System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input symptoms; generation and AI means for analyzing the symptoms; means for the generation and AI means to suggest appropriate non-prescription drugs based on an analysis result; and means for displaying detailed information of the suggested non-prescription drugs.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] Many consumers are confused about which over-the-counter medications to choose, and there is a risk of choosing the wrong one. This is a particularly serious problem for users with little specialized knowledge, and there is a need for support in selecting the right over-the-counter medication for their symptoms. There is also a need for detailed information about over-the-counter medications, including information on precautions for use, side effects, and when to seek medical advice. [Means for solving the problem]

[0005] The present invention provides a system including a means for a user to input symptoms, a generating AI means for analyzing the symptoms, a means for the generating AI means to suggest appropriate over-the-counter drugs based on the analysis results, and a means for displaying detailed information about the suggested over-the-counter drugs.The system also includes a means for transmitting the user's symptom information to a server, and a means for the server to pass the symptom information to the generating AI means.The generating AI means also includes a means for acquiring associations between symptoms and over-the-counter drugs from an internal database to evaluate the suitability of the over-the-counter drugs, and a means for generating a candidate list of over-the-counter drugs using the acquired association information, thereby providing support for selecting over-the-counter drugs.

[0006] A "user" is someone who uses the system to input their symptoms and have appropriate over-the-counter medications suggested.

[0007] "Symptoms" refer to physical abnormalities or discomfort experienced by the user, and are the input data that the system analyzes.

[0008] "Generative AI" is an artificial intelligence tool that analyzes symptom data entered by users and suggests appropriate over-the-counter medications.

[0009] The "server" is a computer system that acts as an intermediary, receiving symptom data from the user and passing it on to the generating AI means.

[0010] "Suggestion" refers to the generative AI presenting the user with a list of appropriate over-the-counter medications based on the analysis results.

[0011] "Over-the-counter drugs" refer to general medicines sold at pharmacies and drug stores, and are used to treat specific symptoms.

[0012] "Detailed information" is additional information that users can refer to when using over-the-counter drugs, such as precautions for use, side effects, and guidelines for when to see a doctor.

[0013] The "internal database" is a database that stores information that the generative AI uses to evaluate the correlation between symptoms and over-the-counter medications.

[0014] The "candidate list" is a list of applicable over-the-counter medications selected by the generation AI based on symptoms.

[0015] "Analysis" is the process by which the generative AI processes symptom data entered by the user and evaluates the suitability of over-the-counter medications. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system that utilizes a generative AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generative AI, an internal database, and an interface.

[0038] Program processing

[0039] User symptom input

[0040] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[0041] Server: Receives symptom information sent from the terminal, checks the data format and prepares it for analysis.

[0042] Symptom analysis

[0043] Server: Makes an API call to pass the received symptom information to the generation AI.

[0044] Generative AI: Analyzes the received symptom data, references the data relating to symptoms and over-the-counter medications stored in an internal database, and generates an index for selecting appropriate over-the-counter medications.

[0045] Over-the-counter medication suggestions

[0046] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. This list includes suggested over-the-counter medication names and common uses.

[0047] Server: Receives the over-the-counter drug list returned by the generation AI, formats it for the user, and returns it to the device.

[0048] Device: Displays a list of over-the-counter medications that are best suited to the user.

[0049] Providing additional information

[0050] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0051] Terminal: Sends the user's request to the server.

[0052] Server: Refers to an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and when to see a doctor).

[0053] Terminal: Display the obtained details to the user.

[0054] Specific examples

[0055] Scenario: User enters symptoms of "sore throat" and "runny nose"

[0056] User: Launches the app, enters "sore throat" and "runny nose," and clicks the send button.

[0057] Terminal: Converts input content into JSON format and sends an API request to the server.

[0058] Server: Receives data and sends symptom data via an interface to provide to the generation AI.

[0059] Generative AI: Analyzes the received data and retrieves over-the-counter drug information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[0060] Generation AI: Returns the selection results to the server.

[0061] Server: Generates a response to return the results of the generated AI to the device.

