means for the server to receive the results of the AI ​​model and return them to the user, and means for the user to check the results via the terminal. This makes it possible to select an appropriate medication for a user's health condition or poor physical condition, and enables highly accurate judgments that take into account interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[0051] "User" refers to an individual who uses the system to input information about their health or ill health.

[0052] "Terminal" refers to a device through which a user inputs information and communicates with a server. Examples include smartphones and personal computers.

[0053] "Server" refers to a central computer system that receives and processes information submitted by users.

[0054] "Database" refers to a system used by a server to store information received from a user. Examples include MySQL (registered trademark) and PostgreSQL.

[0055] An "AI model" is an algorithm built using a machine learning framework that determines appropriate medication based on a user's health condition or physical condition.

[0056] "Health Condition" refers to a user's current physical condition or symptoms. Examples include headache and fever.

[0057] "Illness" refers to any physical discomfort or abnormality experienced by the user. Examples include nausea and fatigue.

[0058] "Input Information" refers to data provided by the User to the System via the Terminal, including, for example, symptoms, existing prescription medications, age, and gender.

[0059] "Appropriate medication" refers to the most effective and safe medication that the AI ​​model selects based on the user's health condition or condition.

[0060] "Results" refers to the information about the appropriate medication determined by the AI ​​model and the data returned to the user.

[0061] This invention is a system for selecting appropriate medications for a user's health condition or when they are feeling unwell. This system uses an AI model to determine the appropriate medication based on information entered by the user and returns the results to the user.

[0062] The server includes an emotion engine that recognizes the user's emotions, a selection means that selects candidate drugs that will improve the user's physical condition based on information including the user's health condition, the user's prescribed medications, the user's age, the user's gender, and the emotion by inputting a specific prompt sentence into a generative AI model, taking into consideration interactions with the prescribed medications, and a means for displaying the selected drugs and information indicating the inventory status of the selected drugs at physical stores on the user's terminal. The selection means may select candidate drugs that will improve the user's physical condition based on the user's lifestyle and interactions with the prescribed medications. The selection means may select candidate drugs that will improve the user's physical condition based on local medical services in the user's area, the user's lifestyle, and interactions with the prescribed medications.

[0063] First, the user uses a device such as a smartphone or computer to enter information about their health condition or physical condition into the system's input form. For example, they enter information such as "I have a headache," "My current prescription is aspirin," "I'm 30 years old," and "I'm male."

[0064] The terminal then transmits the information entered by the user to the server, which does so securely using the HTTPS protocol.

[0065] The server receives the information sent from the device and stores it in a database such as MySQL or PostgreSQL. The stored data includes information such as the user ID, symptoms, existing prescription medications, age, and gender.

[0066] The server retrieves the information stored in the database and inputs it into an AI model built using machine learning frameworks such as TENSORFLOW® and PyTorch, which compares it with past data to determine the most appropriate medication for the user's health condition.

[0067] After the AI ​​model selects a medication, the server returns the results to the user, which are displayed in a user-friendly format, such as a message like "We recommend using ibuprofen."

[0068] As a concrete example, consider the case where a user inputs "I have a headache." The user inputs that he / she is currently taking "aspirin" as a prescription drug. Also, assume that the user is 30 years old and male.

[0069] The server receives this information and stores it in a database. The server then inputs this data into an AI model, which uses past data to determine the most appropriate medication for the user's symptoms. In this case, the AI ​​model might select "ibuprofen."

[0070] The server returns the result to the user, who receives a message via the terminal saying, "We recommend using ibuprofen."

[0071] An example of a prompt sentence to input to the generative AI model is, "A 30-year-old male currently taking aspirin as a prescription medication. Currently suffering from a headache. Please select the medication that is most appropriate for this user."

[0072] The above is an embodiment of the present invention.

[0073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0074] Step 1:

[0075] The user inputs information about their health condition or ill health.

[0076] Specifically, the user opens the application on their smartphone or computer and goes to the "Symptom Input" screen, where they enter information such as "I have a headache," "My current prescription medication is aspirin," "I'm 30 years old," and "I'm male."

[0077] Input: User's health status and ill health information

[0078] Output: Input information is saved on the device

[0079] Step 2:

[0080] The terminal sends the input information to the server.

[0081] Specifically, when the user presses the "Send" button, the terminal sends the input information to the server using the HTTPS protocol.

[0082] Input: Input information saved on the device

[0083] Output: Input sent to the server

[0084] Step 3:

[0085] The server receives the input information and stores it in a database.

[0086] Specifically, the server analyzes the information it receives and stores information such as user ID, symptoms, existing prescription medications, age, and gender in a database such as MySQL or PostgreSQL.

[0087] Input: The input sent to the server

[0088] Output: Information stored in the database

[0089] Step 4:

[0090] The server inputs the stored data into the AI ​​model.

[0091] Specifically, the server retrieves information corresponding to the "user ID" from the database and inputs it into an AI model built using machine learning frameworks such as TensorFlow and PyTorch.

[0092] Input: Information stored in a database

[0093] Output: Data input into the AI ​​model

[0094] Step 5:

[0095] The AI ​​model determines the appropriate medication.

[0096] Specifically, the AI ​​model compares the data with past data to determine the medication that best suits the user's health condition. In this case, the AI ​​model might select "ibuprofen."

[0097] Input: Data fed into the AI ​​model

[0098] Output: Determined appropriate medication

[0099] Step 6:

[0100] The server receives the results of the AI ​​model and returns them to the user.

[0101] Specifically, the server receives the results from the AI ​​model, generates a message saying "We recommend using ibuprofen," and returns it to the user.

[0102] Input: Results from the AI ​​model

[0103] Output: The message returned to the user

[0104] Step 7:

[0105] The user checks the results through the terminal.

[0106] Specifically, the user opens the app on their smartphone and sees the message "Using ibuprofen is recommended" on the "Results Display" screen.

[0107] Input: The message returned by the server

[0108] Output: The result that the user sees

[0109] The above is a concrete explanation of the processing flow of the program of this system.

[0110] (Application example 1)

[0111] Next, a description will be given of Application Example 1 of Embodiment 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."

[0112] Conventional drug selection systems have difficulty selecting the appropriate drug based on the user's health condition or when feeling unwell. Furthermore, when users try to purchase the recommended drug at a physical store, they have the problem of not knowing which store they can purchase it at. Furthermore, since they cannot consider interactions between over-the-counter and prescription drugs or the state of local medical care, it is difficult to select the optimal drug for the user.

[0113] 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.

[0114] In this invention, the server includes a means including AI for determining the user's health condition and the appropriate medication to select when feeling unwell, a means for the AI ​​to determine the appropriate medication based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.), and a means for displaying the recommended medication as available for purchase at a nearby physical store. This allows the user to select the appropriate medication based on their own health condition and makes it easy to purchase at a physical store. It also makes it possible to make decisions taking into account interactions between over-the-counter and prescription medications and the local medical situation.

[0115] A "user" is an individual who uses the system to select medications for their own health condition and when they are feeling unwell.

[0116] "Health status" refers to the user's current physical and mental state, including physical ailments and pre-existing medical history.

[0117] "Illness" refers to physical discomfort or abnormalities felt by the user, and specifically includes symptoms such as headache, fever, and stomachache.

[0118] "Medicine selection" refers to the act of selecting an appropriate medicine for the user's health condition or when they are feeling unwell.

[0119] "AI" refers to artificial intelligence, which includes algorithms and models for selecting appropriate medications based on user input.

[0120] "Input information" refers to data provided by the user to the system, and specifically includes information such as physical condition, existing prescription medications, age, and gender.

[0121] "Appropriate medication" refers to medication that is effective for the user's health condition or physical condition and can be used safely.

[0122] "Brick and mortar store" refers to a physical store where a user can purchase recommended medications.

[0123] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[0124] "Prescription drugs" refers to drugs that require a doctor's prescription.

[0125] "Drug interactions" refers to interactions that occur when multiple drugs are taken at the same time, which may change the effectiveness or side effects of the drugs.

[0126] "Regional medical situation" refers to the state of medical service provision and availability of medical resources in the area where the user lives.

[0127] "Life situation" refers to the environment and conditions in the user's daily life, and specifically includes situations such as elderly people and dual-income households.

[0128] A system for implementing this invention includes AI that determines the user's health condition and the medication to be selected when they are feeling unwell. The system uses AI to determine the appropriate medication based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.), and displays the recommended medication as available for purchase at a nearby physical store.

[0129] Hardware and software used

[0130] Hardware: Smartphone

[0131] Software: Python, Requests library (for API requests), AI models (e.g., TensorFlow, PyTorch)

[0132] Data processing and calculation

[0133] 1. Getting user input:

[0134] Users enter information about their health condition and symptoms of illness through a smartphone app, including symptoms, age, gender, and current prescription medications.

[0135] 2. AI-based drug selection:

[0136] The server sends the input information obtained from the user to the AI ​​model, which then selects the appropriate medication. The AI ​​model then takes into account the user's symptoms and interactions with existing prescription medications to recommend the most appropriate medication.

[0137] 3. Recommended medication indication:

[0138] The server displays the recommended medicines to the user and provides information on nearby physical stores where they can be purchased, allowing the user to immediately purchase the recommended medicines.

[0139] Specific examples

[0140] For example, if a user inputs "I have a headache," their age is 30, their gender is male, and they have no existing prescription medications, the AI ​​will recommend "acetaminophen." The server will then display the recommended medication to the user and provide information on where it can be purchased at nearby pharmacies.

[0141] Prompt Sentence Examples

[0142] User symptoms: Headache

[0143] Age: 30

[0144] Gender: Male

[0145] Current prescription medications: None

[0146] Data to send to the AI ​​model:

[0147] {

[0148] "symptoms": "headache",

[0149] "age": 30,

[0150] "gender": "male",

[0151] "existing_medications": "none"

[0152] }

[0153] In this way, a system can be realized that selects appropriate medications based on the user's health condition and supports purchases in physical stores.

[0154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0155] Step 1:

[0156] Users launch the smartphone app and enter their health status and symptoms of illness, including symptoms, age, gender, and current prescription medications. The entered information is then stored in the application's database.

[0157] Input: User's symptoms, age, gender, existing prescription medications

[0158] Output: Saved user input information

[0159] Step 2:

[0160] The device sends the saved user input information to the server, which receives the data and converts it into a format that can be input into the AI ​​model.

[0161] Input: User input information

[0162] Output: Data in a format that can be fed into an AI model

[0163] Step 3:

[0164] The server inputs the converted data into an AI model to select the appropriate medication. The AI ​​model then takes into account the user's symptoms and interactions with existing prescription medications to recommend the most appropriate medication.

[0165] Input: Data in the format that is fed into the AI ​​model

[0166] Output: Recommended medication information

[0167] Step 4:

[0168] The server receives the recommended medication information and generates data to display to the user, including the name of the recommended medication, its effects, side effects, and availability at nearby physical stores.

[0169] Input: Recommended medication information

[0170] Output: Data to display to the user

[0171] Step 5:

[0172] The terminal displays the data received from the server to the user, who can then check the recommended medicines and obtain information on purchasing them at a nearby physical store.

[0173] Input: The data to display to the user

[0174] Output: Medication information displayed to the user

[0175] Step 6:

[0176] Based on the displayed information, users can purchase the recommended medication at a nearby brick-and-mortar store, and if necessary, they can use the chat function to ask additional questions to a pharmacist.

[0177] Input: Medication information displayed to the user

[0178] Output: Purchase of medicines and answers to follow-up questions

[0179] Example 2

[0180] Next, a description will be given of Example 2 of Form 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."

[0181] Conventional drug selection systems have difficulty considering interactions between prescription and over-the-counter drugs taken by users, resulting in health risks due to incorrect drug selection. Furthermore, since they are unable to consider the state of local medical care or the user's living situation, it is difficult to select the optimal drug for each individual user. This has led to the issue of being unable to properly manage the user's health condition.

[0182] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0183] In this invention, the server includes a means for a user to input prescription drug information, a means for a terminal to transmit the input information to the server, a means for the server to search an over-the-counter drug database, a means for the server to evaluate the risk of drug interactions using a generative AI model, a means for the server to return the evaluation results to the terminal, and a means for the terminal to display the evaluation results to the user. This enables selection of drugs that take into consideration interactions between prescription drugs and over-the-counter drugs taken by the user, and further allows for decisions to be made that take into consideration the state of local medical care and the user's living situation, making it possible to select the most appropriate drug for each individual user.

[0184] "User" refers to an individual who utilizes the system to enter prescription drug information and obtain information regarding drug selection.

[0185] "Terminal" refers to the device through which the user enters prescription drug information and communicates with the server. Specifically, this includes smartphones and personal computers.

[0186] "Server" refers to the computer system that receives user-submitted information, searches a database of commercially available drugs, and uses a generative AI model to assess the risk of interactions.

[0187] "Over-the-counter drug database" refers to a collection of data including over-the-counter drug names, ingredients, and interaction information.

[0188] "Generative AI models" refer to artificial intelligence models that analyze prescription drug information and over-the-counter drug ingredient information entered by users and evaluate the risk of drug interactions. Specifically, they include models that use natural language processing technology.

[0189] "Risk of interaction" refers to the potential health risks that may arise when prescription and over-the-counter medications are taken at the same time.

[0190] "Evaluation results" refers to information about interactions between prescription and over-the-counter drugs obtained as a result of analysis by the generative AI model.

[0191] "Regional medical situation" refers to the state of medical service provision and the state of medical resource utilization in the area where the user resides.

[0192] "Life situation" refers to factors that affect daily life, such as the user's age, occupation, and home environment.

[0193] The present invention provides a system for assisting a user in selecting medications that take into consideration interactions between prescription and over-the-counter medications. Specific embodiments of this system are described below.

[0194] First, the user enters prescription drug information using a terminal. The terminal can be a smartphone or a PC. The prescription drug information entered by the user is sent from the terminal to the server. At this time, the data is encrypted before being sent, ensuring security.

[0195] The server searches an over-the-counter drug database based on the received prescription drug information. The over-the-counter drug database contains information such as the names, ingredients, and drug interactions of over-the-counter drugs. Using a generative AI model, the server analyzes the ingredient information of prescription and over-the-counter drugs and evaluates the risk of drug interactions.

[0196] The generative AI model uses natural language processing technology, such as GPT-4 (registered trademark). This model evaluates the risk of interactions between prescription and over-the-counter drugs by inputting a prompt sentence.

[0197] For example, if the user is taking "aspirin," the server will input the following prompt into the generative AI model:

[0198] If the user is taking aspirin, identify any over-the-counter medications that may interact.

[0199] Based on this prompt, the generative AI model identifies over-the-counter medications that may interact with aspirin and assesses their risk, resulting in the information that "taking aspirin and ibuprofen at the same time increases the risk of bleeding and should be avoided."

[0200] The server returns the evaluation results to the terminal, which then displays the evaluation results received from the server to the user. The user can then check the evaluation results on the application screen and select an appropriate medication.

[0201] This system allows users to select medications that take into account interactions between prescription and over-the-counter medications, thereby reducing health risks.It also takes into account the local medical situation and the user's living situation, allowing for the selection of medications that are optimal for each individual user.

[0202] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0203] Step 1:

[0204] The user enters prescription information.

[0205] The user opens the application on their smartphone or computer and enters the name and ingredient information of the prescription medication they are currently taking. For example, the user enters "aspirin" and presses the send button. The input data is the name and ingredient information of the prescription medication.

[0206] Step 2:

[0207] The terminal transmits the input information to the server.

[0208] The terminal encrypts the prescription drug information entered by the user and sends it to the server. Once transmission is complete, the terminal waits for a response from the server. The input data is the encrypted prescription drug information, and the output data is the prescription drug information sent to the server.

[0209] Step 3:

[0210] The server searches the over-the-counter drug database.

[0211] The server searches the over-the-counter drug database based on the received prescription drug information. The database contains over-the-counter drug names, ingredients, and interaction information. The input data is the prescription drug information, and the output data is a list of applicable over-the-counter drugs.

[0212] Step 4:

[0213] The server uses a generative AI model to assess the risk of the interaction.

[0214] The server inputs a prompt into the generative AI model and analyzes the ingredient information of prescription and over-the-counter drugs. For example, the prompt might read, "If the user is taking aspirin, please identify any over-the-counter drugs that may interact with it." The generative AI model evaluates the risk of interactions between prescription and over-the-counter drugs. The input data is the prompt and a list of over-the-counter drugs, and the output data is the interaction risk assessment results.

[0215] Step 5:

[0216] The server returns the evaluation results to the terminal.

[0217] The server returns the results of the generative AI model's evaluation to the device. The evaluation results include interaction risks and recommended countermeasures. The input data is the interaction risk evaluation results, and the output data is the evaluation results sent to the device.

[0218] Step 6:

[0219] The terminal displays the evaluation results to the user.

[0220] The terminal displays the evaluation results received from the server to the user. The user can check the evaluation results on the application screen and select an appropriate medication. The input data is the evaluation results received from the server, and the output data is the evaluation results displayed to the user.

[0221] (Application example 2)

[0222] Next, a description will be given of Application Example 2 of Form 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."

[0223] Conventional drug selection systems have difficulty considering interactions between prescription drugs and over-the-counter drugs that users are already taking, resulting in health risks due to incorrect drug selection. Another problem is that users have to spend time and effort checking for interactions when purchasing drugs. Furthermore, because they are unable to consider the state of local medical care or the user's living situation, it is difficult to select the optimal drug for each individual user.

[0224] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence means for determining the user's health condition and the selection of medication when feeling unwell, a means for making a decision taking into account interactions between over-the-counter drugs and prescribed medications, a means for scanning barcodes of over-the-counter drugs, a means for registering prescription drugs, a means for checking for interactions, a means for allowing electronic payment if no interactions exist, and a means for displaying a warning if an interaction exists. This allows interactions between prescription drugs and over-the-counter drugs already taken by the user to be checked in real time, enabling appropriate drug selection and safe purchase. Furthermore, since the server can make decisions taking into account the local medical situation and the user's living situation, it is possible to select the most appropriate medication for each individual user.

[0225] "User" refers to an individual who uses the system to select and purchase medications.

[0226] "Health" refers to a user's current physical and mental state.

[0227] "Illness" refers to any discomfort or symptoms of illness experienced by the user.

[0228] "Artificial intelligence" refers to technology that allows computer systems to imitate human intelligence to make judgments and learn.

[0229] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[0230] "Prescription drugs" refers to drugs that require a doctor's prescription.

[0231] Drug interactions refer to interactions that occur when multiple medications are taken at the same time.

[0232] "Barcode" refers to a code consisting of a series of lines and numbers that contains product information.

[0233] "Scanning" refers to the act of reading a barcode.

[0234] "Registration" refers to the act of a user entering and saving information in the system.

[0235] "Drug interaction" refers to the chemical or physiological effects that occur when two or more drugs are taken at the same time.

[0236] "Electronic payments" refers to monetary transactions conducted over the Internet.

[0237] A "warning" refers to a message that alerts the user.

[0238] "Community medical care" refers to the state of medical service provision in a specific area.

[0239] "Life situation" refers to the user's living environment and lifestyle.

[0240] A "generative AI model" refers to a model that allows artificial intelligence to generate new information and content based on data.

[0241] A "prompt" refers to an instruction sentence input into a generative AI model.

[0242] As an embodiment of the present invention, the following system is constructed.

[0243] System Configuration

[0244] The system includes artificial intelligence (AI) to determine the user's health condition and the appropriate medication when they are unwell. Specifically, the system uses the following hardware and software:

[0245] Hardware

[0246] Smartphone: Used by users to scan barcodes on over-the-counter medications.

[0247] Server: Runs the AI ​​model and manages the database.

[0248] software

[0249] Python: The main programming language.

[0250] OpenCV: Used to scan barcodes using a smartphone camera.

[0251] Barcode: Used to generate and read barcodes.

[0252] Requests: Used to access the drug database through API.

[0253] Processing flow

[0254] 1. Barcode Scanning: A user scans the barcode of an over-the-counter medication using the smartphone camera. The OpenCV library is used to capture the camera image and read the barcode.

[0255] 2. Prescription drug registration: The user registers the prescription drugs they are currently taking in the app, which allows the system to store the user's prescription drug information in a database.

[0256] 3. Interaction check: Based on the scanned over-the-counter drug information, AI checks for interactions with prescription drugs. If an interaction is found, a warning is displayed to the user and the purchase is canceled. If there is no interaction, electronic payment is allowed.

[0257] 4. Electronic Payment: If no interactions are determined, users can purchase over-the-counter medications using their smartphones.

[0258] Specific examples

[0259] For example, if a user attempts to purchase an over-the-counter drug "drugX," they scan the barcode of "drugX" with their smartphone's camera. The system checks whether "drugX" interacts with "drugA," a prescription drug registered by the user. If there is an interaction, the app displays a warning and cancels the purchase. If there is no interaction, the user can purchase "drugX" through electronic payment.

[0260] Prompt Sentence Examples

[0261] "Create an application that helps users choose the right over-the-counter medication by checking its interactions with prescription medications they are already taking when purchasing them. Include a feature that scans the barcode of over-the-counter medications and checks for interactions with prescription medications in real time."

[0262] In this way, we have created a system that checks interactions between prescription drugs and over-the-counter drugs in real time, enabling appropriate drug selection and safe purchase. Furthermore, since the system can take into account the local medical situation and the user's living situation, it is possible to select the most appropriate drug for each individual user.

[0263] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0264] Step 1:

[0265] A user scans the barcode of an over-the-counter drug using the smartphone camera. The input is the barcode of the over-the-counter drug, and the output is the information about the over-the-counter drug read from the barcode. Specifically, the smartphone camera is activated, the barcode is captured using the OpenCV library, and the drug information is obtained using the barcode library.

[0266] Step 2:

[0267] The device registers the prescription medications that the user is currently taking. The input is the prescription medication information entered by the user, and the output is the prescription medication information stored in the database. Specifically, the user enters the name and dosage of the prescription medication into the app's input form, and the information is sent to the server and stored in the database.

[0268] Step 3:

[0269] The server checks for interactions based on the scanned over-the-counter drug information and registered prescription drug information. The input is over-the-counter drug information and prescription drug information, and the output is a judgment result regarding whether or not an interaction exists. Specifically, the server compares the over-the-counter drug and prescription drug databases and evaluates the possibility of interactions using an AI model.