[0062] Terminal: Show the user a list of "lozenges" and "nasal sprays."

[0063] User: If you want to know more information about "lozenge" from the list, click the More Information button.

[0064] Terminal: Sends a detailed information request to the server.

[0065] Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[0066] Device: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[0067] In this way, users can self-diagnose and select appropriate over-the-counter medications, and are also provided with detailed information to support their health management.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] User: Using a device (smartphone, tablet, PC, etc.), the user launches the application or web interface. The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button.

[0071] Step 2:

[0072] Terminal: Converts the input symptom data into JSON format and prepares an API request to send to the server. Sends the symptom data to the API endpoint (e.g., https: / / example.com / api / symptoms).

[0073] Step 3:

[0074] Server: Receives symptom data sent from the device and checks the data format. If there are no problems with the format, prepares it for transfer to the generating AI means for analysis.

[0075] Step 4:

[0076] Server: Sends symptom data to the API endpoint of the generative AI solution. Establishes a secure connection using the endpoint URL and authentication information.

[0077] Step 5:

[0078] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for the symptom "sore throat" and "nasal spray" for the symptom "runny nose."

[0079] Step 6:

[0080] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. The generated list of over-the-counter medications is returned to the server in JSON format.

[0081] Step 7:

[0082] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[0083] Step 8:

[0084] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[0085] Step 9:

[0086] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0087] Step 10:

[0088] Device: Sends a request for user details to the server.

[0089] Step 11:

[0090] Server: Receives the detailed information request, calls an internal database or an external medical information API to obtain detailed information about the drug, and returns the obtained details in JSON format to the terminal.

[0091] Step 12:

[0092] Terminal: Generates a UI to display the detailed information received from the server. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[0093] In this way, the system allows users to easily obtain information on over-the-counter medications that suit their symptoms and check the detailed information they need.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] In the past, users needed specialized knowledge to select the appropriate over-the-counter medication based on their symptoms, and using the wrong medication could pose health risks. Furthermore, obtaining detailed drug information required searching multiple sources, which was time-consuming and laborious. Therefore, there is a need for a system that allows users to easily select the appropriate over-the-counter medication and obtain detailed information about it.

[0097] 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.

[0098] In this invention, the server includes a means for converting the symptom data entered by the user into JSON format, a means for preparing the received symptom data for checking and analysis, and a means for retrieving detailed information from an internal database or an external information API and displaying it to the user, thereby enabling the user to easily select an appropriate over-the-counter medicine based on their symptoms and quickly obtain detailed information about it.

[0099] A "user" is an entity that uses the system to input their symptoms and receive suggestions for over-the-counter medications.

[0100] "Symptom" refers to any physical or mental discomfort experienced by the user, including, for example, "sore throat" or "runny nose."

[0101] "Generative AI" is part of a system that uses machine learning and artificial intelligence techniques to analyze symptom data entered by the user and suggest the most appropriate over-the-counter medication.

[0102] "Medicine" refers to over-the-counter medicines intended for the relief or treatment of symptoms.

[0103] The "server" is a computer system that manages the exchange of data between the user's device and the generating AI, and performs tasks such as preparing input data for analysis and obtaining detailed information.

[0104] A "database" is a collection of information that stores information on the relationship between symptoms and drugs and detailed information on drugs.

[0105] "API" stands for Application Program Interface, a means for different software applications to communicate with each other.

[0106] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data exchange format and a text format that is easy to read and write.

[0107] A "list" refers to an ordered collection of items, and in this system refers to a list containing suitable over-the-counter drug candidates.

[0108] "Detailed information" refers to additional information that users want to know, such as precautions for use, side effects, and when to seek medical attention for a drug.

[0109] An "interface" is the means by which a user interacts with a system, including screens, input forms, etc.

[0110] The present invention is a system that utilizes a generating AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generating AI, an internal database, and an interface.

[0111] Hardware and software used

[0112] Hardware: User devices (smartphones, tablets, PCs)

[0113] Software: web or application interface, server software, generative AI, internal database

[0114] Data processing and calculation

[0115] Data format conversion: JSON format

[0116] API Call: Generate AI API for analytics

[0117] Internal database access: symptom and over-the-counter drug association data and detailed information

[0118] Overview of program processing

[0119] User symptom input

[0120] The user launches the application or web interface using a device such as a smartphone or PC, enters their symptoms into the input form, and clicks the submit button.

[0121] Submitting symptom data and preparing for analysis

[0122] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[0123] The server checks and formats the received symptom data, cleaning and formatting the data as needed.

[0124] Symptom analysis

[0125] The server makes an API call to pass the clean data to the generation AI.

[0126] The generated AI analyzes the symptom data it receives and compares it with an internal database to select the most appropriate over-the-counter medication.

[0127] Over-the-counter medication suggestions

[0128] Based on the results of the symptom analysis, the generative AI creates a list of over-the-counter medications that are best suited for the user, including the name of each drug and its common uses.

[0129] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[0130] Providing additional information

[0131] If the user wants more information about a particular drug from the list of suggested over-the-counter medications, they can click on the drug's more information button.

[0132] The device sends the user's request to the server.

[0133] The server references an internal database or an external medical information API to obtain detailed information about the drug.

[0134] The device displays the obtained details to the user.

[0135] Specific examples

[0136] Scenario: User enters symptoms of "sore throat" and "runny nose"

[0137] The user launches the app, types in "sore throat" or "runny nose," and clicks the send button.

[0138] The terminal converts the input content into JSON format and sends an API request to the server.

[0139] The server receives the data and sends the symptom data via an interface to provide to the generation AI.

[0140] The generative AI analyzes the received data and retrieves over-the-counter medication information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[0141] The generation AI returns the selection results to the server.

[0142] The server generates a response to return the results of the generation AI to the terminal.

[0143] The device will present the user with a list of "lozenges" and "nasal sprays."

[0144] If the user wants to know more information about a "lozenge" from the list, he or she can click on the more information button.

[0145] The terminal sends a detailed information request to the server.

[0146] The server references an internal database or an external API to obtain detailed information about the "lozenge" and returns it to the device.

[0147] The device displays detailed information about the lozenge to the user (such as precautions for use, side effects, and when to seek medical advice).

[0148] Prompt Sentence Examples

[0149] User: Suggest suitable over-the-counter medications for symptoms like "sore throat" and "runny nose."

[0150] This system allows users to self-diagnose and select appropriate over-the-counter medications to help manage their health. It also provides detailed drug information, allowing users to choose medications with confidence.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[0154] Input: User-entered symptom data (e.g., "sore throat" or "runny nose").

[0155] Output: Trigger when the submit button is clicked and symptom data is sent.

[0156] Specific actions: The user checks the symptom information entered in the interface and clicks the submit button.

[0157] Step 2:

[0158] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[0159] Input: User-submitted symptom data.

[0160] Output: Symptom data converted to JSON format.

[0161] Specific operation: The terminal receives input data, runs a formatter to convert it into JSON format, and then generates an API request and sends it to the server.

[0162] Step 3:

[0163] The server checks the received symptom data, performs any necessary cleaning (e.g., removing special characters and standardizing formatting), and prepares the data for analysis.

[0164] Input: Symptom data submitted in JSON format.

[0165] Output: Clean data format.

[0166] What happens: The server validates the format of the data it receives, checks for invalid data, and, if necessary, removes extra spaces and special characters and formats the data.

[0167] Step 4:

[0168] The server makes an API call to pass the prepared data to the generative AI for analysis.

[0169] Input: Clean symptom data.

[0170] Output: API request to the generating AI.

[0171] Specific operation: The server converts the clean data back into API request format and sends it to the generative AI's analysis engine.

[0172] Step 5:

[0173] The generated AI analyzes the symptom data it receives and refers to an internal database to select the most appropriate over-the-counter medication.

[0174] Input: Clean symptom data sent from the server.

[0175] Output: A list of the best over-the-counter medications.