[0270] Step 4:

[0271] The server displays a warning to the user based on whether or not there is an interaction. The input is the interaction judgment result, and the output is a warning message to the user. Specifically, if there is an interaction, the app displays a warning message to the user and stops the purchase.

[0272] Step 5:

[0273] The server allows electronic payment if there is no interaction. The input is the result of the judgment that there is no interaction, and the output is a notification that the electronic payment has been completed. Specifically, the app displays a purchase confirmation message to the user, and when the user presses the confirm button, the electronic payment is made.

[0274] Step 6:

[0275] The server saves the purchase history in a database. The input is information about the purchased over-the-counter drug and the user's information, and the output is updated purchase history data. Specifically, after the electronic payment is completed, the information about the purchased over-the-counter drug is added to the database and the user's purchase history is updated.

[0276] Example 3

[0277] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0278] Conventional medical systems have had the problem of being unable to select medical services and medications that fully take into account the user's individual living situation and the state of local medical care. It was particularly difficult to propose appropriate medical services to elderly users and dual-income households. Furthermore, decisions that took into account interactions between over-the-counter and prescription drugs were also insufficient.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0280] In this invention, the server includes: means for a user to input information such as their age, occupation, and lifestyle using a terminal; means for collecting data on regional medical services and types of medication; means for storing the collected regional medical data and the user's living situation data in a database; means for training a generative AI model based on the data stored in the database; means for generating prompt sentences based on the user's living situation and regional medical data; and means for the generative AI model to suggest optimal medical services and medications. This enables optimal medical services and medications to be selected taking into account the user's individual living situation and the local medical situation.

[0281] "User" refers to an individual who uses the system to input information about their health and lifestyle.

[0282] "Terminal" refers to an electronic device that a user uses to input information.

[0283] "Server" refers to a central processing unit that receives information from users and collects, stores, and processes data.

[0284] "Database" refers to an information management system for storing collected regional medical data and user living situation data.

[0285] A "generative AI model" refers to an artificial intelligence model that is trained based on data stored in a database and suggests optimal medical services and medication options.

[0286] A "prompt sentence" refers to an instruction sentence that includes the user's living situation and local medical data and is input to the generative AI model.

[0287] "Medical Services" refers to medical treatments and procedures provided to improve the health of a User.

[0288] "Medicine" refers to a pharmaceutical product used to improve the User's physical condition.

[0289] "Regional medical data" refers to information about medical services and types of medicines in a particular region.

[0290] "Lifestyle data" refers to individual information such as the user's age, occupation, and lifestyle.

[0291] This invention is a system that proposes optimal medical services and drug selections in consideration of the user's living situation and the state of local medical care. Specific embodiments of this system will be described below.

[0292] First, the user uses a device to input information such as their age, occupation, and lifestyle. The device can be an electronic device such as a smartphone, tablet, or PC. For example, if the user is 70 years old and both partners work and lead busy lives, they can input that information into the device.

[0293] Next, the server collects data on medical services and medications in each region. The server uses a server equipped with a high-performance GPU (e.g., a server equipped with a high-performance graphics processing unit) to obtain regional medical data from the Internet. For example, the server collects information on Hospital A and Pharmacy B and stores it as regional medical data.

[0294] The server stores the collected regional medical data and user living situation data in a database management system (e.g., MySQL). For example, the server might store data such as "User ID: 123, Age: 70, Occupation: Dual-income household, Region: X City, Available medical services: Hospital A, Pharmacy B" in the database.

[0295] Next, the server uses a machine learning framework (e.g., TensorFlow) to train a generative AI model based on the data stored in the database. The generative AI model receives the user's living situation and local medical data as input and suggests the optimal medical service and medication selection. For example, the server learns patterns such as "if the user is elderly and both partners are busy working, a home visit medical service would be appropriate."

[0296] The server generates a prompt based on the user's living situation and local medical data. For example, the server generates the following prompt:

[0297] Example prompt sentence:

[0298] The user is a 70-year-old elderly person who leads a busy life as both partners work. In the area where the user lives, Hospital A and Pharmacy B are available. Taking this situation into consideration, please suggest the best medical service and medication selection.

[0299] Based on this prompt, the generative AI model will make a suggestion, for example, "Use the home visit medical service from Hospital A and receive the prescribed medicine from Pharmacy B via home delivery." The user can receive this suggestion through their device and make arrangements for medical services and medicine as needed.

[0300] In this way, the present invention enables selection of optimal medical services and medicines taking into consideration the individual living conditions of the user and the state of local medical care. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0301] Step 1:

[0302] A user uses a terminal to input information such as their age, occupation, and lifestyle. For example, if the user is 70 years old and both partners work and lead busy lives, they input this information into the terminal. The input data is the user's individual information such as age, occupation, and lifestyle. The output is the transmission of this information from the terminal to the server.

[0303] Step 2:

[0304] The server collects data on medical services and types of medications in each region. The server obtains regional medical data via the Internet. For example, the server collects information on Hospital A and Pharmacy B and stores it as regional medical data. The input data is information about the region, and the output is the collected data on medical services and medications.

[0305] Step 3:

[0306] The server stores the collected regional medical data and user living situation data in a database. The server uses a database management system (e.g., MySQL) to store this data. For example, the server might store data such as "User ID: 123, Age: 70, Occupation: Dual-income household, Area: X City, Available medical services: Hospital A, Pharmacy B" in the database. The input data is the collected regional medical data and user living situation data, and the output is the data stored in the database.

[0307] Step 4:

[0308] The server uses a machine learning framework (e.g., TensorFlow) to train a generative AI model based on data stored in a database. The server receives the user's living situation and local medical data as input, and trains a model to suggest optimal medical services and medication options. For example, the server learns patterns such as "if the user is elderly and both partners are busy working, home visit medical services are appropriate." The input data is the data stored in the database, and the output is the trained generative AI model.

[0309] Step 5:

[0310] The server generates a prompt based on the user's living situation and local medical data. For example, the server generates the following prompt:

[0311] Example prompt sentence:

[0312] The user is a 70-year-old elderly person who leads a busy life as both partners work. In the area where the user lives, Hospital A and Pharmacy B are available. Taking this situation into consideration, please suggest the best medical service and medication selection.

[0313] The input data is the user's living situation and local medical data, and the output is the generated prompt sentence.

[0314] Step 6:

[0315] The generative AI model suggests optimal medical services and medications based on the prompt text. For example, the generative AI model might suggest "Use the home visit service of Hospital A and receive the medication prescribed at Pharmacy B via home delivery." The input data is the generated prompt text, and the output is the suggested medical services and medication selections. The user receives this suggestion via their device and can make arrangements for medical services and medications as needed.

[0316] (Application example 3)

[0317] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0318] Conventional medical systems do not adequately consider the user's health condition and lifestyle when selecting medications or suggesting meal menus. This can make it difficult for users to receive appropriate medical services and meals, leading to inadequate health management. Furthermore, since available medical services and ingredients vary by region, decisions must be made taking into account information specific to that region. To solve these issues, a system is needed that can make optimal suggestions based on the user's individual circumstances.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0320] In this invention, the server includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for making a decision taking into consideration interactions between over-the-counter medications and prescribed medications, a means for making a decision taking into consideration the state of local medical care and the user's living situation (e.g., elderly, dual-income households, etc.), and a means for proposing an optimal meal menu taking into consideration the user's health condition and living situation. This makes it possible to propose optimal medical services and meal menus according to the user's health condition and living situation.

[0321] "User's health condition" refers to the illnesses, allergies, and poor physical condition that the user currently has.

[0322] "Choosing medicine when feeling unwell" refers to the act of a user selecting an appropriate medicine when they feel unwell.

[0323] "Over-the-counter" refers to medicines that can be purchased without a doctor's prescription.

[0324] "Prescribed medication" refers to medication that is prescribed by a doctor as a result of a medical examination and dispensed by a pharmacy.

[0325] "Drug interactions" refers to the interactions and effects that occur when multiple medications are taken at the same time.

[0326] "Regional medical situation" refers to the state of medical service provision and availability of medical resources in a particular region.

[0327] "User's living situation" refers to factors that affect the user's daily life, such as the user's age, occupation, and home environment.

[0328] "Meal menu" refers to the contents and combination of meals that a user will eat.

[0329] "Ingredients" refers to the raw materials used to make a meal.

[0330] "Suggestion" refers to the act of showing the user the best option.

[0331] To implement this invention, it is necessary to build a system that proposes optimal medical services and meal menus in consideration of the user's health condition and lifestyle. This system includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for making a decision taking into consideration interactions between over-the-counter drugs and prescribed drugs, a means for making a decision taking into consideration the state of local medical care and the user's lifestyle, and a means for proposing optimal meal menus in consideration of the user's health condition and lifestyle.

[0332] System configuration

[0333] Hardware

[0334] Server: A central server for processing and storing data.

[0335] Smartphone: A device that allows users to enter information and receive suggestions.

[0336] Network: The communications infrastructure that connects the server and smartphones.

[0337] software

[0338] Python: A programming language used for data processing and implementing AI models.

[0339] JSON: The data format used to format user and region data.

[0340] Generative AI model: An AI model that takes into account the user's health condition and lifestyle to make optimal suggestions.

[0341] Processing the data

[0342] The server receives input about the user's health condition and lifestyle, and then proposes optimal medical services and meal plans based on this information. Specifically, it performs the following data processing and calculations:

[0343] 1. Entering user data: Users use their smartphones to enter their health status (e.g., diabetes, high blood pressure) and living situation (e.g., dual-income households, elderly people).

[0344] 2. Obtaining regional data: The server obtains information on medical services and available food ingredients in each region.

[0345] 3. Data integration: Integrate user data and regional data, and use generative AI models to suggest optimal medical services and meal menus for users.

[0346] Specific examples

[0347] For example, if the user is a busy, dual-income family with diabetes and high blood pressure, the system will suggest the best menu based on locally available ingredients.

[0348] The user's health conditions are diabetes and high blood pressure, and their living situation is a busy dual-income household. The ingredients available in the area are chicken breast, tofu, and broccoli. Please suggest the optimal meal menu.

[0349] By inputting this prompt into a generative AI model, it is possible to suggest optimal meal plans for the user, thereby supporting health management and improving the user's quality of life.

[0350] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0351] Step 1:

[0352] Users use their smartphones to input their health conditions (e.g., diabetes, high blood pressure) and living situation (e.g., dual-income households, elderly).

[0353] Input: Data about the user's health and living situation.

[0354] Output: The user data is sent to the server.

[0355] Specific operation: The user opens the application on their smartphone and answers questions about their health and lifestyle. The answers are sent to the server in JSON format.

[0356] Step 2:

[0357] The server obtains information on local medical services and available food ingredients.

[0358] Input: Region information.

[0359] Output: The region data is saved on the server.

[0360] Specific operation: The server accesses the local medical database and food ingredient database to obtain information on medical services and available ingredients in the specified area. This data is stored in JSON format.

[0361] Step 3:

[0362] The server integrates user data and local data to generate prompt sentences to input into the generative AI model.

[0363] Input: User data and region data.

[0364] Output: The generated prompt statement.

[0365] Specific operation: The server analyzes user data and local data and generates a prompt to suggest the optimal meal menu based on the user's health condition and living situation. For example, it generates a prompt such as, "The user's health condition is diabetes and high blood pressure, and their living situation is that they are both working and busy. The ingredients available in the area are chicken breast, tofu, and broccoli. Please suggest the optimal meal menu."

[0366] Step 4:

[0367] The server inputs prompts into the generative AI model to generate the optimal meal menu.

[0368] Input: The generated prompt statement.

[0369] Output: Optimal meal menu.

[0370] How it works: The server inputs prompts into the generative AI model, which then generates an optimal meal menu based on the user's health and lifestyle. The generated menu is saved in JSON format on the server.

[0371] Step 5:

[0372] The server sends the generated optimal meal menu to the user's smartphone.

[0373] Enter: your optimal meal plan.

[0374] Output: A meal menu displayed on the user's smartphone.

[0375] Specific operation: The server sends the generated meal menu to the user's smartphone, where the user can check the proposed meal menu through the smartphone application.

[0376] 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.

[0377] "Example 1"

[0378] One embodiment of the present invention provides a system including an emotion engine that recognizes a user's emotions. This system combines an AI that determines the user's health condition and the appropriate medication when the user is feeling unwell with the emotion engine that recognizes the user's emotions. Specifically, when a user feels unwell, the emotion engine recognizes the user's emotional state and provides that information to the AI. The AI ​​then takes that emotional state into account when making a medication selection. For example, if the user is feeling stressed, the AI ​​can take that information into account and select a medication that will help alleviate stress.

[0379] "Example 2"

[0380] Another embodiment of the present invention provides a system in which an emotion engine makes a decision taking into account interactions between over-the-counter medications and prescription medications. Specifically, when a user feels unwell and wants to take a specific over-the-counter medication, the emotion engine recognizes the user's emotional state. This information and information about the over-the-counter medication are provided to an AI, which then makes a decision taking into account interactions between the over-the-counter medication and prescription medication based on that information. For example, if the user is feeling anxious, the AI ​​can take that information into account and select an over-the-counter medication and prescription medication combination that will help alleviate anxiety.

[0381] "Example 3"

[0382] One embodiment of the present invention provides a system including an emotion engine that recognizes a user's emotions. This system combines an AI that determines the user's health condition and the appropriate medication when the user is feeling unwell with the emotion engine that recognizes the user's emotions. Specifically, when a user feels unwell, the emotion engine recognizes the user's emotional state and provides that information to the AI. The AI ​​then takes that emotional state into account when making a medication selection. For example, if the user is feeling stressed, the AI ​​can take that information into account and select a medication that will help alleviate stress.

[0383] The processing flow of each embodiment will be described below.

[0384] "Example 1"

[0385] Step 1: (Step 1)

[0386] When a user feels unwell, the emotion engine recognizes the user's emotional state. Step 2:

[0387] The emotion engine provides AI with information on the emotional state recognized.

[0388] Step 3: (Step 3)

[0389] The AI ​​will take into account the emotional state information provided to determine medication selection. For example, if the user is feeling stressed, the AI ​​will take that information into account and select medication that will help reduce stress.

[0390] "Example 2"

[0391] Step 1: (Step 1)

[0392] When a user feels unwell and wants to take a particular over-the-counter medication, the emotion engine recognizes the user's emotional state.

[0393] Step 2: (Step 2)

[0394] The emotion engine provides the AI ​​with information on the emotional state recognized and information on over-the-counter medications.

[0395] Step 3: (Step 3)

[0396] The AI ​​makes decisions based on the information provided, taking into account interactions between over-the-counter and prescription medications. For example, if the user is feeling anxious, the AI ​​will take that information into account and select combinations of over-the-counter and prescription medications that will help reduce anxiety.

[0397] Example 1

[0398] Next, a description will be given of Example 1 of Form 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."

[0399] Conventional drug selection systems select drugs based on the user's health condition and information on when they are feeling unwell, but because they do not take the user's emotional state into account, they have the problem of not being able to select the appropriate drug when stress or emotional fluctuations affect the drug selection. There are also issues with the system being difficult to make decisions that take into account interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[0400] 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.

[0401] In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medication when the user is feeling unwell, emotion recognition means for recognizing the user's emotions, means for providing the artificial intelligence means with emotion information recognized by the emotion recognition means, and means for selecting an appropriate medication based on the emotion information and physical condition information provided by the artificial intelligence means. This makes it possible to select an appropriate medication taking into consideration the user's emotional state, and also makes it possible to make decisions that take into consideration interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[0402] "User" refers to an individual who uses the system to select medications for their own health condition or when they are feeling unwell.

[0403] "Health status" refers to information that indicates the state of a user's physical and mental health.

[0404] "Illness" refers to a state of physical discomfort or abnormality felt by the user.

[0405] "Medicine" refers to pharmaceutical products used to improve the health or well-being of the User.

[0406] "Artificial intelligence means" refers to machine learning and data analysis techniques used to determine a user's health condition and the choice of medication when they are unwell.

[0407] "Emotion recognition means" refers to technology or devices for recognizing the emotional state of a user.

[0408] "Emotion information" refers to data relating to the user's emotional state as recognized by the emotion recognition means.

[0409] "Physical condition information" refers to information about the health condition or poor physical condition entered by the user.

[0410] "Over-the-counter" refers to medicines that can be purchased without a prescription.

[0411] "Prescription drugs" refers to medicines that require a doctor's prescription.

[0412] "Regional medical situation" refers to the state of medical service provision and the state of medical resource utilization in the area where the user resides.

[0413] "Life situation" refers to information about the user's living environment and lifestyle habits.

[0414] MODE FOR CARRYING OUT THE INVENTION

[0415] This invention is a system that combines an artificial intelligence means that determines the user's health condition and the selection of medicine when the user is feeling unwell with an emotion recognition means that recognizes the user's emotions. A specific embodiment of this system will be described below.

[0416] System configuration

[0417] This system consists of a terminal used by the user, a server, artificial intelligence means, and emotion recognition means.

[0418] 1. Terminal

[0419] Users use devices such as smartphones and tablets to input information about their own health status and physical condition. A dedicated application is installed on the device, and users input information through this application.

[0420] 2. Server

[0421] The server receives the information sent by the user and processes it by integrating the emotion recognition means and the artificial intelligence means. The server communicates with the terminal via an internet connection.

[0422] 3. Artificial Intelligence Means

[0423] Artificial intelligence tools are used to determine the user's health condition and the choice of medication when they are unwell. Specifically, models built using machine learning frameworks (e.g., TensorFlow and PyTorch) recommend the most appropriate medication based on past data.

[0424] 4. Emotion recognition means

[0425] Emotion recognition means are used to recognize the user's emotional state, for example, by using software that analyzes the user's facial expressions and tone of voice (e.g., Emotion API) to recognize the user's emotional state (e.g., stress, sadness, joy).

[0426] System Operation

[0427] When a user feels unwell, they use a terminal to input information such as "I have a headache" or "I feel stressed." The terminal then sends this information to a server. The server then activates an emotion recognition means based on the received information and recognizes the user's emotional state. The emotion information recognized by the emotion recognition means is then sent to the server. The server then provides the emotion information and physical condition information to an artificial intelligence means, which then selects an appropriate medication based on this information.

[0428] Specific examples

[0429] For example, if the user inputs "I have a headache" or "I feel stressed," the device sends this information to the server. The server activates the emotion recognition means and recognizes the user's stress state. The server provides the recognized emotion information and physical condition information to the artificial intelligence means, which then selects a medication that has the effect of reducing stress (e.g., acetaminophen and herbal tea with a relaxing effect). The server sends this medication information to the terminal, and the terminal displays detailed information about the medication to the user.

[0430] Prompt Sentence Examples

[0431] Examples of prompts to input to a generative AI model include:

[0432] "If a user complains of headaches and stress, provide them with information to help them choose the right medication."

[0433] The system allows users to choose more appropriate medications based on their own health and emotional state.

[0434] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0435] Step 1:

[0436] The user inputs physical condition information.

[0437] The user uses the device to input their own physical condition information (e.g., headache, stress) into a dedicated application. The input information is saved in text format on the device.

[0438] Step 2:

[0439] The terminal sends the input information to the server.

[0440] The terminal encrypts the health information entered by the user and sends it to the server via the Internet. The input data is health information in text format. The server stores the received health information in a database.

[0441] Step 3:

[0442] The server activates the emotion recognition means to recognize the emotion of the user.

[0443] The server activates the emotion recognition means based on the received physical condition information. The emotion recognition means analyzes the user's facial expressions and tone of voice using the device's camera and microphone. The analyzed data is sent to the server as emotion information.

[0444] Step 4:

[0445] The server provides the emotional information and physical condition information to the artificial intelligence means.

[0446] The server provides the emotional information received from the emotion recognition means and the physical condition information input by the user to the artificial intelligence means. The provided data is a combination of the emotional information and the physical condition information.

[0447] Step 5:

[0448] Artificial intelligence means select the appropriate medication.

[0449] The artificial intelligence means uses a machine learning model to select the appropriate medication based on the provided emotional and physical condition information. For example, a model built using TensorFlow or PyTorch can recommend the optimal medication based on past data. The selected medication information is sent to the server.

[0450] Step 6:

[0451] The server transmits the selected drug information to the terminal.

[0452] The server encrypts the drug information received from the artificial intelligence means and transmits it to the terminal via the Internet. The transmitted data includes detailed information about the drug (e.g., name of the drug, its effects, and side effects).

[0453] Step 7:

[0454] The terminal displays the medication information to the user.

[0455] The terminal displays the medication information received from the server to the user, who can then check detailed information about the recommended medication on the application screen.

[0456] (Application example 1)

[0457] Next, a description will be given of Application Example 1 of Embodiment 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."

[0458] Conventional drug selection systems do not take into account the user's emotional state when determining the user's health condition or the drug to be selected when feeling unwell. This means that they are unable to select the appropriate drug when emotional states such as stress or fatigue affect the drug selection. Furthermore, there is a lack of information to quickly obtain the suggested drugs, which means it takes a long time for the user to obtain the drug.

[0459] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medicine when the user is feeling unwell, emotion recognition means for recognizing the user's emotional state, means for the artificial intelligence means to select medicine based on the emotional state obtained from the emotion recognition means, and means for providing inventory information of nearby physical stores based on the suggested medicine. This makes it possible to select an appropriate medicine taking into account the user's emotional state, and also makes it possible to provide information for quickly obtaining the suggested medicine.

[0460] "User" refers to an individual who uses the system.

[0461] "Health status" refers to information indicating the physical health status of a user.

[0462] "Illness" refers to physical discomfort or symptoms experienced by the user.

[0463] "Medicine" refers to pharmaceutical products used to improve the health or well-being of the User.

[0464] "Artificial intelligence means" refers to algorithms and programs that determine the user's health condition and the choice of medication when feeling unwell.

[0465] "Emotion recognition means" refers to an algorithm or program for recognizing the user's emotional state.

[0466] "Emotional state" refers to emotions such as stress, fatigue, and impatience felt by the user.

[0467] "Brick and mortar store" refers to a physical store where users can purchase medications.

[0468] "Inventory information" refers to information showing the stock status of medicines at physical stores.

[0469] "Regional medical situation" refers to the state of medical services provided in the area where the user resides.

[0470] "Life situation" refers to information about the user's life, such as age, sex, occupation, and home environment.

[0471] A system for implementing this invention includes artificial intelligence means for determining the user's health condition and the selection of medication when the user is feeling unwell, emotion recognition means for recognizing the user's emotional state, means for the artificial intelligence means to select medication based on the emotional state obtained from the emotion recognition means, and means for providing inventory information of nearby physical stores based on the suggested medication.

[0472] System configuration

[0473] 1. Hardware

[0474] Smartphone: A device that allows users to enter information and receive results.

[0475] Server: A device for executing artificial intelligence means and emotion recognition means.

[0476] 2. Software

[0477] EmotionEngine: An emotion recognition engine for recognizing the user's emotional state.

[0478] HealthAI: Artificial intelligence to determine a user's health condition and the appropriate medication when they are unwell.

[0479] requests: A Python library for sending API requests.

[0480] Data processing and calculation

[0481] 1. User Input

[0482] Users use their smartphones to enter information such as symptoms, existing prescription medications, age, and gender.

[0483] 2. Emotion recognition

[0484] The emotion recognition means (Emotion Engine) recognizes the user's emotional state, for example, determining whether the user is feeling stressed.

[0485] 3. Medication selection

[0486] The artificial intelligence (HealthAI) will suggest appropriate medications based on the user's input and emotional state. For example, if the user has a headache and is stressed, it will suggest painkillers and medications that reduce stress.

[0487] 4. Providing inventory information

[0488] Based on the suggested medications, inventory information from nearby physical stores is obtained via API and provided to the user.

[0489] Specific examples

[0490] If a user has a "headache" and "stress," the app will suggest painkillers and stress-reducing medications and provide information on stock availability at nearby pharmacies.

[0491] Prompt Sentence Examples

[0492] When a user feels unwell, they input information such as their symptoms, existing prescription medications, age, and gender. An emotion engine is used to recognize the user's emotional state, and the AI ​​suggests appropriate medications based on the input information and emotional state. The suggested medications are listed as available at nearby physical stores, and stock information is also provided.

[0493] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0494] Step 1:

[0495] The user uses a smartphone to input information such as symptoms, current prescription medications, age, and gender. The input information is then sent to the server. The input data includes detailed information about the user's health condition and physical condition.

[0496] Step 2:

[0497] The server uses an emotion recognition means (Emotion Engine) to recognize the user's emotional state. The emotion recognition means analyzes data such as the user's input information, voice, and facial expressions, and identifies emotional states such as stress, fatigue, and impatience. The user's emotional state is obtained as an output.

[0498] Step 3:

[0499] The server uses artificial intelligence (HealthAI) to suggest appropriate medications based on the user's input information and emotional state. The artificial intelligence selects the most appropriate medication by taking into account the user's symptoms, existing prescription medications, age, gender, and emotional state. The output is a list of suggested medications.

[0500] Step 4:

[0501] The server retrieves inventory information of nearby physical stores based on the suggested medications through an API. The server then uses the list of suggested medications to search for inventory information at physical stores and generate a list of stores that have the medications in stock. The output is a list of stores with inventory information.

[0502] Step 5:

[0503] The server then sends the acquired inventory information to the user's smartphone. The user can then check the suggested medications and their inventory information on their smartphone. This allows the user to quickly obtain medications from a nearby physical store.

[0504] Example 2

[0505] Next, a description will be given of Example 2 of Form 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."

[0506] Conventional drug selection systems have difficulty selecting appropriate medications because they are unable to consider the user's emotional state or interactions between over-the-counter and prescription drugs. Furthermore, they are unable to make decisions that take into account the user's living situation or the state of local medical care, making it difficult to select the optimal medication for each individual user.

[0507] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0508] In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medication when the user is feeling unwell, emotion engine means for recognizing the user's emotional state, and means for providing information from the emotion engine means to the artificial intelligence means, thereby enabling the selection of appropriate medication taking into consideration the user's emotional state and interactions between over-the-counter and prescription medications.

[0509] "User" refers to an individual who uses the system to select medications for their own health condition or when they are feeling unwell.

[0510] "Health Status" refers to the state of a user's physical and mental health.

[0511] "Illness" refers to physical or mental discomfort experienced by the user.

[0512] "Medication selection" refers to the act of a user selecting a medication to take.

[0513] "Artificial intelligence means" refers to artificial intelligence technology used to determine the user's health condition and the choice of medication when feeling unwell.

[0514] "Emotion engine means" refers to technology for recognizing the user's emotional state.

[0515] The "means for providing information" refers to a technology for transmitting information obtained from the emotion engine means to the artificial intelligence means.

[0516] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[0517] "Prescription drugs" refers to drugs that require a doctor's prescription.

[0518] Drug interactions refer to interactions that occur when multiple medications are taken at the same time.

[0519] "Regional medical situation" refers to the state of medical care provided in the area where the user resides.

[0520] "Life situation" refers to the environment and conditions in the user's daily life.

[0521] This invention is a system for assisting a user in selecting medications for their health condition or when they are feeling unwell. The system includes artificial intelligence means for recognizing the user's emotional state and selecting appropriate medications taking into account interactions between over-the-counter and prescription medications.

[0522] Hardware and software used

[0523] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0524] Software: Emotion engine, AI model, Database Management System (DBMS)

[0525] Program processing explanation

[0526] 1. User Input

[0527] The user uses the terminal to input his / her physical condition and emotional state.

[0528] Example: A user types, "I have a headache, but I've been feeling very anxious lately."

[0529] 2. Operation of the Emotion Engine

[0530] The terminal transmits the user's input information to the emotion engine, which analyzes the user's input information and recognizes the user's emotional state.

[0531] Example: An emotion engine analyzes a user's input and determines that they are feeling "anxiety."

[0532] 3. Data transmission

[0533] The emotion engine sends the recognized emotional state and over-the-counter medication information to the server, which receives this information and provides it to the AI ​​model.

[0534] Example: An emotion engine sends the emotional state "anxiety" and information about over-the-counter medications to a server.

[0535] 4. AI model judgment

[0536] The server uses an AI model to make decisions that take into account interactions between over-the-counter and prescription drugs. The AI ​​model retrieves information about the prescription drugs the user is currently taking from a database and evaluates interactions with over-the-counter drugs.

[0537] Example: A server uses an AI model to select over-the-counter medication B that does not interact with prescription medication A that a user is taking.

[0538] 5. Providing results

[0539] The server sends the AI ​​model's results to the device, which then presents the user with appropriate over-the-counter drug options.

[0540] Example: The server sends the result "Over-the-counter drug B is recommended" to the terminal, and the terminal displays that information to the user.

[0541] Prompt Sentence Examples

[0542] "If a user is experiencing headaches and anxiety, explain how you would recommend an over-the-counter medication that will not interact with prescription drug A that the user is taking."

[0543] The system helps users select safe and effective over-the-counter medications based on their emotional state and current medication information.

[0544] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0545] Step 1:

[0546] The user uses the terminal to input his / her physical condition and emotional state.

[0547] Input: Information about the user's physical and emotional state (e.g., "I have a headache, and I've been feeling very anxious lately")

[0548] Output: The user's input is saved to the device.

[0549] Specific operation: The user opens the application on the device, enters their physical and emotional state in the text box, and presses the send button.

[0550] Step 2:

[0551] The terminal transmits the user's input information to the emotion engine, which analyzes the user's input information and recognizes the user's emotional state.

[0552] Input: User input (e.g., "I have a headache, but I've been feeling very anxious lately.")

[0553] Output: Emotional state recognition by the emotion engine (e.g., "anxiety")

[0554] Specific operation: The device sends the user's input information to the emotion engine, which then analyzes the user's emotional state using natural language processing technology.

[0555] Step 3:

[0556] The emotion engine sends the recognized emotional state and over-the-counter medication information to the server, which receives this information and provides it to the AI ​​model.

[0557] Input: Emotional state recognition results (e.g., "anxiety") from the emotion engine and over-the-counter medication information

[0558] Output: Emotional state and over-the-counter medication information sent to the server

[0559] How it works: The emotion engine sends the recognition results and over-the-counter drug information to the server, which then inputs this information into the AI ​​model.

[0560] Step 4:

[0561] The server uses an AI model to make decisions that take into account interactions between over-the-counter and prescription drugs. The AI ​​model retrieves information about the prescription drugs the user is currently taking from a database and evaluates interactions with over-the-counter drugs.

[0562] Input: Emotional state, over-the-counter medication information, and information about prescription medications the user is currently taking

[0563] Output: Judgment result taking into account interactions between over-the-counter and prescription drugs (e.g., "Over-the-counter drug B is recommended")

[0564] How it works: The server uses an AI model to retrieve prescription drug information from a database and evaluate interactions with over-the-counter drugs.

[0565] Step 5:

[0566] The server sends the AI ​​model's results to the device, which then presents the user with appropriate over-the-counter drug options.

[0567] Input: The AI ​​model's judgment (e.g., "Over-the-counter drug B is recommended")

[0568] Output: Over-the-counter medication options presented to the user

[0569] Specific operation: The server sends the judgment result to the terminal, and the terminal displays the information to the user.

[0570] (Application example 2)

[0571] Next, a description will be given of Application Example 2 of Form 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."

[0572] Conventional drug selection systems have difficulty selecting appropriate medications because they are unable to consider the user's emotional state or interactions between over-the-counter and prescription drugs. Furthermore, they are unable to make decisions that take into account the user's living situation or the state of local medical care, making it difficult to select the optimal medication for each individual user.

[0573] 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.

[0574] In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medications when the user is feeling unwell, emotion engine means for recognizing the user's emotional state, and artificial intelligence means for making a decision taking into account interactions between over-the-counter and prescription drugs based on information obtained from the emotion engine means and over-the-counter drug information. This makes it possible to select appropriate medications taking into account the user's emotional state and interactions between over-the-counter and prescription drugs. Furthermore, the server can also make decisions taking into account the state of local medical care and the user's living situation, enabling the selection of medications that are optimal for each individual user.

[0575] "User's health condition" refers to information such as the user's current physical condition, medical history, and past illnesses.

[0576] "When feeling unwell" refers to a state in which the user is experiencing symptoms such as a cold, headache, or stomachache.

[0577] "Artificial intelligence means for determining medication selection" refers to a system that includes algorithms or programs for selecting appropriate medications when a user is experiencing a health condition or is feeling unwell.

[0578] "Emotion engine means" refers to a system that includes algorithms and programs for recognizing a user's emotional state and providing that information to other systems.

[0579] "Over-the-counter" refers to medicines that can be purchased without a doctor's prescription.

[0580] "Prescription drugs" refers to drugs that require a doctor's prescription.

[0581] "Artificial intelligence means for making decisions that take into account drug interactions" refers to systems that include algorithms or programs for evaluating interactions between over-the-counter and prescription drugs and selecting appropriate drug combinations.

[0582] "Regional medical situation" refers to the number and quality of medical facilities in a particular area, as well as the state of medical service provision.

[0583] "User's living situation" refers to information such as the user's age, occupation, and home environment.

[0584] A system for implementing this invention includes artificial intelligence means for determining the user's health condition and the selection of medication when feeling unwell, emotion engine means for recognizing the user's emotional state, and artificial intelligence means for making decisions taking into consideration interactions between over-the-counter drugs and prescription drugs.

[0585] System configuration

[0586] 1. Artificial intelligence means to determine the user's health condition and choice of medicine when feeling unwell:

[0587] This means includes algorithms and programs for selecting appropriate medications when the user is in poor health or is feeling unwell. Specifically, it suggests the most appropriate medication based on the symptoms and medical history data entered by the user.

[0588] 2. Emotion engine means for recognizing the user's emotional state:

[0589] This method uses a smartphone camera to capture a user's facial image and recognizes the user's emotional state using an emotion recognition algorithm (e.g., EmotionRecognizer). The recognized emotional state influences the selection of medication.

[0590] 3. Artificial intelligence methods for making decisions regarding over-the-counter and prescription drug interactions:

[0591] This tool uses a drug interaction checking library such as DrugInteractionChecker to evaluate interactions between over-the-counter and prescription drugs, thereby suggesting safe drug combinations for users.

[0592] Hardware and software used

[0593] Hardware: Smartphone camera

[0594] Software: EmotionRecognizer (emotion recognition library), DrugInteractionChecker (drug interaction checking library)

[0595] Processing flow

[0596] 1. User Emotion Recognition:

[0597] A user takes a picture of their face using the smartphone camera. The emotion engine inputs this image into the EmotionRecognizer and recognizes the user's emotional state.

[0598] 2. Drug Interaction Check:

[0599] The user enters prescription and over-the-counter drug information into DrugInteractionChecker to check for interactions.

[0600] 3. Proposal generation:

[0601] Based on the recognized emotional state and drug interaction information, the system suggests the optimal drug combination for the user.

[0602] Specific examples

[0603] User image: 'path_to_user_image.jpg'

[0604] Prescription drug: 'DrugA'

[0605] Over-the-counter drug: 'DrugB'

[0606] Prompt Sentence Examples

[0607] Enter the user image: path_to_user_image.jpg

[0608] Enter the name of your prescription drug: DrugA

[0609] Enter the name of your over-the-counter medication: DrugB

[0610] The system allows users to select the best over-the-counter medication for their mood and taking into account interactions with existing prescription medications, making medication choices safer and more effective.

[0611] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0612] Step 1:

[0613] The user takes a picture of their face using the smartphone camera.

[0614] Input: User's face image

[0615] Output: Face image data

[0616] Specific operation: The user activates the smartphone camera and takes a picture of their face. The captured image is saved in the smartphone.

[0617] Step 2:

[0618] The device inputs facial image data into EmotionRecognizer to recognize the user's emotional state.

[0619] Input: Facial image data

[0620] Output: Emotional state data (e.g., anxiety, joy, sadness, etc.)

[0621] What happens: The device sends the stored facial image data to the EmotionRecognizer library and runs the emotion recognition algorithm, which analyzes facial features and identifies the user's emotional state.

[0622] Step 3:

[0623] The user enters prescription and over-the-counter drug information into the terminal.

[0624] Input: Name of prescription drug, name of over-the-counter drug

[0625] Output: Prescription and over-the-counter drug information data

[0626] How it works: Using a smartphone application, users input the names of prescription medications they are currently taking and over-the-counter medications they are considering purchasing. The information is then stored on the device.

[0627] Step 4:

[0628] The terminal inputs prescription and over-the-counter drug information data into DrugInteractionChecker to check for interactions.

[0629] Input: Prescription and over-the-counter drug information data

[0630] Output: Interaction check result (e.g., safe, dangerous, caution required, etc.)

[0631] How it works: The device sends stored prescription and over-the-counter drug information data to the DrugInteractionChecker library and runs the drug interaction checking algorithm, which analyzes the drug's ingredients and mechanism of action and assesses the risk of drug interactions.

[0632] Step 5:

[0633] The server suggests the optimal drug combination for the user based on emotional state data and interaction check results.

[0634] Input: Emotional state data, interaction check results

[0635] Output: Drug combination suggestions (e.g., over-the-counter drug B is safe and effective in reducing anxiety)

[0636] Specific operation: The server integrates the emotional state data and the interaction check results, and then uses a generative AI model to propose optimal drug combinations, taking into account the user's emotional state and the risk of drug interactions. The proposals are displayed on the user's smartphone.

[0637] Step 6:

[0638] The user reviews the suggested drug combination and purchases over-the-counter medications as needed.

[0639] Input: Drug combination suggestions

[0640] Output: Purchase decision

[0641] Specific operation: The user checks the drug combination suggestions displayed on the smartphone and decides whether to purchase over-the-counter drugs based on the suggestions. If the user decides to purchase, they go to a pharmacy or drugstore to purchase the over-the-counter drug.

[0642] Example 3

[0643] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0644] Conventional drug selection systems were unable to fully consider the local medical situation, the user's lifestyle, or even the user's emotional state when selecting a drug for a user's health condition or when feeling unwell. This made it difficult to recommend the most suitable drug for the user, and in some cases, the system was unable to provide effective treatment. Furthermore, the system was also unable to adequately consider interactions between over-the-counter drugs and prescribed drugs. This led to the issue of inadequate health management for users.

[0645] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0646] In this invention, the server includes an artificial intelligence means for determining the user's health condition and the selection of medicine when the user is feeling unwell, an emotion analysis means for recognizing the user's emotional state, a means for acquiring medical information from a local medical database, a means for collecting data on the user's living situation, a means for selecting the most appropriate medicine based on the collected data, and a means for presenting information on the selected medicine to the user. This makes it possible to select the most appropriate medicine taking into consideration the user's health condition, emotional state, local medical situation, and living situation in a comprehensive manner.

[0647] "Artificial intelligence means" refers to algorithms and models used to determine the user's health condition and the choice of medication when feeling unwell.

[0648] "Emotion analysis means" refers to technology for analyzing a user's facial expressions and voice data to recognize the user's emotional state.

[0649] A "regional medical database" refers to a database containing information about medical services and types of medications in a particular region.

[0650] "Life situation data" refers to information about the user's life, such as the user's age, occupation, and lifestyle.

[0651] "Means for selecting the optimal medication" refers to algorithms or models that use collected data to select the medication that is most suitable for the user.

[0652] "Means for presenting drug information" refers to technology for providing users with information such as the name, effects, and side effects of the selected drug.

[0653] This invention is a system for optimizing the selection of medications for a user's health condition or when they are feeling unwell. The system includes an artificial intelligence means, an emotion analysis means, a regional medical database, a living situation data collection means, a means for selecting the most appropriate medication, and a means for presenting medication information.

[0654] First, when a user feels unwell, they input that information through a smartphone app. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice using emotion analysis techniques to recognize the user's emotional state. This emotion analysis is performed using machine learning libraries such as TensorFlow and PyTorch, which use Python.

[0655] The server then retrieves medical information from a regional medical database based on the user's location. The regional medical database contains information about medical services and medications available in a specific area. Additionally, the server collects the user's lifestyle data, such as age, occupation, and lifestyle habits.

[0656] Based on this data, the server uses artificial intelligence means to select the most suitable medication. The artificial intelligence means uses the collected data as input and runs algorithms and models to select the most suitable medication for the user. This process can use generative AI models.

[0657] Finally, the server sends information about the selected medication to the device, which then presents the information to the user, such as displaying the medication name, effects, and side effects on the smartphone screen.

[0658] As a concrete example, consider the case where a user feels they have a cold and types "I have a cold" into a smartphone app. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice and recognizes that they are feeling stressed. The server retrieves a list of available cold medications from a medical database for the user's area, and further confirms that the user is elderly and leads a busy dual-income household. This data is input into an AI model to select a cold medication that is effective in reducing stress. Information about the selected medication is sent to the device, which then displays information such as the name of the medication, its effects, and side effects on the smartphone screen.

[0659] Examples of prompts to input to a generative AI model include:

[0660] "When the user has a cold, the emotion engine recognizes that they are feeling stressed. The user is elderly and both partners work, leading busy lives. We retrieve a list of available cold medications from the local medical service database. Based on this information, we select the most appropriate cold medication for the user."

[0661] In this way, this system enables the selection of the most appropriate medicine by comprehensively considering the user's health condition, emotional state, local medical situation, and living situation. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0662] Step 1:

[0663] The user feels unwell

[0664] The user inputs information about their poor health, such as "I have a cold," through a smartphone app. This input data indicates the user's health condition and serves as the initial input for the system.

[0665] Step 2:

[0666] The device analyzes the user's emotional state using emotion analysis means.

[0667] The device uses the smartphone's camera and microphone to collect the user's facial expressions and voice. The collected data is input into an emotion analysis means to recognize the user's emotional state (e.g., stress, anxiety, joy, etc.). This analysis uses machine learning libraries such as TensorFlow and PyTorch. The output is data on the user's emotional state.

[0668] Step 3:

[0669] The server retrieves medical information from the local medical database.

[0670] The server accesses a local medical database based on the user's area of ​​residence to obtain information on medical services and medications available in the area. The user's area of ​​residence information is used as input, and a list of medications available in the area is obtained as output.

[0671] Step 4:

[0672] The server collects data on the user's living situation.

[0673] The server collects the user's life situation data, such as age, occupation, and lifestyle. This data is obtained from the user's profile information and past input data. The user's profile information is used as input, and the user's life situation data is obtained as output.

[0674] Step 5:

[0675] The server inputs the data into the AI ​​model and selects the optimal medication.

[0676] The server inputs the collected local medical information, the user's living situation data, and emotional state data into the AI ​​model. Based on this data, the AI ​​model runs an algorithm to select the most appropriate medication for the user. Local medical information, living situation data, and emotional state data are used as input, and information on the most appropriate medication is obtained as output.

[0677] Step 6:

[0678] The server sends the selected drug information to the terminal.

[0679] The server sends the information of the medication selected by the AI ​​model to the terminal. The information of the optimal medication is used as input, and the information of the medication sent to the terminal is obtained as output.

[0680] Step 7:

[0681] The device presents medication information to the user.

[0682] The device presents the drug information received from the server to the user. Specifically, information such as the name of the drug, its effects, and side effects is displayed on the smartphone screen. The drug information sent from the server is used as input, and the drug information presented to the user is obtained as output.

[0683] (Application example 3)

[0684] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0685] Conventional drug selection systems select drugs based on the user's health condition and information on when they are feeling unwell, but they are unable to consider the user's emotional state, local medical situation, or living situation, making it difficult to select the most appropriate drug.There was also a need for a system that could obtain information on locally available medical services and drugs in real time when the user visits a pharmacy, and select drugs that take their emotional state into consideration.

[0686] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0687] In this invention, the server includes a means for determining the user's health condition and the selection of medicine when feeling unwell, a means for recognizing the user's emotions, a means for making a decision taking into consideration the state of local medical care and the user's living situation, a means for acquiring information on medical services and medicines available in the area based on the user's location information, and a means for determining the selection of medicine based on the user's emotional state. This enables the selection of the optimal medicine taking into consideration the user's health condition, emotional state, and local medical information in a comprehensive manner.

[0688] "User's health condition" refers to the physical condition of the user, such as the illnesses or symptoms the user currently has, and whether the user is in good or bad physical condition.

[0689] "Means for determining the appropriate medication when feeling unwell" refers to an algorithm or system that selects the appropriate medication for the symptoms when a user feels unwell.

[0690] "Means for recognizing a user's emotions" refers to technology or devices that analyze a user's facial expressions, tone of voice, etc., and identify their emotional state.

[0691] "Regional medical situation" refers to the state of medical service provision in a particular area, the types of medicines available, the number of medical facilities, etc.

[0692] "User's living situation" refers to information related to the user's daily life, such as the user's age, occupation, lifestyle, and home environment.

[0693] "Means of obtaining information on medical services and medications available in the area based on location information" refers to technologies and systems that identify the user's current location and obtain information on medical services and medications available in that area.

[0694] "Means for determining medication selection based on the user's emotional state" refers to an algorithm or system that takes into account the user's emotional state and selects medication appropriate for that state.

[0695] The following system configuration will be described as an embodiment of the present invention.

[0696] System Configuration

[0697] This system includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for recognizing the user's emotions, a means for making decisions taking into account the local medical situation and the user's living situation, a means for obtaining information on medical services and medications available in the area based on the user's location information, and a means for determining the choice of medication based on the user's emotional state.

[0698] Hardware and software used

[0699] Smartphone: Used for user input and emotion recognition.

[0700] EmotionEngine: Software for recognizing user emotions.

[0701] HealthcareAI: AI that suggests medications based on the user's health and emotional state.

[0702] API: External service for obtaining local medical information.

[0703] Data processing and calculation

[0704] User health status input

[0705] Users can input their current health condition and symptoms via their smartphone, for example, specific symptoms such as headache or fatigue.

[0706] emotion recognition

[0707] The EmotionEngine analyzes images taken with a smartphone camera and recognizes the user's emotional state. For example, if the user is feeling stressed, that information will be recognized.

[0708] Obtaining local medical information

[0709] The API is called based on the user's location information to obtain information on medical services and medications available in the area. For example, if a user lives in Tokyo, information on medications available in Tokyo will be obtained.

[0710] Medication suggestions

[0711] Healthcare AI takes into account health status, emotional state, and local medical information to recommend the most appropriate medication. For example, if a user has a headache, fatigue, and is also feeling stressed, a medication with a stress-reducing effect will be suggested.

[0712] Specific examples

[0713] If the user is a 45-year-old busy person living in Tokyo who is experiencing headaches and fatigue, the emotion engine will recognize from an image taken with a smartphone camera that the person is feeling stressed. Based on local medical information, the AI ​​will suggest medications available in Tokyo that are effective in reducing stress.

[0714] Prompt Sentence Examples

[0715] If the user is a 45-year-old busy person living in Tokyo who is experiencing headaches and fatigue, the emotion engine will recognize from an image taken with a smartphone camera that the person is feeling stressed. Based on local medical information, the AI ​​will suggest medications available in Tokyo that are effective in reducing stress.

[0716] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0717] Step 1:

[0718] Users use their smartphones to input their current health status and symptoms. The input data includes specific symptoms (e.g., headache, fatigue), age, lifestyle habits, etc. This allows information about the user's health status to be collected.

[0719] Step 2:

[0720] The device uses the smartphone camera to take a picture of the user's face and sends the image to the Emotion Engine, which analyzes the image and recognizes the user's emotional state (e.g., stress, joy, sadness). The recognized emotional state is output.

[0721] Step 3:

[0722] The server acquires the user's location information and calls the local medical information API based on that location information. The API acquires information on medical services and medications available in the user's current location, and outputs the acquired local medical information.

[0723] Step 4:

[0724] The server integrates the user's health condition, emotional state, and local medical information and sends it to Healthcare AI. Healthcare AI processes and calculates this data to select the most suitable medication for the user, and outputs the most suitable medication recommendation.

[0725] Step 5:

[0726] The device displays the medication suggestions received from the server to the user, who can then check the suggested medication information and purchase it at a pharmacy if necessary. This allows the user to select the medication that best suits their health and emotional state.

[0727] 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.

[0728] 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> ) 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.

[0729] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0730] 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.

[0731] [Second embodiment]

[0732] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0733] 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.

[0734] 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).

[0735] 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.

[0736] 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.

[0737] 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).

[0738] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0744] "Example 1"

[0745] One embodiment of the present invention is a system that includes AI that determines the user's health condition and the appropriate medication to use when they are feeling unwell. In this system, the AI ​​determines the appropriate medication to use based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.).

[0746] "Example 2"

[0747] Additionally, the AI ​​takes into account interactions between over-the-counter and prescription medications. For example, if a user is already taking a specific prescription medication, the AI ​​will avoid over-the-counter medications that may interact with the prescription medication.

[0748] "Example 3"

[0749] The AI ​​also makes decisions that take into account the state of local medical care and the user's lifestyle. For example, if the medical services and types of medicine available vary by region, the AI ​​will make decisions based on that information. The AI ​​will also suggest medication options that are appropriate for the user's lifestyle, such as if the user is elderly or if both partners are busy working.

[0750] The processing flow of each embodiment will be described below.

[0751] "Example 1"

[0752] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.).

[0753] Step 2: Based on the information received, the AI ​​determines the appropriate medication.

[0754] Step 3: Present the AI's decision to the user.

[0755] "Example 2"

[0756] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.) and over-the-counter medication information.

[0757] Step 2: Based on the information received, the AI ​​makes a decision taking into account interactions between over-the-counter and prescribed medications.

[0758] Step 3: Present the AI's decision to the user.

[0759] "Example 3"

[0760] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.), the local medical situation, and the user's living situation.

[0761] Step 2: Based on the information received, the AI ​​makes a decision taking into account the local medical situation and the user's living situation.

[0762] Step 3: Present the AI's decision to the user.

[0763] Example 1

[0764] Next, a description will be given of Example 1 of Form 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."

[0765] Conventional drug selection systems have difficulty selecting the appropriate drug based on the user's health condition or when feeling unwell, and a particular issue is that they do not take into account interactions between over-the-counter and prescription drugs, the state of local medical care, or the user's living situation. This increases the risk that users will select the wrong drug, so a more accurate drug selection system is needed.

[0766] 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.

[0767] In this invention, the server includes means for a user to input information about their health condition or poor physical condition, means for a terminal to send the input information to the server, means for the server to receive the input information and store it in a database, means for the server to input the stored data into an AI model, means for the AI ​​model to determine an appropriate medication, means for the server to receive the results of the AI ​​model and return them to the user, and means for the user to check the results via the terminal. This makes it possible to select an appropriate medication for a user's health condition or poor physical condition, and enables highly accurate judgments that take into account interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[0768] "User" refers to an individual who uses the system to input information about their health or ill health.

[0769] "Terminal" refers to a device through which a user inputs information and communicates with a server. Examples include smartphones and personal computers.

[0770] "Server" refers to a central computer system that receives and processes information submitted by users.

[0771] "Database" refers to a system that allows a server to store information received from users. Examples include MySQL and PostgreSQL.

[0772] An "AI model" is an algorithm built using a machine learning framework that determines appropriate medication based on a user's health condition or physical condition.

[0773] "Health Condition" refers to a user's current physical condition or symptoms. Examples include headache and fever.

[0774] "Illness" refers to any physical discomfort or abnormality experienced by the user. Examples include nausea and fatigue.

[0775] "Input Information" refers to data provided by the User to the System via the Terminal, including, for example, symptoms, existing prescription medications, age, and gender.

[0776] "Appropriate medication" refers to the most effective and safe medication that the AI ​​model selects based on the user's health condition or condition.

[0777] "Results" refers to the information about the appropriate medication determined by the AI ​​model and the data returned to the user.

[0778] This invention is a system for selecting appropriate medications for a user's health condition or when they are feeling unwell. This system uses an AI model to determine the appropriate medication based on information entered by the user and returns the results to the user.

[0779] First, the user uses a device such as a smartphone or computer to enter information about their health condition or physical condition into the system's input form. For example, they enter information such as "I have a headache," "My current prescription is aspirin," "I'm 30 years old," and "I'm male."

[0780] The terminal then transmits the information entered by the user to the server, which does so securely using the HTTPS protocol.

[0781] The server receives the information sent from the device and stores it in a database such as MySQL or PostgreSQL. The stored data includes information such as the user ID, symptoms, existing prescription medications, age, and gender.

[0782] The server retrieves the information stored in the database and inputs it into an AI model built using machine learning frameworks such as TensorFlow and PyTorch, which then compares it with past data to determine the most appropriate medication for the user's health condition.

[0783] After the AI ​​model selects a medication, the server returns the results to the user, which are displayed in a user-friendly format, such as a message like "We recommend using ibuprofen."

[0784] As a concrete example, consider the case where a user inputs "I have a headache." The user inputs that he / she is currently taking "aspirin" as a prescription drug. Also, assume that the user is 30 years old and male.

[0785] The server receives this information and stores it in a database. The server then inputs this data into an AI model, which uses past data to determine the most appropriate medication for the user's symptoms. In this case, the AI ​​model might select "ibuprofen."

[0786] The server returns the result to the user, who receives a message via the terminal saying, "We recommend using ibuprofen."

[0787] An example of a prompt sentence to input to the generative AI model is, "A 30-year-old male currently taking aspirin as a prescription medication. Currently suffering from a headache. Please select the medication that is most appropriate for this user."

[0788] The above is an embodiment of the present invention.

[0789] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0790] Step 1:

[0791] The user inputs information about their health condition or ill health.

[0792] Specifically, the user opens the application on their smartphone or computer and goes to the "Symptom Input" screen, where they enter information such as "I have a headache," "My current prescription medication is aspirin," "I'm 30 years old," and "I'm male."

[0793] Input: User's health status and ill health information

[0794] Output: Input information is saved on the device

[0795] Step 2:

[0796] The terminal sends the input information to the server.

[0797] Specifically, when the user presses the "Send" button, the terminal sends the input information to the server using the HTTPS protocol.

[0798] Input: Input information saved on the device

[0799] Output: Input sent to the server

[0800] Step 3:

[0801] The server receives the input information and stores it in a database.

[0802] Specifically, the server analyzes the information it receives and stores information such as user ID, symptoms, existing prescription medications, age, and gender in a database such as MySQL or PostgreSQL.

[0803] Input: The input sent to the server

[0804] Output: Information stored in the database

[0805] Step 4:

[0806] The server inputs the stored data into the AI ​​model.

[0807] Specifically, the server retrieves information corresponding to the "user ID" from the database and inputs it into an AI model built using machine learning frameworks such as TensorFlow and PyTorch.

[0808] Input: Information stored in a database

[0809] Output: Data input into the AI ​​model

[0810] Step 5:

[0811] The AI ​​model determines the appropriate medication.

[0812] Specifically, the AI ​​model compares the data with past data to determine the medication that best suits the user's health condition. In this case, the AI ​​model might select "ibuprofen."

[0813] Input: Data fed into the AI ​​model

[0814] Output: Determined appropriate medication

[0815] Step 6:

[0816] The server receives the results of the AI ​​model and returns them to the user.

[0817] Specifically, the server receives the results from the AI ​​model, generates a message saying "We recommend using ibuprofen," and returns it to the user.

[0818] Input: Results from the AI ​​model

[0819] Output: The message returned to the user

[0820] Step 7:

[0821] The user checks the results through the terminal.

[0822] Specifically, the user opens the app on their smartphone and sees the message "Using ibuprofen is recommended" on the "Results Display" screen.

[0823] Input: The message returned by the server

[0824] Output: The result that the user sees

[0825] The above is a concrete explanation of the processing flow of the program of this system.

[0826] (Application example 1)

[0827] Next, a description will be given of Application Example 1 of Form 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."

[0828] Conventional drug selection systems have difficulty selecting the appropriate drug based on the user's health condition or when feeling unwell. Furthermore, when users try to purchase the recommended drug at a physical store, they have the problem of not knowing which store they can purchase it at. Furthermore, since they cannot consider interactions between over-the-counter and prescription drugs or the state of local medical care, it is difficult to select the optimal drug for the user.

[0829] 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.

[0830] In this invention, the server includes a means including AI for determining the user's health condition and the appropriate medication to select when feeling unwell, a means for the AI ​​to determine the appropriate medication based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.), and a means for displaying the recommended medication as available for purchase at a nearby physical store. This allows the user to select the appropriate medication based on their own health condition and makes it easy to purchase at a physical store. It also makes it possible to make decisions taking into account interactions between over-the-counter and prescription medications and the local medical situation.

[0831] A "user" is an individual who uses the system to select medications for their own health condition and when they are feeling unwell.

[0832] "Health status" refers to the user's current physical and mental state, including physical ailments and pre-existing medical history.

[0833] "Illness" refers to physical discomfort or abnormalities felt by the user, and specifically includes symptoms such as headache, fever, and stomachache.

[0834] "Medicine selection" refers to the act of selecting an appropriate medicine for the user's health condition or when they are feeling unwell.

[0835] "AI" refers to artificial intelligence, which includes algorithms and models for selecting appropriate medications based on user input.

[0836] "Input information" refers to data provided by the user to the system, and specifically includes information such as physical condition, existing prescription medications, age, and gender.

[0837] "Appropriate medication" refers to medication that is effective for the user's health condition or physical condition and can be used safely.

[0838] "Brick and mortar store" refers to a physical store where a user can purchase recommended medications.

[0839] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[0840] "Prescription drugs" refers to drugs that require a doctor's prescription.

[0841] "Drug interactions" refers to interactions that occur when multiple drugs are taken at the same time, which may change the effectiveness or side effects of the drugs.

[0842] "Regional medical situation" refers to the state of medical service provision and availability of medical resources in the area where the user lives.

[0843] "Life situation" refers to the environment and conditions in the user's daily life, and specifically includes situations such as elderly people and dual-income households.

[0844] A system for implementing this invention includes AI that determines the user's health condition and the medication to be selected when they are feeling unwell. The system uses AI to determine the appropriate medication based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.), and displays the recommended medication as available for purchase at a nearby physical store.

[0845] Hardware and software used

[0846] Hardware: Smartphone

[0847] Software: Python, Requests library (for API requests), AI models (e.g., TensorFlow, PyTorch)

[0848] Data processing and calculation

[0849] 1. Getting user input:

[0850] Users enter information about their health condition and symptoms of illness through a smartphone app, including symptoms, age, gender, and current prescription medications.

[0851] 2. AI-based drug selection:

[0852] The server sends the input information obtained from the user to the AI ​​model, which then selects the appropriate medication. The AI ​​model then takes into account the user's symptoms and interactions with existing prescription medications to recommend the most appropriate medication.

[0853] 3. Recommended medication indication:

[0854] The server displays the recommended medicines to the user and provides information on nearby physical stores where they can be purchased, allowing the user to immediately purchase the recommended medicines.

[0855] Specific examples

[0856] For example, if a user inputs "I have a headache," their age is 30, their gender is male, and they have no existing prescription medications, the AI ​​will recommend "acetaminophen." The server will then display the recommended medication to the user and provide information on where it can be purchased at nearby pharmacies.

[0857] Prompt Sentence Examples

[0858] User symptoms: Headache

[0859] Age: 30

[0860] Gender: Male

[0861] Current prescription medications: None

[0862] Data to send to the AI ​​model:

[0863] {

[0864] "symptoms": "headache",

[0865] "age": 30,

[0866] "gender": "male",

[0867] "existing_medications": "none"

[0868] }

[0869] In this way, a system can be realized that selects appropriate medications based on the user's health condition and supports purchases in physical stores.

[0870] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0871] Step 1:

[0872] Users launch the smartphone app and enter their health status and symptoms of illness, including symptoms, age, gender, and current prescription medications. The entered information is then stored in the application's database.

[0873] Input: User's symptoms, age, gender, existing prescription medications

[0874] Output: Saved user input information

[0875] Step 2:

[0876] The device sends the saved user input information to the server, which receives the data and converts it into a format that can be input into the AI ​​model.

[0877] Input: User input information

[0878] Output: Data in a format that can be fed into an AI model

[0879] Step 3:

[0880] The server inputs the converted data into an AI model to select the appropriate medication. The AI ​​model then takes into account the user's symptoms and interactions with existing prescription medications to recommend the most appropriate medication.

[0881] Input: Data in the format that is fed into the AI ​​model

[0882] Output: Recommended medication information

[0883] Step 4:

[0884] The server receives the recommended medication information and generates data to display to the user, including the name of the recommended medication, its effects, side effects, and availability at nearby physical stores.

[0885] Input: Recommended medication information

[0886] Output: Data to display to the user

[0887] Step 5:

[0888] The terminal displays the data received from the server to the user, who can then check the recommended medicines and obtain information on purchasing them at a nearby physical store.

[0889] Input: The data to display to the user

[0890] Output: Medication information displayed to the user

[0891] Step 6:

[0892] Based on the displayed information, users can purchase the recommended medication at a nearby brick-and-mortar store, and if necessary, they can use the chat function to ask additional questions to a pharmacist.

[0893] Input: Medication information displayed to the user

[0894] Output: Purchase of medicines and answers to follow-up questions

[0895] Example 2

[0896] Next, a description will be given of Example 2 of Form 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."

[0897] Conventional drug selection systems have difficulty considering interactions between prescription and over-the-counter drugs taken by users, resulting in health risks due to incorrect drug selection. Furthermore, since they are unable to consider the state of local medical care or the user's living situation, it is difficult to select the optimal drug for each individual user. This has led to the issue of being unable to properly manage the user's health condition.

[0898] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0899] In this invention, the server includes a means for a user to input prescription drug information, a means for a terminal to transmit the input information to the server, a means for the server to search an over-the-counter drug database, a means for the server to evaluate the risk of drug interactions using a generative AI model, a means for the server to return the evaluation results to the terminal, and a means for the terminal to display the evaluation results to the user. This enables selection of drugs that take into consideration interactions between prescription drugs and over-the-counter drugs taken by the user, and further allows for decisions to be made that take into consideration the state of local medical care and the user's living situation, making it possible to select the most appropriate drug for each individual user.

[0900] "User" refers to an individual who utilizes the system to enter prescription drug information and obtain information regarding drug selection.

[0901] "Terminal" refers to the device through which the user enters prescription drug information and communicates with the server. Specifically, this includes smartphones and personal computers.

[0902] "Server" refers to the computer system that receives user-submitted information, searches a database of commercially available drugs, and uses a generative AI model to assess the risk of interactions.

[0903] "Over-the-counter drug database" refers to a collection of data including over-the-counter drug names, ingredients, and interaction information.

[0904] "Generative AI models" refer to artificial intelligence models that analyze prescription drug information and over-the-counter drug ingredient information entered by users and evaluate the risk of drug interactions. Specifically, they include models that use natural language processing technology.

[0905] "Risk of interaction" refers to the potential health risks that may arise when prescription and over-the-counter medications are taken at the same time.

[0906] "Evaluation results" refers to information about interactions between prescription and over-the-counter drugs obtained as a result of analysis by the generative AI model.

[0907] "Regional medical situation" refers to the state of medical service provision and the state of medical resource utilization in the area where the user resides.

[0908] "Life situation" refers to factors that affect daily life, such as the user's age, occupation, and home environment.

[0909] The present invention provides a system for assisting a user in selecting medications that take into consideration interactions between prescription and over-the-counter medications. Specific embodiments of this system are described below.

[0910] First, the user enters prescription drug information using a terminal. The terminal can be a smartphone or a PC. The prescription drug information entered by the user is sent from the terminal to the server. At this time, the data is encrypted before being sent, ensuring security.

[0911] The server searches an over-the-counter drug database based on the received prescription drug information. The over-the-counter drug database contains information such as the names, ingredients, and drug interactions of over-the-counter drugs. Using a generative AI model, the server analyzes the ingredient information of prescription and over-the-counter drugs and evaluates the risk of drug interactions.

[0912] The generative AI model uses natural language processing technology, such as GPT-4, to assess the risk of interactions between prescription and over-the-counter drugs by inputting a prompt.

[0913] For example, if the user is taking "aspirin," the server will input the following prompt into the generative AI model:

[0914] If the user is taking aspirin, identify any over-the-counter medications that may interact.

[0915] Based on this prompt, the generative AI model identifies over-the-counter medications that may interact with aspirin and assesses their risk, resulting in the information that "taking aspirin and ibuprofen at the same time increases the risk of bleeding and should be avoided."

[0916] The server returns the evaluation results to the terminal, which then displays the evaluation results received from the server to the user. The user can then check the evaluation results on the application screen and select an appropriate medication.

[0917] This system allows users to select medications that take into account interactions between prescription and over-the-counter medications, thereby reducing health risks.It also takes into account the local medical situation and the user's living situation, allowing for the selection of medications that are optimal for each individual user.

[0918] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0919] Step 1:

[0920] The user enters prescription information.

[0921] The user opens the application on their smartphone or computer and enters the name and ingredient information of the prescription medication they are currently taking. For example, the user enters "aspirin" and presses the send button. The input data is the name and ingredient information of the prescription medication.

[0922] Step 2:

[0923] The terminal transmits the input information to the server.

[0924] The terminal encrypts the prescription drug information entered by the user and sends it to the server. Once transmission is complete, the terminal waits for a response from the server. The input data is the encrypted prescription drug information, and the output data is the prescription drug information sent to the server.

[0925] Step 3:

[0926] The server searches the over-the-counter drug database.

[0927] The server searches the over-the-counter drug database based on the received prescription drug information. The database contains over-the-counter drug names, ingredients, and interaction information. The input data is the prescription drug information, and the output data is a list of applicable over-the-counter drugs.

[0928] Step 4:

[0929] The server uses a generative AI model to assess the risk of the interaction.

[0930] The server inputs a prompt into the generative AI model and analyzes the ingredient information of prescription and over-the-counter drugs. For example, the prompt might read, "If the user is taking aspirin, please identify any over-the-counter drugs that may interact with it." The generative AI model evaluates the risk of interactions between prescription and over-the-counter drugs. The input data is the prompt and a list of over-the-counter drugs, and the output data is the interaction risk assessment results.

[0931] Step 5:

[0932] The server returns the evaluation results to the terminal.

[0933] The server returns the results of the generative AI model's evaluation to the device. The evaluation results include interaction risks and recommended countermeasures. The input data is the interaction risk evaluation results, and the output data is the evaluation results sent to the device.

[0934] Step 6:

[0935] The terminal displays the evaluation results to the user.

[0936] The terminal displays the evaluation results received from the server to the user. The user can check the evaluation results on the application screen and select an appropriate medication. The input data is the evaluation results received from the server, and the output data is the evaluation results displayed to the user.

[0937] (Application example 2)

[0938] Next, a description will be given of Application Example 2 of Form 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."

[0939] Conventional drug selection systems have difficulty considering interactions between prescription drugs and over-the-counter drugs that users are already taking, resulting in health risks due to incorrect drug selection. Another problem is that users have to spend time and effort checking for interactions when purchasing drugs. Furthermore, because they are unable to consider the state of local medical care or the user's living situation, it is difficult to select the optimal drug for each individual user.

[0940] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence means for determining the user's health condition and the selection of medication when feeling unwell, a means for making a decision taking into account interactions between over-the-counter drugs and prescribed medications, a means for scanning barcodes of over-the-counter drugs, a means for registering prescription drugs, a means for checking for interactions, a means for allowing electronic payment if no interactions exist, and a means for displaying a warning if an interaction exists. This allows interactions between prescription drugs and over-the-counter drugs already taken by the user to be checked in real time, enabling appropriate drug selection and safe purchase. Furthermore, since the server can make decisions taking into account the local medical situation and the user's living situation, it is possible to select the most appropriate medication for each individual user.

[0941] "User" refers to an individual who uses the system to select and purchase medications.

[0942] "Health" refers to a user's current physical and mental state.

[0943] "Illness" refers to any discomfort or symptoms of illness experienced by the user.

[0944] "Artificial intelligence" refers to technology that allows computer systems to imitate human intelligence to make judgments and learn.

[0945] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[0946] "Prescription drugs" refers to drugs that require a doctor's prescription.

[0947] Drug interactions refer to interactions that occur when multiple medications are taken at the same time.

[0948] "Barcode" refers to a code consisting of a series of lines and numbers that contains product information.

[0949] "Scanning" refers to the act of reading a barcode.

[0950] "Registration" refers to the act of a user entering and saving information in the system.

[0951] "Drug interaction" refers to the chemical or physiological effects that occur when two or more drugs are taken at the same time.

[0952] "Electronic payments" refers to monetary transactions conducted over the Internet.

[0953] A "warning" refers to a message that alerts the user.

[0954] "Community medical care" refers to the state of medical service provision in a specific area.

[0955] "Life situation" refers to the user's living environment and lifestyle.

[0956] A "generative AI model" refers to a model that allows artificial intelligence to generate new information and content based on data.

[0957] A "prompt" refers to an instruction sentence input into a generative AI model.

[0958] As an embodiment of the present invention, the following system is constructed.

[0959] System Configuration

[0960] The system includes artificial intelligence (AI) to determine the user's health condition and the appropriate medication when they are unwell. Specifically, the system uses the following hardware and software:

[0961] Hardware

[0962] Smartphone: Used by users to scan barcodes on over-the-counter medications.

[0963] Server: Runs the AI ​​model and manages the database.

[0964] software

[0965] Python: The main programming language.

[0966] OpenCV: Used to scan barcodes using a smartphone camera.

[0967] Barcode: Used to generate and read barcodes.

[0968] Requests: Used to access the drug database through API.

[0969] Processing flow

[0970] 1. Barcode Scanning: A user scans the barcode of an over-the-counter medication using the smartphone camera. The OpenCV library is used to capture the camera image and read the barcode.

[0971] 2. Prescription drug registration: The user registers the prescription drugs they are currently taking in the app, which allows the system to store the user's prescription drug information in a database.

[0972] 3. Interaction check: Based on the scanned over-the-counter drug information, AI checks for interactions with prescription drugs. If an interaction is found, a warning is displayed to the user and the purchase is canceled. If there is no interaction, electronic payment is allowed.

[0973] 4. Electronic Payment: If no interactions are determined, users can purchase over-the-counter medications using their smartphones.

[0974] Specific examples

[0975] For example, if a user attempts to purchase an over-the-counter drug "drugX," they scan the barcode of "drugX" with their smartphone's camera. The system checks whether "drugX" interacts with "drugA," a prescription drug registered by the user. If there is an interaction, the app displays a warning and cancels the purchase. If there is no interaction, the user can purchase "drugX" through electronic payment.

[0976] Prompt Sentence Examples

[0977] "Create an application that helps users choose the right over-the-counter medication by checking its interactions with prescription medications they are already taking when purchasing them. Include a feature that scans the barcode of over-the-counter medications and checks for interactions with prescription medications in real time."

[0978] In this way, we have created a system that checks interactions between prescription drugs and over-the-counter drugs in real time, enabling appropriate drug selection and safe purchase. Furthermore, since the system can take into account the local medical situation and the user's living situation, it is possible to select the most appropriate drug for each individual user.

[0979] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0980] Step 1:

[0981] A user scans the barcode of an over-the-counter drug using the smartphone camera. The input is the barcode of the over-the-counter drug, and the output is the information about the over-the-counter drug read from the barcode. Specifically, the smartphone camera is activated, the barcode is captured using the OpenCV library, and the drug information is obtained using the barcode library.

[0982] Step 2:

[0983] The device registers the prescription medications that the user is currently taking. The input is the prescription medication information entered by the user, and the output is the prescription medication information stored in the database. Specifically, the user enters the name and dosage of the prescription medication into the app's input form, and the information is sent to the server and stored in the database.

[0984] Step 3:

[0985] The server checks for interactions based on the scanned over-the-counter drug information and registered prescription drug information. The input is over-the-counter drug information and prescription drug information, and the output is a judgment result regarding whether or not an interaction exists. Specifically, the server compares the over-the-counter drug and prescription drug databases and evaluates the possibility of interactions using an AI model.

[0986] Step 4:

[0987] The server displays a warning to the user based on whether or not there is an interaction. The input is the interaction judgment result, and the output is a warning message to the user. Specifically, if there is an interaction, the app displays a warning message to the user and stops the purchase.

[0988] Step 5:

[0989] The server allows electronic payment if there is no interaction. The input is the result of the judgment that there is no interaction, and the output is a notification that the electronic payment has been completed. Specifically, the app displays a purchase confirmation message to the user, and when the user presses the confirm button, the electronic payment is made.

[0990] Step 6:

[0991] The server saves the purchase history in a database. The input is information about the purchased over-the-counter drug and the user's information, and the output is updated purchase history data. Specifically, after the electronic payment is completed, the information about the purchased over-the-counter drug is added to the database and the user's purchase history is updated.

[0992] Example 3

[0993] Next, a description will be given of Example 3 of Form Example 3. 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."

[0994] Conventional medical systems have had the problem of being unable to select medical services and medications that fully take into account the user's individual living situation and the state of local medical care. It was particularly difficult to propose appropriate medical services to elderly users and dual-income households. Furthermore, decisions that took into account interactions between over-the-counter and prescription drugs were also insufficient.

[0995] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0996] In this invention, the server includes: means for a user to input information such as their age, occupation, and lifestyle using a terminal; means for collecting data on regional medical services and types of medication; means for storing the collected regional medical data and the user's living situation data in a database; means for training a generative AI model based on the data stored in the database; means for generating prompt sentences based on the user's living situation and regional medical data; and means for the generative AI model to suggest optimal medical services and medications. This enables optimal medical services and medications to be selected taking into account the user's individual living situation and the local medical situation.

[0997] "User" refers to an individual who uses the system to input information about their health and lifestyle.

[0998] "Terminal" refers to an electronic device that a user uses to input information.

[0999] "Server" refers to a central processing unit that receives information from users and collects, stores, and processes data.

[1000] "Database" refers to an information management system for storing collected regional medical data and user living situation data.

[1001] A "generative AI model" refers to an artificial intelligence model that is trained based on data stored in a database and suggests optimal medical services and medication options.

[1002] A "prompt sentence" refers to an instruction sentence that includes the user's living situation and local medical data and is input to the generative AI model.

[1003] "Medical Services" refers to medical treatments and procedures provided to improve the health of a User.

[1004] "Medicine" refers to a pharmaceutical product used to improve the User's physical condition.

[1005] "Regional medical data" refers to information about medical services and types of medicines in a particular region.

[1006] "Lifestyle data" refers to individual information such as the user's age, occupation, and lifestyle.

[1007] This invention is a system that proposes optimal medical services and drug selections in consideration of the user's living situation and the state of local medical care. Specific embodiments of this system will be described below.

[1008] First, the user uses a device to input information such as their age, occupation, and lifestyle. The device can be an electronic device such as a smartphone, tablet, or PC. For example, if the user is 70 years old and both partners work and lead busy lives, they can input that information into the device.

[1009] Next, the server collects data on medical services and medications in each region. The server uses a server equipped with a high-performance GPU (e.g., a server equipped with a high-performance graphics processing unit) to obtain regional medical data from the Internet. For example, the server collects information on Hospital A and Pharmacy B and stores it as regional medical data.

[1010] The server stores the collected regional medical data and user living situation data in a database management system (e.g., MySQL). For example, the server might store data such as "User ID: 123, Age: 70, Occupation: Dual-income household, Region: X City, Available medical services: Hospital A, Pharmacy B" in the database.

[1011] Next, the server uses a machine learning framework (e.g., TensorFlow) to train a generative AI model based on the data stored in the database. The generative AI model receives the user's living situation and local medical data as input and suggests the optimal medical service and medication selection. For example, the server learns patterns such as "if the user is elderly and both partners are busy working, a home visit medical service would be appropriate."

[1012] The server generates a prompt based on the user's living situation and local medical data. For example, the server generates the following prompt:

[1013] Example prompt sentence:

[1014] The user is a 70-year-old elderly person who leads a busy life as both partners work. In the area where the user lives, Hospital A and Pharmacy B are available. Taking this situation into consideration, please suggest the best medical service and medication selection.

[1015] Based on this prompt, the generative AI model will make a suggestion, for example, "Use the home visit medical service from Hospital A and receive the prescribed medicine from Pharmacy B via home delivery." The user can receive this suggestion through their device and make arrangements for medical services and medicine as needed.

[1016] In this way, the present invention enables selection of optimal medical services and medicines taking into consideration the individual living conditions of the user and the state of local medical care. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1017] Step 1:

[1018] A user uses a terminal to input information such as their age, occupation, and lifestyle. For example, if the user is 70 years old and both partners work and lead busy lives, they input this information into the terminal. The input data is the user's individual information such as age, occupation, and lifestyle. The output is the transmission of this information from the terminal to the server.

[1019] Step 2:

[1020] The server collects data on medical services and types of medications in each region. The server obtains regional medical data via the Internet. For example, the server collects information on Hospital A and Pharmacy B and stores it as regional medical data. The input data is information about the region, and the output is the collected data on medical services and medications.

[1021] Step 3:

[1022] The server stores the collected regional medical data and user living situation data in a database. The server uses a database management system (e.g., MySQL) to store this data. For example, the server might store data such as "User ID: 123, Age: 70, Occupation: Dual-income household, Area: X City, Available medical services: Hospital A, Pharmacy B" in the database. The input data is the collected regional medical data and user living situation data, and the output is the data stored in the database.

[1023] Step 4:

[1024] The server uses a machine learning framework (e.g., TensorFlow) to train a generative AI model based on data stored in a database. The server receives the user's living situation and local medical data as input, and trains a model to suggest optimal medical services and medication options. For example, the server learns patterns such as "if the user is elderly and both partners are busy working, home visit medical services are appropriate." The input data is the data stored in the database, and the output is the trained generative AI model.

[1025] Step 5:

[1026] The server generates a prompt based on the user's living situation and local medical data. For example, the server generates the following prompt:

[1027] Example prompt sentence:

[1028] The user is a 70-year-old elderly person who leads a busy life as both partners work. In the area where the user lives, Hospital A and Pharmacy B are available. Taking this situation into consideration, please suggest the best medical service and medication selection.

[1029] The input data is the user's living situation and local medical data, and the output is the generated prompt sentence.

[1030] Step 6:

[1031] The generative AI model suggests optimal medical services and medications based on the prompt text. For example, the generative AI model might suggest "Use the home visit service of Hospital A and receive the medication prescribed at Pharmacy B via home delivery." The input data is the generated prompt text, and the output is the suggested medical services and medication selections. The user receives this suggestion via their device and can make arrangements for medical services and medications as needed.

[1032] (Application example 3)

[1033] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1034] Conventional medical systems do not adequately consider the user's health condition and lifestyle when selecting medications or suggesting meal menus. This can make it difficult for users to receive appropriate medical services and meals, leading to inadequate health management. Furthermore, since available medical services and ingredients vary by region, decisions must be made taking into account information specific to that region. To solve these issues, a system is needed that can make optimal suggestions based on the user's individual circumstances.

[1035] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1036] In this invention, the server includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for making a decision taking into consideration interactions between over-the-counter medications and prescribed medications, a means for making a decision taking into consideration the state of local medical care and the user's living situation (e.g., elderly, dual-income households, etc.), and a means for proposing an optimal meal menu taking into consideration the user's health condition and living situation. This makes it possible to propose optimal medical services and meal menus according to the user's health condition and living situation.

[1037] "User's health condition" refers to the illnesses, allergies, and poor physical condition that the user currently has.

[1038] "Choosing medicine when feeling unwell" refers to the act of a user selecting an appropriate medicine when they feel unwell.

[1039] "Over-the-counter" refers to medicines that can be purchased without a doctor's prescription.

[1040] "Prescribed medication" refers to medication that is prescribed by a doctor as a result of a medical examination and dispensed by a pharmacy.

[1041] "Drug interactions" refers to the interactions and effects that occur when multiple medications are taken at the same time.

[1042] "Regional medical situation" refers to the state of medical service provision and availability of medical resources in a particular region.

[1043] "User's living situation" refers to factors that affect the user's daily life, such as the user's age, occupation, and home environment.

[1044] "Meal menu" refers to the contents and combination of meals that a user will eat.

[1045] "Ingredients" refers to the raw materials used to make a meal.

[1046] "Suggestion" refers to the act of showing the user the best option.

[1047] To implement this invention, it is necessary to build a system that proposes optimal medical services and meal menus in consideration of the user's health condition and lifestyle. This system includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for making a decision taking into consideration interactions between over-the-counter drugs and prescribed drugs, a means for making a decision taking into consideration the state of local medical care and the user's lifestyle, and a means for proposing optimal meal menus in consideration of the user's health condition and lifestyle.

[1048] System configuration

[1049] Hardware

[1050] Server: A central server for processing and storing data.

[1051] Smartphone: A device that allows users to enter information and receive suggestions.

[1052] Network: The communications infrastructure that connects the server and smartphones.

[1053] software

[1054] Python: A programming language used for data processing and implementing AI models.

[1055] JSON: The data format used to format user and region data.

[1056] Generative AI model: An AI model that takes into account the user's health condition and lifestyle to make optimal suggestions.

[1057] Processing the data

[1058] The server receives input about the user's health condition and lifestyle, and then proposes optimal medical services and meal plans based on this information. Specifically, it performs the following data processing and calculations:

[1059] 1. Entering user data: Users use their smartphones to enter their health status (e.g., diabetes, high blood pressure) and living situation (e.g., dual-income households, elderly people).

[1060] 2. Obtaining regional data: The server obtains information on medical services and available food ingredients in each region.

[1061] 3. Data integration: Integrate user data and regional data, and use generative AI models to suggest optimal medical services and meal menus for users.

[1062] Specific examples

[1063] For example, if the user is a busy, dual-income family with diabetes and high blood pressure, the system will suggest the best menu based on locally available ingredients.

[1064] The user's health conditions are diabetes and high blood pressure, and their living situation is a busy dual-income household. The ingredients available in the area are chicken breast, tofu, and broccoli. Please suggest the optimal meal menu.

[1065] By inputting this prompt into a generative AI model, it is possible to suggest optimal meal plans for the user, thereby supporting health management and improving the user's quality of life.

[1066] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1067] Step 1:

[1068] Users use their smartphones to input their health conditions (e.g., diabetes, high blood pressure) and living situation (e.g., dual-income households, elderly).

[1069] Input: Data about the user's health and living situation.

[1070] Output: The user data is sent to the server.

[1071] Specific operation: The user opens the application on their smartphone and answers questions about their health and lifestyle. The answers are sent to the server in JSON format.

[1072] Step 2:

[1073] The server obtains information on local medical services and available food ingredients.

[1074] Input: Region information.

[1075] Output: The region data is saved on the server.

[1076] Specific operation: The server accesses the local medical database and food ingredient database to obtain information on medical services and available ingredients in the specified area. This data is stored in JSON format.

[1077] Step 3:

[1078] The server integrates user data and local data to generate prompt sentences to input into the generative AI model.

[1079] Input: User data and region data.

[1080] Output: The generated prompt statement.

[1081] Specific operation: The server analyzes user data and local data and generates a prompt to suggest the optimal meal menu based on the user's health condition and living situation. For example, it generates a prompt such as, "The user's health condition is diabetes and high blood pressure, and their living situation is that they are both working and busy. The ingredients available in the area are chicken breast, tofu, and broccoli. Please suggest the optimal meal menu."

[1082] Step 4:

[1083] The server inputs prompts into the generative AI model to generate the optimal meal menu.

[1084] Input: The generated prompt statement.

[1085] Output: Optimal meal menu.

[1086] How it works: The server inputs prompts into the generative AI model, which then generates an optimal meal menu based on the user's health and lifestyle. The generated menu is saved in JSON format on the server.

[1087] Step 5:

[1088] The server sends the generated optimal meal menu to the user's smartphone.

[1089] Enter: your optimal meal plan.

[1090] Output: A meal menu displayed on the user's smartphone.

[1091] Specific operation: The server sends the generated meal menu to the user's smartphone, where the user can check the proposed meal menu through the smartphone application.

[1092] 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.

[1093] "Example 1"

[1094] One embodiment of the present invention provides a system including an emotion engine that recognizes a user's emotions. This system combines an AI that determines the user's health condition and the appropriate medication when the user is feeling unwell with the emotion engine that recognizes the user's emotions. Specifically, when a user feels unwell, the emotion engine recognizes the user's emotional state and provides that information to the AI. The AI ​​then takes that emotional state into account when making a medication selection. For example, if the user is feeling stressed, the AI ​​can take that information into account and select a medication that will help alleviate stress.

[1095] "Example 2"

[1096] Another embodiment of the present invention provides a system in which an emotion engine makes a decision taking into account interactions between over-the-counter medications and prescription medications. Specifically, when a user feels unwell and wants to take a specific over-the-counter medication, the emotion engine recognizes the user's emotional state. This information and information about the over-the-counter medication are provided to an AI, which then makes a decision taking into account interactions between the over-the-counter medication and prescription medication based on that information. For example, if the user is feeling anxious, the AI ​​can take that information into account and select an over-the-counter medication and prescription medication combination that will help alleviate anxiety.

[1097] "Example 3"

[1098] One embodiment of the present invention provides a system including an emotion engine that recognizes a user's emotions. This system combines an AI that determines the user's health condition and the appropriate medication when the user is feeling unwell with the emotion engine that recognizes the user's emotions. Specifically, when a user feels unwell, the emotion engine recognizes the user's emotional state and provides that information to the AI. The AI ​​then takes that emotional state into account when making a medication selection. For example, if the user is feeling stressed, the AI ​​can take that information into account and select a medication that will help alleviate stress.

[1099] The processing flow of each embodiment will be described below.

[1100] "Example 1"

[1101] Step 1: (Step 1)

[1102] When a user feels unwell, the emotion engine recognizes the user's emotional state. Step 2:

[1103] The emotion engine provides AI with information on the emotional state recognized.

[1104] Step 3: (Step 3)

[1105] The AI ​​will take into account the emotional state information provided to determine medication selection. For example, if the user is feeling stressed, the AI ​​will take that information into account and select medication that will help reduce stress.

[1106] "Example 2"

[1107] Step 1: (Step 1)

[1108] When a user feels unwell and wants to take a particular over-the-counter medication, the emotion engine recognizes the user's emotional state.

[1109] Step 2: (Step 2)

[1110] The emotion engine provides the AI ​​with information on the emotional state recognized and information on over-the-counter medications.

[1111] Step 3: (Step 3)

[1112] The AI ​​makes decisions based on the information provided, taking into account interactions between over-the-counter and prescription medications. For example, if the user is feeling anxious, the AI ​​will take that information into account and select combinations of over-the-counter and prescription medications that will help reduce anxiety.

[1113] Example 1

[1114] Next, a description will be given of Example 1 of Form 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."

[1115] Conventional drug selection systems select drugs based on the user's health condition and information on when they are feeling unwell, but because they do not take the user's emotional state into account, they have the problem of not being able to select the appropriate drug when stress or emotional fluctuations affect the drug selection. There are also issues with the system being difficult to make decisions that take into account interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[1116] 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.

[1117] In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medication when the user is feeling unwell, emotion recognition means for recognizing the user's emotions, means for providing the artificial intelligence means with emotion information recognized by the emotion recognition means, and means for selecting an appropriate medication based on the emotion information and physical condition information provided by the artificial intelligence means. This makes it possible to select an appropriate medication taking into consideration the user's emotional state, and also makes it possible to make decisions that take into consideration interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[1118] "User" refers to an individual who uses the system to select medications for their own health condition or when they are feeling unwell.

[1119] "Health status" refers to information that indicates the state of a user's physical and mental health.

[1120] "Illness" refers to a state of physical discomfort or abnormality felt by the user.

[1121] "Medicine" refers to pharmaceutical products used to improve the health or well-being of the User.

[1122] "Artificial intelligence means" refers to machine learning and data analysis techniques used to determine a user's health condition and the choice of medication when they are unwell.

[1123] "Emotion recognition means" refers to technology or devices for recognizing the emotional state of a user.

[1124] "Emotion information" refers to data relating to the user's emotional state as recognized by the emotion recognition means.

[1125] "Physical condition information" refers to information about the health condition or poor physical condition entered by the user.

[1126] "Over-the-counter" refers to medicines that can be purchased without a prescription.

[1127] "Prescription drugs" refers to medicines that require a doctor's prescription.

[1128] "Regional medical situation" refers to the state of medical service provision and the state of medical resource utilization in the area where the user resides.

[1129] "Life situation" refers to information about the user's living environment and lifestyle habits.

[1130] MODE FOR CARRYING OUT THE INVENTION

[1131] This invention is a system that combines an artificial intelligence means that determines the user's health condition and the selection of medicine when the user is feeling unwell with an emotion recognition means that recognizes the user's emotions. A specific embodiment of this system will be described below.

[1132] System configuration

[1133] This system consists of a terminal used by the user, a server, artificial intelligence means, and emotion recognition means.

[1134] 1. Terminal

[1135] Users use devices such as smartphones and tablets to input information about their own health status and physical condition. A dedicated application is installed on the device, and users input information through this application.

[1136] 2. Server

[1137] The server receives the information sent by the user and processes it by integrating the emotion recognition means and the artificial intelligence means. The server communicates with the terminal via an internet connection.

[1138] 3. Artificial Intelligence Means

[1139] Artificial intelligence tools are used to determine the user's health condition and the choice of medication when they are unwell. Specifically, models built using machine learning frameworks (e.g., TensorFlow and PyTorch) recommend the most appropriate medication based on past data.

[1140] 4. Emotion recognition means

[1141] Emotion recognition means are used to recognize the user's emotional state, for example, by using software that analyzes the user's facial expressions and tone of voice (e.g., Emotion API) to recognize the user's emotional state (e.g., stress, sadness, joy).

[1142] System Operation

[1143] When a user feels unwell, they use a terminal to input information such as "I have a headache" or "I feel stressed." The terminal then sends this information to a server. The server then activates an emotion recognition means based on the received information and recognizes the user's emotional state. The emotion information recognized by the emotion recognition means is then sent to the server. The server then provides the emotion information and physical condition information to an artificial intelligence means, which then selects an appropriate medication based on this information.

[1144] Specific examples

[1145] For example, if the user inputs "I have a headache" or "I feel stressed," the device sends this information to the server. The server activates the emotion recognition means and recognizes the user's stress state. The server provides the recognized emotion information and physical condition information to the artificial intelligence means, which then selects a medication that has the effect of reducing stress (e.g., acetaminophen and herbal tea with a relaxing effect). The server sends this medication information to the terminal, and the terminal displays detailed information about the medication to the user.

[1146] Prompt Sentence Examples

[1147] Examples of prompts to input to a generative AI model include:

[1148] "If a user complains of headaches and stress, provide them with information to help them choose the right medication."

[1149] The system allows users to choose more appropriate medications based on their own health and emotional state.

[1150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1151] Step 1:

[1152] The user inputs physical condition information.

[1153] The user uses the device to input their own physical condition information (e.g., headache, stress) into a dedicated application. The input information is saved in text format on the device.

[1154] Step 2:

[1155] The terminal sends the input information to the server.

[1156] The terminal encrypts the health information entered by the user and sends it to the server via the Internet. The input data is health information in text format. The server stores the received health information in a database.

[1157] Step 3:

[1158] The server activates the emotion recognition means to recognize the emotion of the user.

[1159] The server activates the emotion recognition means based on the received physical condition information. The emotion recognition means analyzes the user's facial expressions and tone of voice using the device's camera and microphone. The analyzed data is sent to the server as emotion information.

[1160] Step 4:

[1161] The server provides the emotional information and physical condition information to the artificial intelligence means.

[1162] The server provides the emotional information received from the emotion recognition means and the physical condition information input by the user to the artificial intelligence means. The provided data is a combination of the emotional information and the physical condition information.

[1163] Step 5:

[1164] Artificial intelligence means select the appropriate medication.

[1165] The artificial intelligence means uses a machine learning model to select the appropriate medication based on the provided emotional and physical condition information. For example, a model built using TensorFlow or PyTorch can recommend the optimal medication based on past data. The selected medication information is sent to the server.

[1166] Step 6:

[1167] The server transmits the selected drug information to the terminal.

[1168] The server encrypts the drug information received from the artificial intelligence means and transmits it to the terminal via the Internet. The transmitted data includes detailed information about the drug (e.g., name of the drug, its effects, and side effects).

[1169] Step 7:

[1170] The terminal displays the medication information to the user.

[1171] The terminal displays the medication information received from the server to the user, who can then check detailed information about the recommended medication on the application screen.

[1172] (Application example 1)

[1173] Next, a description will be given of Application Example 1 of Form 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."

[1174] Conventional drug selection systems do not take into account the user's emotional state when determining the user's health condition or the drug to be selected when feeling unwell. This means that they are unable to select the appropriate drug when emotional states such as stress or fatigue affect the drug selection. Furthermore, there is a lack of information to quickly obtain the suggested drugs, which means it takes a long time for the user to obtain the drug.

[1175] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medicine when the user is feeling unwell, emotion recognition means for recognizing the user's emotional state, means for the artificial intelligence means to select medicine based on the emotional state obtained from the emotion recognition means, and means for providing inventory information of nearby physical stores based on the suggested medicine. This makes it possible to select an appropriate medicine taking into account the user's emotional state, and also makes it possible to provide information for quickly obtaining the suggested medicine.

[1176] "User" refers to an individual who uses the system.

[1177] "Health status" refers to information indicating the physical health status of a user.

[1178] "Illness" refers to physical discomfort or symptoms experienced by the user.

[1179] "Medicine" refers to pharmaceutical products used to improve the health or well-being of the User.

[1180] "Artificial intelligence means" refers to algorithms and programs that determine the user's health condition and the choice of medication when feeling unwell.

[1181] "Emotion recognition means" refers to an algorithm or program for recognizing the user's emotional state.

[1182] "Emotional state" refers to emotions such as stress, fatigue, and impatience felt by the user.

[1183] "Brick and mortar store" refers to a physical store where users can purchase medications.

[1184] "Inventory information" refers to information showing the stock status of medicines at physical stores.

[1185] "Regional medical situation" refers to the state of medical services provided in the area where the user resides.

[1186] "Life situation" refers to information about the user's life, such as age, sex, occupation, and home environment.

[1187] A system for implementing this invention includes artificial intelligence means for determining the user's health condition and the selection of medication when the user is feeling unwell, emotion recognition means for recognizing the user's emotional state, means for the artificial intelligence means to select medication based on the emotional state obtained from the emotion recognition means, and means for providing inventory information of nearby physical stores based on the suggested medication.

[1188] System configuration

[1189] 1. Hardware

[1190] Smartphone: A device that allows users to enter information and receive results.

[1191] Server: A device for executing artificial intelligence means and emotion recognition means.

[1192] 2. Software

[1193] EmotionEngine: An emotion recognition engine for recognizing the user's emotional state.

[1194] HealthAI: Artificial intelligence to determine a user's health condition and the appropriate medication when they are unwell.

[1195] requests: A Python library for sending API requests.

[1196] Data processing and calculation

[1197] 1. User Input

[1198] Users use their smartphones to enter information such as symptoms, existing prescription medications, age, and gender.

[1199] 2. Emotion recognition

[1200] The emotion recognition means (Emotion Engine) recognizes the user's emotional state, for example, determining whether the user is feeling stressed.

[1201] 3. Medication selection

[1202] The artificial intelligence (HealthAI) will suggest appropriate medications based on the user's input and emotional state. For example, if the user has a headache and is stressed, it will suggest painkillers and medications that reduce stress.

[1203] 4. Providing inventory information

[1204] Based on the suggested medications, inventory information from nearby physical stores is obtained via API and provided to the user.

[1205] Specific examples

[1206] If a user has a "headache" and "stress," the app will suggest painkillers and stress-reducing medications and provide information on stock availability at nearby pharmacies.

[1207] Prompt Sentence Examples

[1208] When a user feels unwell, they input information such as their symptoms, existing prescription medications, age, and gender. An emotion engine is used to recognize the user's emotional state, and the AI ​​suggests appropriate medications based on the input information and emotional state. The suggested medications are listed as available at nearby physical stores, and stock information is also provided.

[1209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1210] Step 1:

[1211] The user uses a smartphone to input information such as symptoms, existing prescription medications, age, and gender. The input information is then sent to the server. The input data includes detailed information about the user's health condition and physical condition.

[1212] Step 2:

[1213] The server uses an emotion recognition means (Emotion Engine) to recognize the user's emotional state. The emotion recognition means analyzes data such as the user's input information, voice, and facial expressions, and identifies emotional states such as stress, fatigue, and impatience. The user's emotional state is obtained as an output.

[1214] Step 3:

[1215] The server uses artificial intelligence (HealthAI) to suggest appropriate medications based on the user's input information and emotional state. The artificial intelligence selects the most appropriate medication by taking into account the user's symptoms, existing prescription medications, age, gender, and emotional state. The output is a list of suggested medications.

[1216] Step 4:

[1217] The server retrieves inventory information of nearby physical stores based on the suggested medications through an API. The server then searches for inventory information of physical stores using the list of suggested medications and generates a list of stores that have the medications in stock. The output is a list of stores with inventory information.

[1218] Step 5:

[1219] The server then sends the acquired inventory information to the user's smartphone. The user can then check the suggested medications and their inventory information on their smartphone. This allows the user to quickly obtain medications from a nearby physical store.

[1220] Example 2

[1221] Next, a description will be given of Example 2 of Form 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."

[1222] Conventional drug selection systems have difficulty selecting appropriate medications because they are unable to consider the user's emotional state or interactions between over-the-counter and prescription drugs. Furthermore, they are unable to make decisions that take into account the user's living situation or the state of local medical care, making it difficult to select the optimal medication for each individual user.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1224] In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medication when the user is feeling unwell, emotion engine means for recognizing the user's emotional state, and means for providing information from the emotion engine means to the artificial intelligence means, thereby enabling the selection of appropriate medication taking into consideration the user's emotional state and interactions between over-the-counter and prescription medications.

[1225] "User" refers to an individual who uses the system to select medications for their own health condition or when they are feeling unwell.

[1226] "Health Status" refers to the state of a user's physical and mental health.

[1227] "Illness" refers to physical or mental discomfort experienced by the user.

[1228] "Medication selection" refers to the act of a user selecting a medication to take.

[1229] "Artificial intelligence means" refers to artificial intelligence technology used to determine the user's health condition and the choice of medication when feeling unwell.

[1230] "Emotion engine means" refers to technology for recognizing the user's emotional state.

[1231] The "means for providing information" refers to a technology for transmitting information obtained from the emotion engine means to the artificial intelligence means.

[1232] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[1233] "Prescription drugs" refers to drugs that require a doctor's prescription.

[1234] Drug interactions refer to interactions that occur when multiple medications are taken at the same time.

[1235] "Regional medical situation" refers to the state of medical care provided in the area where the user resides.

[1236] "Life situation" refers to the environment and conditions in the user's daily life.

[1237] This invention is a system for assisting a user in selecting medications for their health condition or when they are feeling unwell. The system includes artificial intelligence means for recognizing the user's emotional state and selecting appropriate medications taking into account interactions between over-the-counter and prescription medications.

[1238] Hardware and software used

[1239] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1240] Software: Emotion engine, AI model, Database Management System (DBMS)

[1241] Program processing explanation

[1242] 1. User Input

[1243] The user uses the terminal to input his / her physical condition and emotional state.

[1244] Example: A user types, "I have a headache, but I've been feeling very anxious lately."

[1245] 2. Operation of the Emotion Engine

[1246] The terminal transmits the user's input information to the emotion engine, which analyzes the user's input information and recognizes the user's emotional state.

[1247] Example: An emotion engine analyzes a user's input and determines that they are feeling "anxiety."

[1248] 3. Data transmission

[1249] The emotion engine sends the recognized emotional state and over-the-counter medication information to the server, which receives this information and provides it to the AI ​​model.

[1250] Example: An emotion engine sends the emotional state "anxiety" and information about over-the-counter medications to a server.

[1251] 4. AI model judgment

[1252] The server uses an AI model to make decisions that take into account interactions between over-the-counter and prescription drugs. The AI ​​model retrieves information about the prescription drugs the user is currently taking from a database and evaluates interactions with over-the-counter drugs.

[1253] Example: A server uses an AI model to select over-the-counter medication B that does not interact with prescription medication A that a user is taking.

[1254] 5. Providing results

[1255] The server sends the AI ​​model's results to the device, which then presents the user with appropriate over-the-counter drug options.

[1256] Example: The server sends the result "Over-the-counter drug B is recommended" to the terminal, and the terminal displays that information to the user.

[1257] Prompt Sentence Examples

[1258] "If a user is experiencing headaches and anxiety, explain how you would recommend an over-the-counter medication that will not interact with prescription drug A that the user is taking."

[1259] The system helps users select safe and effective over-the-counter medications based on their emotional state and current medication information.

[1260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1261] Step 1:

[1262] The user uses the terminal to input his / her physical condition and emotional state.

[1263] Input: Information about the user's physical and emotional state (e.g., "I have a headache, and I've been feeling very anxious lately")

[1264] Output: The user's input is saved to the device.

[1265] Specific operation: The user opens the application on the device, enters their physical and emotional state in the text box, and presses the send button.

[1266] Step 2:

[1267] The terminal transmits the user's input information to the emotion engine, which analyzes the user's input information and recognizes the user's emotional state.

[1268] Input: User input (e.g., "I have a headache, but I've been feeling very anxious lately.")

[1269] Output: Emotional state recognition by the emotion engine (e.g., "anxiety")

[1270] Specific operation: The device sends the user's input information to the emotion engine, which then analyzes the user's emotional state using natural language processing technology.

[1271] Step 3:

[1272] The emotion engine sends the recognized emotional state and over-the-counter medication information to the server, which receives this information and provides it to the AI ​​model.

[1273] Input: Emotional state recognition results (e.g., "anxiety") from the emotion engine and over-the-counter medication information

[1274] Output: Emotional state and over-the-counter medication information sent to the server

[1275] How it works: The emotion engine sends the recognition results and over-the-counter drug information to the server, which then inputs this information into the AI ​​model.

[1276] Step 4:

[1277] The server uses an AI model to make decisions that take into account interactions between over-the-counter and prescription drugs. The AI ​​model retrieves information about the prescription drugs the user is currently taking from a database and evaluates interactions with over-the-counter drugs.

[1278] Input: Emotional state, over-the-counter medication information, and information about prescription medications the user is currently taking

[1279] Output: Judgment result taking into account interactions between over-the-counter and prescription drugs (e.g., "Over-the-counter drug B is recommended")

[1280] How it works: The server uses an AI model to retrieve prescription drug information from a database and evaluate interactions with over-the-counter drugs.

[1281] Step 5:

[1282] The server sends the AI ​​model's results to the device, which then presents the user with appropriate over-the-counter drug options.

[1283] Input: The AI ​​model's judgment (e.g., "Over-the-counter drug B is recommended")

[1284] Output: Over-the-counter medication options presented to the user

[1285] Specific operation: The server sends the judgment result to the terminal, and the terminal displays the information to the user.

[1286] (Application example 2)

[1287] Next, a description will be given of Application Example 2 of Form 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."

[1288] Conventional drug selection systems have difficulty selecting appropriate medications because they are unable to consider the user's emotional state or interactions between over-the-counter and prescription drugs. Furthermore, they are unable to make decisions that take into account the user's living situation or the state of local medical care, making it difficult to select the optimal medication for each individual user.

[1289] 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.

[1290] In this invention, the server includes artificial intelligence means for determining the user's health condition and the selection of medications when the user is feeling unwell, emotion engine means for recognizing the user's emotional state, and artificial intelligence means for making a decision taking into account interactions between over-the-counter and prescription drugs based on information obtained from the emotion engine means and over-the-counter drug information. This makes it possible to select appropriate medications taking into account the user's emotional state and interactions between over-the-counter and prescription drugs. Furthermore, the server can also make decisions taking into account the state of local medical care and the user's living situation, enabling the selection of medications that are optimal for each individual user.

[1291] "User's health condition" refers to information such as the user's current physical condition, medical history, and past illnesses.

[1292] "When feeling unwell" refers to a state in which the user is experiencing symptoms such as a cold, headache, or stomachache.

[1293] "Artificial intelligence means for determining medication selection" refers to a system that includes algorithms or programs for selecting appropriate medications when a user is experiencing a health condition or is feeling unwell.

[1294] "Emotion engine means" refers to a system that includes algorithms and programs for recognizing a user's emotional state and providing that information to other systems.

[1295] "Over-the-counter" refers to medicines that can be purchased without a doctor's prescription.

[1296] "Prescription drugs" refers to drugs that require a doctor's prescription.

[1297] "Artificial intelligence means for making decisions that take into account drug interactions" refers to systems that include algorithms or programs for evaluating interactions between over-the-counter and prescription drugs and selecting appropriate drug combinations.

[1298] "Regional medical situation" refers to the number and quality of medical facilities in a particular area, as well as the state of medical service provision.

[1299] "User's living situation" refers to information such as the user's age, occupation, and home environment.

[1300] A system for implementing this invention includes artificial intelligence means for determining the user's health condition and the selection of medication when feeling unwell, emotion engine means for recognizing the user's emotional state, and artificial intelligence means for making decisions taking into consideration interactions between over-the-counter drugs and prescription drugs.

[1301] System configuration

[1302] 1. Artificial intelligence means to determine the user's health condition and choice of medicine when feeling unwell:

[1303] This means includes algorithms and programs for selecting appropriate medications when the user is in poor health or is feeling unwell. Specifically, it suggests the most appropriate medication based on the symptoms and medical history data entered by the user.

[1304] 2. Emotion engine means for recognizing the user's emotional state:

[1305] This method uses a smartphone camera to capture a user's facial image and recognizes the user's emotional state using an emotion recognition algorithm (e.g., EmotionRecognizer). The recognized emotional state influences the selection of medication.

[1306] 3. Artificial intelligence methods for making decisions regarding over-the-counter and prescription drug interactions:

[1307] This tool uses a drug interaction checking library such as DrugInteractionChecker to evaluate interactions between over-the-counter and prescription drugs, thereby suggesting safe drug combinations for users.

[1308] Hardware and software used

[1309] Hardware: Smartphone camera

[1310] Software: EmotionRecognizer (emotion recognition library), DrugInteractionChecker (drug interaction checking library)

[1311] Processing flow

[1312] 1. User Emotion Recognition:

[1313] A user takes a picture of their face using the smartphone camera. The emotion engine inputs this image into the EmotionRecognizer and recognizes the user's emotional state.

[1314] 2. Drug Interaction Check:

[1315] The user enters prescription and over-the-counter drug information into DrugInteractionChecker to check for interactions.

[1316] 3. Proposal generation:

[1317] Based on the recognized emotional state and drug interaction information, the system suggests the optimal drug combination for the user.

[1318] Specific examples

[1319] User image: 'path_to_user_image.jpg'

[1320] Prescription drug: 'DrugA'

[1321] Over-the-counter drug: 'DrugB'

[1322] Prompt Sentence Examples

[1323] Enter the user image: path_to_user_image.jpg

[1324] Enter the name of your prescription drug: DrugA

[1325] Enter the name of your over-the-counter medication: DrugB

[1326] The system allows users to select the best over-the-counter medication for their mood and taking into account interactions with existing prescription medications, making medication choices safer and more effective.

[1327] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1328] Step 1:

[1329] The user takes a picture of their face using the smartphone camera.

[1330] Input: User's face image

[1331] Output: Face image data

[1332] Specific operation: The user activates the smartphone camera and takes a picture of their face. The captured image is saved in the smartphone.

[1333] Step 2:

[1334] The device inputs facial image data into EmotionRecognizer to recognize the user's emotional state.

[1335] Input: Facial image data

[1336] Output: Emotional state data (e.g., anxiety, joy, sadness, etc.)

[1337] What happens: The device sends the stored facial image data to the EmotionRecognizer library and runs the emotion recognition algorithm, which analyzes facial features and identifies the user's emotional state.

[1338] Step 3:

[1339] The user enters prescription and over-the-counter drug information into the terminal.

[1340] Input: Name of prescription drug, name of over-the-counter drug

[1341] Output: Prescription and over-the-counter drug information data

[1342] How it works: Using a smartphone application, users input the names of prescription medications they are currently taking and over-the-counter medications they are considering purchasing. The information is then stored on the device.

[1343] Step 4:

[1344] The terminal inputs prescription and over-the-counter drug information data into DrugInteractionChecker to check for interactions.

[1345] Input: Prescription and over-the-counter drug information data

[1346] Output: Interaction check result (e.g., safe, dangerous, caution required, etc.)

[1347] How it works: The device sends stored prescription and over-the-counter drug information data to the DrugInteractionChecker library and runs the drug interaction checking algorithm, which analyzes the drug's ingredients and mechanism of action and assesses the risk of drug interactions.

[1348] Step 5:

[1349] The server suggests the optimal drug combination for the user based on emotional state data and interaction check results.

[1350] Input: Emotional state data, interaction check results

[1351] Output: Drug combination suggestions (e.g., over-the-counter drug B is safe and effective in reducing anxiety)

[1352] Specific operation: The server integrates the emotional state data and the interaction check results, and then uses a generative AI model to propose optimal drug combinations, taking into account the user's emotional state and the risk of drug interactions. The proposals are displayed on the user's smartphone.

[1353] Step 6:

[1354] The user reviews the suggested drug combination and purchases over-the-counter medications as needed.

[1355] Input: Drug combination suggestions

[1356] Output: Purchase decision

[1357] Specific operation: The user checks the drug combination suggestions displayed on the smartphone and decides whether to purchase over-the-counter drugs based on the suggestions. If the user decides to purchase, they go to a pharmacy or drugstore to purchase the over-the-counter drug.

[1358] Example 3

[1359] Next, a description will be given of Example 3 of Form Example 3. 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."

[1360] Conventional drug selection systems were unable to fully consider the local medical situation, the user's lifestyle, or even the user's emotional state when selecting a drug for a user's health condition or when feeling unwell. This made it difficult to recommend the most suitable drug for the user, and in some cases, the system was unable to provide effective treatment. Furthermore, the system was also unable to adequately consider interactions between over-the-counter drugs and prescribed drugs. This led to the issue of inadequate health management for users.

[1361] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1362] In this invention, the server includes an artificial intelligence means for determining the user's health condition and the selection of medicine when the user is feeling unwell, an emotion analysis means for recognizing the user's emotional state, a means for acquiring medical information from a local medical database, a means for collecting data on the user's living situation, a means for selecting the most appropriate medicine based on the collected data, and a means for presenting information on the selected medicine to the user. This makes it possible to select the most appropriate medicine taking into consideration the user's health condition, emotional state, local medical situation, and living situation in a comprehensive manner.

[1363] "Artificial intelligence means" refers to algorithms and models used to determine the user's health condition and the choice of medication when feeling unwell.

[1364] "Emotion analysis means" refers to technology for analyzing a user's facial expressions and voice data to recognize the user's emotional state.

[1365] A "regional medical database" refers to a database containing information about medical services and types of medications in a particular region.

[1366] "Life situation data" refers to information about the user's life, such as the user's age, occupation, and lifestyle.

[1367] "Means for selecting the optimal medication" refers to algorithms or models that use collected data to select the medication that is most suitable for the user.

[1368] "Means for presenting drug information" refers to technology for providing users with information such as the name, effects, and side effects of the selected drug.

[1369] This invention is a system for optimizing the selection of medications for a user's health condition or when they are feeling unwell. The system includes an artificial intelligence means, an emotion analysis means, a regional medical database, a living situation data collection means, a means for selecting the most appropriate medication, and a means for presenting medication information.

[1370] First, when a user feels unwell, they input that information through a smartphone app. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice using emotion analysis techniques to recognize the user's emotional state. This emotion analysis is performed using machine learning libraries such as TensorFlow and PyTorch, which use Python.

[1371] The server then retrieves medical information from a regional medical database based on the user's location. The regional medical database contains information about medical services and medications available in a specific area. Additionally, the server collects the user's lifestyle data, such as age, occupation, and lifestyle habits.

[1372] Based on this data, the server uses artificial intelligence means to select the most suitable medication. The artificial intelligence means uses the collected data as input and runs algorithms and models to select the most suitable medication for the user. This process can use generative AI models.

[1373] Finally, the server sends information about the selected medication to the device, which then presents the information to the user, such as displaying the medication name, effects, and side effects on the smartphone screen.

[1374] As a concrete example, consider the case where a user feels they have a cold and types "I have a cold" into a smartphone app. The device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice and recognizes that they are feeling stressed. The server retrieves a list of available cold medications from a medical database for the user's area, and further confirms that the user is elderly and leads a busy dual-income household. This data is input into an AI model to select a cold medication that is effective in reducing stress. Information about the selected medication is sent to the device, which then displays information such as the name of the medication, its effects, and side effects on the smartphone screen.

[1375] Examples of prompts to input to a generative AI model include:

[1376] "When the user has a cold, the emotion engine recognizes that they are feeling stressed. The user is elderly and both partners work, leading busy lives. We retrieve a list of available cold medications from the local medical service database. Based on this information, we select the most appropriate cold medication for the user."

[1377] In this way, this system enables the selection of the most appropriate medicine by comprehensively considering the user's health condition, emotional state, local medical situation, and living situation. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1378] Step 1:

[1379] The user feels unwell

[1380] The user inputs information about their poor health, such as "I have a cold," through a smartphone app. This input data indicates the user's health condition and serves as the initial input for the system.

[1381] Step 2:

[1382] The device analyzes the user's emotional state using emotion analysis means.

[1383] The device uses the smartphone's camera and microphone to collect the user's facial expressions and voice. The collected data is input into an emotion analysis means to recognize the user's emotional state (e.g., stress, anxiety, joy, etc.). This analysis uses machine learning libraries such as TensorFlow and PyTorch. The output is data on the user's emotional state.

[1384] Step 3:

[1385] The server retrieves medical information from the local medical database.

[1386] The server accesses a local medical database based on the user's area of ​​residence to obtain information on medical services and medications available in the area. The user's area of ​​residence information is used as input, and a list of medications available in the area is obtained as output.

[1387] Step 4:

[1388] The server collects data on the user's living situation.

[1389] The server collects the user's life situation data, such as age, occupation, and lifestyle. This data is obtained from the user's profile information and past input data. The user's profile information is used as input, and the user's life situation data is obtained as output.

[1390] Step 5:

[1391] The server inputs the data into the AI ​​model and selects the optimal medication.

[1392] The server inputs the collected local medical information, the user's living situation data, and emotional state data into the AI ​​model. Based on this data, the AI ​​model runs an algorithm to select the most appropriate medication for the user. Local medical information, living situation data, and emotional state data are used as input, and information on the most appropriate medication is obtained as output.

[1393] Step 6:

[1394] The server sends the selected drug information to the terminal.

[1395] The server sends the information of the drug selected by the AI ​​model to the terminal. The information of the optimal drug is used as input, and the drug information sent to the terminal is obtained as output.

[1396] Step 7:

[1397] The device presents medication information to the user.

[1398] The device presents the drug information received from the server to the user. Specifically, information such as the name of the drug, its effects, and side effects is displayed on the smartphone screen. The drug information sent from the server is used as input, and the drug information presented to the user is obtained as output.

[1399] (Application example 3)

[1400] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1401] Conventional drug selection systems select drugs based on the user's health condition and information on when they are feeling unwell, but they are unable to consider the user's emotional state, local medical situation, or living situation, making it difficult to select the most appropriate drug.There was also a need for a system that could obtain information on locally available medical services and drugs in real time when the user visits a pharmacy, and select drugs that take their emotional state into consideration.

[1402] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1403] In this invention, the server includes a means for determining the user's health condition and the selection of medicine when feeling unwell, a means for recognizing the user's emotions, a means for making a decision taking into consideration the state of local medical care and the user's living situation, a means for acquiring information on medical services and medicines available in the area based on the user's location information, and a means for determining the selection of medicine based on the user's emotional state. This enables the selection of the optimal medicine taking into consideration the user's health condition, emotional state, and local medical information in a comprehensive manner.

[1404] "User's health condition" refers to the physical condition of the user, such as the illnesses or symptoms the user currently has, and whether the user is in good or bad physical condition.

[1405] "Means for determining the appropriate medication when feeling unwell" refers to an algorithm or system that selects the appropriate medication for the symptoms when a user feels unwell.

[1406] "Means for recognizing a user's emotions" refers to technology or devices that analyze a user's facial expressions, tone of voice, etc., and identify their emotional state.

[1407] "Regional medical situation" refers to the state of medical service provision in a particular area, the types of medicines available, the number of medical facilities, etc.

[1408] "User's living situation" refers to information related to the user's daily life, such as the user's age, occupation, lifestyle, and home environment.

[1409] "Means of obtaining information on medical services and medications available in the area based on location information" refers to technologies and systems that identify the user's current location and obtain information on medical services and medications available in that area.

[1410] "Means for determining medication selection based on the user's emotional state" refers to an algorithm or system that takes into account the user's emotional state and selects medication appropriate for that state.

[1411] The following system configuration will be described as an embodiment of the present invention.

[1412] System Configuration

[1413] This system includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for recognizing the user's emotions, a means for making decisions taking into account the local medical situation and the user's living situation, a means for obtaining information on medical services and medications available in the area based on the user's location information, and a means for determining the choice of medication based on the user's emotional state.

[1414] Hardware and software used

[1415] Smartphone: Used for user input and emotion recognition.

[1416] EmotionEngine: Software for recognizing user emotions.

[1417] HealthcareAI: AI that suggests medications based on the user's health and emotional state.

[1418] API: External service for obtaining local medical information.

[1419] Data processing and calculation

[1420] User health status input

[1421] Users can input their current health condition and symptoms via their smartphone, for example, specific symptoms such as headache or fatigue.

[1422] emotion recognition

[1423] The EmotionEngine analyzes images taken with a smartphone camera and recognizes the user's emotional state. For example, if the user is feeling stressed, that information will be recognized.

[1424] Obtaining local medical information

[1425] The API is called based on the user's location information to obtain information on medical services and medications available in the area. For example, if a user lives in Tokyo, information on medications available in Tokyo will be obtained.

[1426] Medication suggestions

[1427] Healthcare AI takes into account health status, emotional state, and local medical information to recommend the most appropriate medication. For example, if a user has a headache, fatigue, and is also feeling stressed, a medication with a stress-reducing effect will be suggested.

[1428] Specific examples

[1429] If the user is a 45-year-old busy person living in Tokyo who is experiencing headaches and fatigue, the emotion engine will recognize from an image taken with a smartphone camera that the person is feeling stressed. Based on local medical information, the AI ​​will suggest medications available in Tokyo that are effective in reducing stress.

[1430] Prompt Sentence Examples

[1431] If the user is a 45-year-old busy person living in Tokyo who is experiencing headaches and fatigue, the emotion engine will recognize from an image taken with a smartphone camera that the person is feeling stressed. Based on local medical information, the AI ​​will suggest medications available in Tokyo that are effective in reducing stress.

[1432] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1433] Step 1:

[1434] Users use their smartphones to input their current health status and symptoms. The input data includes specific symptoms (e.g., headache, fatigue), age, lifestyle habits, etc. This allows information about the user's health status to be collected.

[1435] Step 2:

[1436] The device uses the smartphone camera to take a picture of the user's face and sends the image to the Emotion Engine, which analyzes the image and recognizes the user's emotional state (e.g., stress, joy, sadness). The recognized emotional state is output.

[1437] Step 3:

[1438] The server acquires the user's location information and calls the local medical information API based on that location information. The API acquires information on medical services and medications available in the user's current location, and outputs the acquired local medical information.

[1439] Step 4:

[1440] The server integrates the user's health condition, emotional state, and local medical information and sends it to Healthcare AI. Healthcare AI processes and calculates this data to select the most suitable medication for the user, and outputs the most suitable medication recommendation.

[1441] Step 5:

[1442] The device displays the medication suggestions received from the server to the user, who can then check the suggested medication information and purchase it at a pharmacy if necessary. This allows the user to select the medication that best suits their health and emotional state.

[1443] 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.

[1444] 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> ) 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.

[1445] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1446] 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.

[1447] [Third embodiment]

[1448] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1449] 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.

[1450] 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).

[1451] 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.

[1452] 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.

[1453] 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).

[1454] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1460] "Example 1"

[1461] One embodiment of the present invention is a system that includes AI that determines the user's health condition and the appropriate medication to use when they are feeling unwell. In this system, the AI ​​determines the appropriate medication to use based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.).

[1462] "Example 2"

[1463] Additionally, the AI ​​takes into account interactions between over-the-counter and prescription medications. For example, if a user is already taking a specific prescription drug, the AI ​​will avoid over-the-counter medications that may interact with the prescription drug.

[1464] "Example 3"

[1465] The AI ​​also makes decisions that take into account the state of local medical care and the user's lifestyle. For example, if the medical services and types of medicine available vary by region, the AI ​​will make decisions based on that information. The AI ​​will also suggest medication options that are appropriate for the user's lifestyle, such as if the user is elderly or if both partners are busy working.

[1466] The processing flow of each embodiment will be described below.

[1467] "Example 1"

[1468] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.).

[1469] Step 2: Based on the information received, the AI ​​determines the appropriate medication.

[1470] Step 3: Present the AI's decision to the user.

[1471] "Example 2"

[1472] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.) and over-the-counter medication information.

[1473] Step 2: Based on the information received, the AI ​​makes a decision taking into account interactions between over-the-counter and prescribed medications.

[1474] Step 3: Present the AI's decision to the user.

[1475] "Example 3"

[1476] Step 1: The system receives input information from the user (e.g., physical condition, existing prescription medications, age, gender, etc.), the local medical situation, and the user's living situation.

[1477] Step 2: Based on the information received, the AI ​​makes a decision taking into account the local medical situation and the user's living situation.

[1478] Step 3: Present the AI's decision to the user.

[1479] Example 1

[1480] Next, a description will be given of Example 1 of Form 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."

[1481] Conventional drug selection systems have difficulty selecting the appropriate drug based on the user's health condition or when feeling unwell, and a particular issue is that they do not take into account interactions between over-the-counter and prescription drugs, the state of local medical care, or the user's living situation. This increases the risk that users will select the wrong drug, so a more accurate drug selection system is needed.

[1482] 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.

[1483] In this invention, the server includes means for a user to input information about their health condition or poor physical condition, means for a terminal to send the input information to the server, means for the server to receive the input information and store it in a database, means for the server to input the stored data into an AI model, means for the AI ​​model to determine an appropriate medication, means for the server to receive the results of the AI ​​model and return them to the user, and means for the user to check the results via the terminal. This makes it possible to select an appropriate medication for a user's health condition or poor physical condition, and enables highly accurate judgments that take into account interactions between over-the-counter and prescription drugs, the state of local medical care, and the user's living situation.

[1484] "User" refers to an individual who uses the system to input information about their health or ill health.

[1485] "Terminal" refers to a device through which a user inputs information and communicates with a server. Examples include smartphones and personal computers.

[1486] "Server" refers to a central computer system that receives and processes information submitted by users.

[1487] "Database" refers to a system that allows a server to store information received from users. Examples include MySQL and PostgreSQL.

[1488] An "AI model" is an algorithm built using a machine learning framework that determines the appropriate medication based on the user's health condition or physical condition.

[1489] "Health Condition" refers to a user's current physical condition or symptoms. Examples include headache and fever.

[1490] "Illness" refers to any physical discomfort or abnormality experienced by the user. Examples include nausea and fatigue.

[1491] "Input Information" refers to data provided by the User to the System via the Terminal, including, for example, symptoms, existing prescription medications, age, and gender.

[1492] "Appropriate medication" refers to the most effective and safe medication that the AI ​​model selects based on the user's health condition or condition.

[1493] "Results" refers to the information about the appropriate medication determined by the AI ​​model and the data returned to the user.

[1494] This invention is a system for selecting appropriate medications for a user's health condition or when they are feeling unwell. This system uses an AI model to determine the appropriate medication based on information entered by the user and returns the results to the user.

[1495] First, the user uses a device such as a smartphone or computer to enter information about their health condition or physical condition into the system's input form. For example, they enter information such as "I have a headache," "My current prescription is aspirin," "I'm 30 years old," and "I'm male."

[1496] The terminal then transmits the information entered by the user to the server, which does so securely using the HTTPS protocol.

[1497] The server receives the information sent from the device and stores it in a database such as MySQL or PostgreSQL. The stored data includes information such as the user ID, symptoms, existing prescription medications, age, and gender.

[1498] The server retrieves the information stored in the database and inputs it into an AI model built using machine learning frameworks such as TensorFlow and PyTorch, which then compares it with past data to determine the most appropriate medication for the user's health condition.

[1499] After the AI ​​model selects a medication, the server returns the results to the user, which are displayed in a user-friendly format, such as a message like "We recommend using ibuprofen."

[1500] As a concrete example, consider the case where a user inputs "I have a headache." The user inputs that he / she is taking "aspirin" as a prescription drug. The user's age is 30 and his / her gender is male.

[1501] The server receives this information and stores it in a database. The server then inputs this data into an AI model, which uses past data to determine the most appropriate medication for the user's symptoms. In this case, the AI ​​model might select "ibuprofen."

[1502] The server returns the result to the user, who receives a message via the terminal saying, "We recommend using ibuprofen."

[1503] An example of a prompt sentence to input to the generative AI model is, "A 30-year-old male currently taking aspirin as a prescription medication. Currently suffering from a headache. Please select the medication that is most appropriate for this user."

[1504] The above is an embodiment of the present invention.

[1505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1506] Step 1:

[1507] The user inputs information about their health condition or ill health.

[1508] Specifically, the user opens the application on their smartphone or computer and goes to the "Symptom Input" screen, where they enter information such as "I have a headache," "My current prescription medication is aspirin," "I'm 30 years old," and "I'm male."

[1509] Input: User's health status and ill health information

[1510] Output: Input information is saved on the device

[1511] Step 2:

[1512] The terminal sends the input information to the server.

[1513] Specifically, when the user presses the "Send" button, the terminal sends the input information to the server using the HTTPS protocol.

[1514] Input: Input information saved on the device

[1515] Output: Input sent to the server

[1516] Step 3:

[1517] The server receives the input information and stores it in a database.

[1518] Specifically, the server analyzes the information it receives and stores information such as user ID, symptoms, existing prescription medications, age, and gender in a database such as MySQL or PostgreSQL.

[1519] Input: The input sent to the server

[1520] Output: Information stored in the database

[1521] Step 4:

[1522] The server inputs the stored data into the AI ​​model.

[1523] Specifically, the server retrieves information corresponding to the "user ID" from the database and inputs it into an AI model built using machine learning frameworks such as TensorFlow and PyTorch.

[1524] Input: Information stored in a database

[1525] Output: Data input into the AI ​​model

[1526] Step 5:

[1527] The AI ​​model determines the appropriate medication.

[1528] Specifically, the AI ​​model compares the data with past data to determine the medication that best suits the user's health condition. In this case, the AI ​​model might select "ibuprofen."

[1529] Input: Data fed into the AI ​​model

[1530] Output: Determined appropriate medication

[1531] Step 6:

[1532] The server receives the results of the AI ​​model and returns them to the user.

[1533] Specifically, the server receives the results from the AI ​​model, generates a message saying "We recommend using ibuprofen," and returns it to the user.

[1534] Input: Results from the AI ​​model

[1535] Output: The message returned to the user

[1536] Step 7:

[1537] The user checks the results through the terminal.

[1538] Specifically, the user opens the app on their smartphone and sees the message "It is recommended to use ibuprofen" on the "Results Display" screen.

[1539] Input: The message returned by the server

[1540] Output: The result that the user sees

[1541] The above is a concrete explanation of the processing flow of the program of this system.

[1542] (Application example 1)

[1543] Next, a description will be given of Application Example 1 of Form 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."

[1544] Conventional drug selection systems have difficulty selecting the appropriate drug based on the user's health condition or when feeling unwell. Furthermore, when users try to purchase the recommended drug at a physical store, they have the problem of not knowing which store they can purchase it at. Furthermore, since they cannot consider interactions between over-the-counter and prescription drugs or the state of local medical care, it is difficult to select the optimal drug for the user.

[1545] 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.

[1546] In this invention, the server includes a means including AI for determining the user's health condition and the appropriate medication to select when feeling unwell, a means for the AI ​​to determine the appropriate medication based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.), and a means for displaying the recommended medication as available for purchase at a nearby physical store. This allows the user to select the appropriate medication based on their own health condition and makes it easy to purchase at a physical store. It also makes it possible to make decisions taking into account interactions between over-the-counter and prescription medications and the local medical situation.

[1547] A "user" is an individual who uses the system to select medications for their own health condition and when they are feeling unwell.

[1548] "Health status" refers to the user's current physical and mental state, including physical ailments and pre-existing medical history.

[1549] "Illness" refers to physical discomfort or abnormalities felt by the user, and specifically includes symptoms such as headache, fever, and stomachache.

[1550] "Medicine selection" refers to the act of selecting an appropriate medicine for the user's health condition or when they are feeling unwell.

[1551] "AI" refers to artificial intelligence, which includes algorithms and models for selecting appropriate medications based on user input.

[1552] "Input information" refers to data provided by the user to the system, and specifically includes information such as physical condition, existing prescription medications, age, and gender.

[1553] "Appropriate medication" refers to medication that is effective for the user's health condition or physical condition and can be used safely.

[1554] "Brick and mortar store" refers to a physical store where a user can purchase recommended medications.

[1555] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[1556] "Prescription drugs" refers to drugs that require a doctor's prescription.

[1557] "Drug interactions" refers to interactions that occur when multiple drugs are taken at the same time, which may change the effectiveness or side effects of the drugs.

[1558] "Regional medical situation" refers to the state of medical service provision and availability of medical resources in the area where the user lives.

[1559] "Life situation" refers to the environment and conditions in the user's daily life, and specifically includes situations such as elderly people and dual-income households.

[1560] A system for implementing this invention includes AI that determines the user's health condition and the medication to be selected when they are feeling unwell. The system uses AI to determine the appropriate medication based on information input by the user (e.g., physical condition, existing prescription medications, age, gender, etc.), and displays the recommended medication as available for purchase at a nearby physical store.

[1561] Hardware and software used

[1562] Hardware: Smartphone

[1563] Software: Python, Requests library (for API requests), AI models (e.g., TensorFlow, PyTorch)

[1564] Data processing and calculation

[1565] 1. Getting user input:

[1566] Users enter information about their health condition and symptoms of illness through a smartphone app, including symptoms, age, gender, and current prescription medications.

[1567] 2. AI-based drug selection:

[1568] The server sends the input information obtained from the user to the AI ​​model, which then selects the appropriate medication. The AI ​​model then takes into account the user's symptoms and interactions with existing prescription medications to recommend the most appropriate medication.

[1569] 3. Recommended medication indication:

[1570] The server displays the recommended medicines to the user and provides information on nearby physical stores where they can be purchased, allowing the user to immediately purchase the recommended medicines.

[1571] Specific examples

[1572] For example, if a user inputs "I have a headache," their age is 30, their gender is male, and they have no existing prescription medications, the AI ​​will recommend "acetaminophen." The server will then display the recommended medication to the user and provide information on where it can be purchased at nearby pharmacies.

[1573] Prompt Sentence Examples

[1574] User symptoms: Headache

[1575] Age: 30

[1576] Gender: Male

[1577] Current prescription medications: None

[1578] Data to send to the AI ​​model:

[1579] {

[1580] "symptoms": "headache",

[1581] "age": 30,

[1582] "gender": "male",

[1583] "existing_medications": "none"

[1584] }

[1585] In this way, a system can be realized that selects appropriate medications based on the user's health condition and supports purchases in physical stores.

[1586] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1587] Step 1:

[1588] Users launch the smartphone app and enter their health status and symptoms of illness, including symptoms, age, gender, and current prescription medications. The entered information is then stored in the application's database.

[1589] Input: User's symptoms, age, gender, existing prescription medications

[1590] Output: Saved user input information

[1591] Step 2:

[1592] The device sends the saved user input information to the server, which receives the data and converts it into a format that can be input into the AI ​​model.

[1593] Input: User input information

[1594] Output: Data in a format that can be fed into an AI model

[1595] Step 3:

[1596] The server inputs the converted data into an AI model to select the appropriate medication. The AI ​​model then takes into account the user's symptoms and interactions with existing prescription medications to recommend the most appropriate medication.

[1597] Input: Data in the format that is fed into the AI ​​model

[1598] Output: Recommended medication information

[1599] Step 4:

[1600] The server receives the recommended medication information and generates data to display to the user, including the name of the recommended medication, its effects, side effects, and availability at nearby physical stores.

[1601] Input: Recommended medication information

[1602] Output: Data to display to the user

[1603] Step 5:

[1604] The terminal displays the data received from the server to the user, who can then check the recommended medicines and obtain information on purchasing them at a nearby physical store.

[1605] Input: The data to display to the user

[1606] Output: Medication information displayed to the user

[1607] Step 6:

[1608] Based on the displayed information, users can purchase the recommended medication at a nearby brick-and-mortar store, and if necessary, they can use the chat function to ask additional questions to a pharmacist.

[1609] Input: Medication information displayed to the user

[1610] Output: Purchase of medicines and answers to follow-up questions

[1611] Example 2

[1612] Next, a description will be given of Example 2 of Form 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."

[1613] Conventional drug selection systems have difficulty considering interactions between prescription and over-the-counter drugs taken by users, resulting in health risks due to incorrect drug selection. Furthermore, since they are unable to consider the state of local medical care or the user's living situation, it is difficult to select the optimal drug for each individual user. This has led to the issue of being unable to properly manage the user's health condition.

[1614] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1615] In this invention, the server includes a means for a user to input prescription drug information, a means for a terminal to transmit the input information to the server, a means for the server to search an over-the-counter drug database, a means for the server to evaluate the risk of drug interactions using a generative AI model, a means for the server to return the evaluation results to the terminal, and a means for the terminal to display the evaluation results to the user. This enables selection of drugs that take into consideration interactions between prescription drugs and over-the-counter drugs taken by the user, and further allows for decisions to be made that take into consideration the state of local medical care and the user's living situation, making it possible to select the most appropriate drug for each individual user.

[1616] "User" refers to an individual who utilizes the system to enter prescription drug information and obtain information regarding drug selection.

[1617] "Terminal" refers to the device through which the user enters prescription drug information and communicates with the server. Specifically, this includes smartphones and personal computers.

[1618] "Server" refers to the computer system that receives user-submitted information, searches a database of commercially available drugs, and uses a generative AI model to assess the risk of interactions.

[1619] "Over-the-counter drug database" refers to a collection of data including over-the-counter drug names, ingredients, and interaction information.

[1620] "Generative AI models" refer to artificial intelligence models that analyze prescription drug information and over-the-counter drug ingredient information entered by users and evaluate the risk of drug interactions. Specifically, they include models that use natural language processing technology.

[1621] "Risk of interaction" refers to the potential health risks that may arise when prescription and over-the-counter medications are taken at the same time.

[1622] "Evaluation results" refers to information about interactions between prescription and over-the-counter drugs obtained as a result of analysis by the generative AI model.

[1623] "Regional medical situation" refers to the state of medical service provision and the state of medical resource utilization in the area where the user resides.

[1624] "Life situation" refers to factors that affect daily life, such as the user's age, occupation, and home environment.

[1625] The present invention provides a system for assisting a user in selecting medications that take into consideration interactions between prescription and over-the-counter medications. Specific embodiments of this system are described below.

[1626] First, the user enters prescription drug information using a terminal. The terminal can be a smartphone or a PC. The prescription drug information entered by the user is sent from the terminal to the server. At this time, the data is encrypted before being sent, ensuring security.

[1627] The server searches an over-the-counter drug database based on the received prescription drug information. The over-the-counter drug database contains information such as the names, ingredients, and drug interactions of over-the-counter drugs. Using a generative AI model, the server analyzes the ingredient information of prescription and over-the-counter drugs and evaluates the risk of drug interactions.

[1628] The generative AI model uses natural language processing technology, such as GPT-4, to assess the risk of interactions between prescription and over-the-counter drugs by inputting a prompt.

[1629] For example, if the user is taking "aspirin," the server will input the following prompt into the generative AI model:

[1630] If the user is taking aspirin, identify any over-the-counter medications that may interact.

[1631] Based on this prompt, the generative AI model identifies over-the-counter medications that may interact with aspirin and assesses their risk, resulting in the information that "taking aspirin and ibuprofen at the same time increases the risk of bleeding and should be avoided."

[1632] The server returns the evaluation results to the terminal, which then displays the evaluation results received from the server to the user. The user can then check the evaluation results on the application screen and select an appropriate medication.

[1633] This system allows users to select medications that take into account interactions between prescription and over-the-counter medications, thereby reducing health risks.It also takes into account the local medical situation and the user's living situation, allowing for the selection of medications that are optimal for each individual user.

[1634] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1635] Step 1:

[1636] The user enters prescription information.

[1637] The user opens the application on their smartphone or computer and enters the name and ingredient information of the prescription medication they are currently taking. For example, the user enters "aspirin" and presses the send button. The input data is the name and ingredient information of the prescription medication.

[1638] Step 2:

[1639] The terminal transmits the input information to the server.

[1640] The terminal encrypts the prescription drug information entered by the user and sends it to the server. Once transmission is complete, the terminal waits for a response from the server. The input data is the encrypted prescription drug information, and the output data is the prescription drug information sent to the server.

[1641] Step 3:

[1642] The server searches the over-the-counter drug database.

[1643] The server searches the over-the-counter drug database based on the received prescription drug information. The database contains the names, ingredients, and interaction information of over-the-counter drugs. The input data is the prescription drug information, and the output data is a list of corresponding over-the-counter drugs.

[1644] Step 4:

[1645] The server uses a generative AI model to assess the risk of the interaction.

[1646] The server inputs a prompt into the generative AI model and analyzes the ingredient information of prescription and over-the-counter drugs. For example, the prompt might read, "If the user is taking aspirin, please identify any over-the-counter drugs that may interact with it." The generative AI model evaluates the risk of interactions between prescription and over-the-counter drugs. The input data is the prompt and a list of over-the-counter drugs, and the output data is the interaction risk assessment results.

[1647] Step 5:

[1648] The server returns the evaluation results to the terminal.

[1649] The server returns the results of the generative AI model's evaluation to the device. The evaluation results include interaction risks and recommended countermeasures. The input data is the interaction risk evaluation results, and the output data is the evaluation results sent to the device.

[1650] Step 6:

[1651] The terminal displays the evaluation results to the user.

[1652] The terminal displays the evaluation results received from the server to the user. The user can check the evaluation results on the application screen and select an appropriate medication. The input data is the evaluation results received from the server, and the output data is the evaluation results displayed to the user.

[1653] (Application example 2)

[1654] Next, a description will be given of Application Example 2 of Form 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."

[1655] Conventional drug selection systems have difficulty considering interactions between prescription drugs and over-the-counter drugs that users are already taking, resulting in health risks due to incorrect drug selection. Another problem is that users have to spend time and effort checking for interactions when purchasing drugs. Furthermore, because they are unable to consider the state of local medical care or the user's living situation, it is difficult to select the optimal drug for each individual user.

[1656] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence means for determining the user's health condition and the selection of medication when feeling unwell, a means for making a decision taking into account interactions between over-the-counter drugs and prescribed medications, a means for scanning barcodes of over-the-counter drugs, a means for registering prescription drugs, a means for checking for interactions, a means for allowing electronic payment if no interactions exist, and a means for displaying a warning if an interaction exists. This allows interactions between prescription drugs and over-the-counter drugs already taken by the user to be checked in real time, enabling appropriate drug selection and safe purchase. Furthermore, since the server can make decisions taking into account the local medical situation and the user's living situation, it is possible to select the most appropriate medication for each individual user.

[1657] "User" refers to an individual who uses the system to select and purchase medications.

[1658] "Health" refers to a user's current physical and mental state.

[1659] "Illness" refers to any discomfort or symptoms of illness experienced by the user.

[1660] "Artificial intelligence" refers to technology that allows computer systems to imitate human intelligence to make judgments and learn.

[1661] "Over-the-counter" refers to drugs that can be purchased without a prescription.

[1662] "Prescription drugs" refers to drugs that require a doctor's prescription.

[1663] Drug interactions refer to interactions that occur when multiple medications are taken at the same time.

[1664] "Barcode" refers to a code consisting of a series of lines and numbers that contains product information.

[1665] "Scanning" refers to the act of reading a barcode.

[1666] "Registration" refers to the act of a user entering and saving information in the system.

[1667] "Drug interaction" refers to the chemical or physiological effects that occur when two or more drugs are taken at the same time.

[1668] "Electronic payments" refers to monetary transactions conducted over the Internet.

[1669] A "warning" refers to a message that alerts the user.

[1670] "Community medical care" refers to the state of medical service provision in a specific area.

[1671] "Life situation" refers to the user's living environment and lifestyle.

[1672] A "generative AI model" refers to a model that allows artificial intelligence to generate new information and content based on data.

[1673] A "prompt" refers to an instruction sentence input into a generative AI model.

[1674] As an embodiment of the present invention, the following system is constructed.

[1675] System Configuration

[1676] The system includes artificial intelligence (AI) to determine the user's health condition and the appropriate medication when they are unwell. Specifically, the system uses the following hardware and software:

[1677] Hardware

[1678] Smartphone: Used by users to scan barcodes on over-the-counter medications.

[1679] Server: Runs the AI ​​model and manages the database.

[1680] software

[1681] Python: The main programming language.

[1682] OpenCV: Used to scan barcodes using a smartphone camera.

[1683] Barcode: Used to generate and read barcodes.

[1684] Requests: Used to access the drug database through API.

[1685] Processing flow

[1686] 1. Barcode Scanning: A user scans the barcode of an over-the-counter medication using the smartphone camera. The OpenCV library is used to capture the camera image and read the barcode.

[1687] 2. Prescription drug registration: The user registers the prescription drugs they are currently taking in the app, which allows the system to store the user's prescription drug information in a database.

[1688] 3. Interaction check: Based on the scanned over-the-counter drug information, AI checks for interactions with prescription drugs. If an interaction is found, a warning is displayed to the user and the purchase is canceled. If there is no interaction, electronic payment is allowed.

[1689] 4. Electronic Payment: If no interactions are determined, users can purchase over-the-counter medications using their smartphones.

[1690] Specific examples

[1691] For example, if a user attempts to purchase an over-the-counter drug "drugX," they scan the barcode of "drugX" with their smartphone's camera. The system checks whether "drugX" interacts with "drugA," a prescription drug registered by the user. If there is an interaction, the app displays a warning and cancels the purchase. If there is no interaction, the user can purchase "drugX" through electronic payment.

[1692] Prompt Sentence Examples

[1693] "Create an application that helps users choose the right over-the-counter medication by checking its interactions with prescription medications they are already taking when purchasing them. Include a feature that scans the barcode of over-the-counter medications and checks for interactions with prescription medications in real time."

[1694] In this way, we have created a system that checks interactions between prescription drugs and over-the-counter drugs in real time, enabling appropriate drug selection and safe purchase. Furthermore, since the system can take into account the local medical situation and the user's living situation, it is possible to select the most appropriate drug for each individual user.

[1695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1696] Step 1:

[1697] A user scans the barcode of an over-the-counter drug using the smartphone camera. The input is the barcode of the over-the-counter drug, and the output is the information about the over-the-counter drug read from the barcode. Specifically, the smartphone camera is activated, the barcode is captured using the OpenCV library, and the drug information is obtained using the barcode library.

[1698] Step 2:

[1699] The device registers the prescription medications that the user is currently taking. The input is the prescription medication information entered by the user, and the output is the prescription medication information stored in the database. Specifically, the user enters the name and dosage of the prescription medication into the app's input form, and the information is sent to the server and stored in the database.

[1700] Step 3:

[1701] The server checks for interactions based on the scanned over-the-counter drug information and registered prescription drug information. The input is over-the-counter drug information and prescription drug information, and the output is a judgment result regarding whether or not an interaction exists. Specifically, the server compares the over-the-counter drug and prescription drug databases and evaluates the possibility of interactions using an AI model.

[1702] Step 4:

[1703] The server displays a warning to the user based on whether or not there is an interaction. The input is the interaction judgment result, and the output is a warning message to the user. Specifically, if there is an interaction, the app displays a warning message to the user and stops the purchase.

[1704] Step 5:

[1705] The server allows electronic payment if there is no interaction. The input is the result of the judgment that there is no interaction, and the output is a notification that the electronic payment has been completed. Specifically, the app displays a purchase confirmation message to the user, and when the user presses the confirm button, the electronic payment is made.

[1706] Step 6:

[1707] The server saves the purchase history in a database. The input is information about the purchased over-the-counter drug and the user's information, and the output is updated purchase history data. Specifically, after the electronic payment is completed, the information about the purchased over-the-counter drug is added to the database and the user's purchase history is updated.

[1708] Example 3

[1709] Next, a third embodiment of the third embodiment will be described. 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."

[1710] Conventional medical systems have had the problem of being unable to select medical services and medications that fully take into account the user's individual living situation and the state of local medical care. It was particularly difficult to propose appropriate medical services to elderly users and dual-income households. Furthermore, decisions that took into account interactions between over-the-counter and prescription drugs were also insufficient.

[1711] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1712] In this invention, the server includes: means for a user to input information such as their age, occupation, and lifestyle using a terminal; means for collecting data on regional medical services and types of medication; means for storing the collected regional medical data and the user's living situation data in a database; means for training a generative AI model based on the data stored in the database; means for generating prompt sentences based on the user's living situation and regional medical data; and means for the generative AI model to suggest optimal medical services and medications. This enables optimal medical services and medications to be selected taking into account the user's individual living situation and the local medical situation.

[1713] "User" refers to an individual who uses the system to input information about their health and lifestyle.

[1714] "Terminal" refers to an electronic device that a user uses to input information.

[1715] "Server" refers to a central processing unit that receives information from users and collects, stores, and processes data.

[1716] "Database" refers to an information management system for storing collected regional medical data and user living situation data.

[1717] A "generative AI model" refers to an artificial intelligence model that is trained based on data stored in a database and suggests optimal medical services and medication options.

[1718] A "prompt sentence" refers to an instruction sentence that includes the user's living situation and local medical data and is input to the generative AI model.

[1719] "Medical Services" refers to medical treatments and procedures provided to improve the health of a User.

[1720] "Medicine" refers to a pharmaceutical product used to improve the User's physical condition.

[1721] "Regional medical data" refers to information about medical services and types of medicines in a particular region.

[1722] "Lifestyle data" refers to individual information such as the user's age, occupation, and lifestyle.

[1723] This invention is a system that proposes optimal medical services and drug selections in consideration of the user's living situation and the state of local medical care. Specific embodiments of this system will be described below.

[1724] First, the user uses a device to input information such as their age, occupation, and lifestyle. The device can be an electronic device such as a smartphone, tablet, or PC. For example, if the user is 70 years old and both partners work and lead busy lives, they can input that information into the device.

[1725] Next, the server collects data on medical services and medications in each region. The server uses a server equipped with a high-performance GPU (e.g., a server equipped with a high-performance graphics processing unit) to obtain regional medical data from the Internet. For example, the server collects information on Hospital A and Pharmacy B and stores it as regional medical data.

[1726] The server stores the collected regional medical data and user living situation data in a database management system (e.g., MySQL). For example, the server might store data such as "User ID: 123, Age: 70, Occupation: Dual-income household, Region: X City, Available medical services: Hospital A, Pharmacy B" in the database.

[1727] Next, the server uses a machine learning framework (e.g., TensorFlow) to train a generative AI model based on the data stored in the database. The generative AI model receives the user's living situation and local medical data as input and suggests the optimal medical service and medication selection. For example, the server learns patterns such as "if the user is elderly and both partners are busy working, a home visit medical service would be appropriate."

[1728] The server generates a prompt based on the user's living situation and local medical data. For example, the server generates the following prompt:

[1729] Example prompt sentence:

[1730] The user is a 70-year-old elderly person who leads a busy life as both partners work. In the area where the user lives, Hospital A and Pharmacy B are available. Taking this situation into consideration, please suggest the best medical service and medication selection.

[1731] Based on this prompt, the generative AI model will make a suggestion, for example, "Use the home visit service of Hospital A and receive the prescribed medicine at Pharmacy B via home delivery service." The user can receive this suggestion through their device and make arrangements for medical services and medicine as needed.

[1732] In this way, the present invention enables selection of optimal medical services and medicines taking into consideration the individual living conditions of the user and the state of local medical care. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1733] Step 1:

[1734] A user uses a terminal to input information such as their age, occupation, and lifestyle. For example, if the user is 70 years old and both partners work and lead busy lives, they input this information into the terminal. The input data is the user's individual information such as age, occupation, and lifestyle. The output is the transmission of this information from the terminal to the server.

[1735] Step 2:

[1736] The server collects data on medical services and types of medications in each region. The server obtains regional medical data via the Internet. For example, the server collects information on Hospital A and Pharmacy B and stores it as regional medical data. The input data is information about the region, and the output is the collected data on medical services and medications.

[1737] Step 3:

[1738] The server stores the collected regional medical data and user living situation data in a database. The server uses a database management system (e.g., MySQL) to store this data. For example, the server might store data such as "User ID: 123, Age: 70, Occupation: Dual-income household, Area: X City, Available medical services: Hospital A, Pharmacy B" in the database. The input data is the collected regional medical data and user living situation data, and the output is the data stored in the database.

[1739] Step 4:

[1740] The server uses a machine learning framework (e.g., TensorFlow) to train a generative AI model based on data stored in a database. The server receives the user's living situation and local medical data as input, and trains a model to suggest optimal medical services and medication options. For example, the server learns patterns such as "if the user is elderly and both partners are busy working, home visit medical services are appropriate." The input data is the data stored in the database, and the output is the trained generative AI model.

[1741] Step 5:

[1742] The server generates a prompt based on the user's living situation and local medical data. For example, the server generates the following prompt:

[1743] Example prompt sentence:

[1744] The user is a 70-year-old elderly person who leads a busy life as both partners work. In the area where the user lives, Hospital A and Pharmacy B are available. Taking this situation into consideration, please suggest the best medical service and medication selection.

[1745] The input data is the user's living situation and local medical data, and the output is the generated prompt sentence.

[1746] Step 6:

[1747] The generative AI model suggests optimal medical services and medications based on the prompt text. For example, the generative AI model might suggest "Use the home visit service of Hospital A and receive the medication prescribed at Pharmacy B via home delivery." The input data is the generated prompt text, and the output is the suggested medical services and medication selections. The user receives this suggestion via their device and can make arrangements for medical services and medications as needed.

[1748] (Application example 3)

[1749] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1750] Conventional medical systems do not adequately consider the user's health condition and lifestyle when selecting medications or suggesting meal menus. This can make it difficult for users to receive appropriate medical services and meals, leading to inadequate health management. Furthermore, since available medical services and ingredients vary by region, decisions must be made taking into account information specific to that region. To solve these issues, a system is needed that can make optimal suggestions based on the user's individual circumstances.

[1751] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1752] In this invention, the server includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for making a decision taking into consideration interactions between over-the-counter medications and prescribed medications, a means for making a decision taking into consideration the state of local medical care and the user's living situation (e.g., elderly, dual-income households, etc.), and a means for proposing an optimal meal menu taking into consideration the user's health condition and living situation. This makes it possible to propose optimal medical services and meal menus according to the user's health condition and living situation.

[1753] "User's health condition" refers to the illnesses, allergies, and poor physical condition that the user currently has.

[1754] "Choosing medicine when feeling unwell" refers to the act of a user selecting an appropriate medicine when they feel unwell.

[1755] "Over-the-counter" refers to medicines that can be purchased without a doctor's prescription.

[1756] "Prescribed medication" refers to medication that is prescribed by a doctor as a result of a medical examination and dispensed by a pharmacy.

[1757] "Drug interactions" refers to the interactions and effects that occur when multiple medications are taken at the same time.

[1758] "Regional medical situation" refers to the state of medical service provision and availability of medical resources in a particular region.

[1759] "User's living situation" refers to factors that affect the user's daily life, such as the user's age, occupation, and home environment.

[1760] "Meal menu" refers to the contents and combination of meals that a user will eat.

[1761] "Ingredients" refers to the raw materials used to make a meal.

[1762] "Suggestion" refers to the act of showing the user the best option.

[1763] To implement this invention, it is necessary to build a system that proposes optimal medical services and meal menus in consideration of the user's health condition and lifestyle. This system includes a means for determining the user's health condition and the choice of medication when feeling unwell, a means for making a decision taking into consideration interactions between over-the-counter drugs and prescribed drugs, a means for making a decision taking into consideration the state of local medical care and the user's lifestyle, and a means for proposing optimal meal menus in consideration of the user's health condition and lifestyle.

[1764] System configuration

[1765] Hardware

[1766] Server: A central server for processing and storing data.

[1767] Smartphone: A device that allows users to enter information and receive suggestions.

[1768] Network: The communications infrastructure that connects the server and smartphones.

[1769] software

[1770] Python: A programming language used for data processing and implementing AI models.

[1771] JSON: The data format used to format user and region data.

[1772] Generative AI model: An AI model that takes into account the user's health condition and lifestyle to make optimal suggestions.

[1773] Processing the data

[1774] The server receives input about the user's health condition and lifestyle, and then proposes optimal medical services and meal plans based on this information. Specifically, it performs the following data processing and calculations:

[1775] 1. Entering user data: Users use their smartphones to enter their health status (e.g., diabetes, high blood pressure) and living situation (e.g., dual-income households, elderly people).

[1776] 2. Obtaining regional data: The server obtains information on medical services and available food ingredients in each region.

[1777] 3. Data integration: Integrate user data and regional data, and use generative AI models to suggest optimal medical services and meal menus for users.

[1778] Specific examples

[1779] For example, if the user is a busy, dual-income family with diabetes and high blood pressure, the system will suggest the best menu based on locally availa...

Claims

1. an emotion engine that recognizes the user's emotions; A selection means for selecting a candidate drug that will improve the user's physical condition by inputting a specific prompt sentence into a generative AI model, based on information including the user's health condition, the user's prescribed drugs, the user's age, the user's sex, and the emotion, and taking into consideration interactions with the prescribed drugs; A means for displaying the selected drug and information indicating the inventory status of the selected drug at a physical store on the user's terminal; A system including:

2. The system according to claim 1 , wherein the selection means selects candidate drugs that will improve the user's physical condition, taking into consideration the user's lifestyle habits and interactions with the prescribed drugs.

3. The system according to claim 2 , wherein the selection means selects candidate drugs that will improve the user's physical condition, taking into consideration local medical services in which the user lives, the user's lifestyle habits, and interactions with the prescribed drugs.

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