[0176] How it works: The generative AI passes symptom data to an analysis algorithm, searches an internal database for symptom-drug correlation data, and generates a list of the most suitable over-the-counter medications.

[0177] Step 6:

[0178] Based on the results of symptom analysis, the generative AI creates a list of over-the-counter medications that are optimal for the user and returns it to the server.

[0179] Input: Analysis results.

[0180] Output: List of over-the-counter medications.

[0181] Specific operation: The generation AI formats the analysis results into a list and sends the data back to the server.

[0182] Step 7:

[0183] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[0184] Input: List of over-the-counter medications received from the generation AI.

[0185] Output: A formatted list for display to the user.

[0186] Specific operation: The server formats the list received from the generation AI into a user-friendly format, generates an API response, and sends it to the device.

[0187] Step 8:

[0188] The device displays the response received from the server to the user, showing a list of over-the-counter medications such as "lozenges for sore throats" and "nasal spray for runny noses."

[0189] Input: A formatted over-the-counter medication list from the server.

[0190] Output: A list of over-the-counter medications displayed in the user interface.

[0191] What happens: The device renders the received data into a user interface and displays a list of appropriate over-the-counter medications.

[0192] Step 9:

[0193] If the user wishes to know more information about a particular drug from the list, he or she clicks on the drug's detailed information button.

[0194] Input: User click.

[0195] Output: Request for more information.

[0196] What happens: When the user clicks, a request for more information is sent to the server.

[0197] Step 10:

[0198] The terminal sends a detailed information request to the server.

[0199] Input: The user's request for more information.

[0200] Output: API request to the server.

[0201] Specific operation: The device sends the user's request to the server in the corresponding API request format.

[0202] Step 11:

[0203] The server receives the request and retrieves detailed information about the drug by referencing an internal database or an external medical information API.

[0204] Input: More information request.

[0205] Output: Detailed information.

[0206] What happens: The server performs a database lookup to get the required information, and if an external API is required, calls it to get the information.

[0207] Step 12:

[0208] The server generates a response to return the acquired detailed information to the terminal.

[0209] Input: More information.

[0210] Output: The response data.

[0211] Specific operation: The server formats the detailed information and sends the response data to the terminal.

[0212] Step 13:

[0213] The device displays the obtained details to the user.

[0214] Input: Detailed information response from the server.

[0215] Output: Detailed information displayed in the user interface.

[0216] What happens: The device renders the details in its user interface and displays them to the user.

[0217] (Application example 1)

[0218] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0219] Conventional over-the-counter drug recommendation systems only suggest the most appropriate over-the-counter drug based on the symptoms entered by the user. However, there is a need for a system that can suggest meal menus suited to the user's health condition, especially in the field of food delivery. The lack of such a system makes it difficult for users to choose the appropriate meal to improve their health condition.

[0220] 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.

[0221] In this invention, the server includes a means for the generation AI means to suggest appropriate over-the-counter drugs or meal menus based on the analysis results, a means for transmitting the user's symptom information to the server, and a means for the generation AI means to acquire from an internal database the association between the symptoms and over-the-counter drugs or meal menus so that the association can evaluate the suitability of the over-the-counter drugs or meal menus. This allows the user to be suggested the optimal over-the-counter drugs or meal menus based on their health condition, thereby supporting health management.

[0222] "Means for users to input symptoms" refers to a device or application that provides an interface for users to input their own health condition and specific symptoms.

[0223] The "generative AI means" is an artificial intelligence system that analyzes input symptom data and suggests appropriate over-the-counter medications and meal menus.

[0224] The "server" is a central processing unit that receives information sent from a user terminal, passes the information to the generation AI means, and returns the analysis results of the generation AI to the user.

[0225] The "internal database" is a data storage system that stores data relating to symptoms, over-the-counter medications, and meal plans.

[0226] "Means for suggesting over-the-counter medications or meal menus" refers to a device or application that presents users with candidates for over-the-counter medications or meal menus that suit the user's symptoms based on the results of analysis by the generative AI means.

[0227] The "means for displaying detailed information" refers to a device or interface for displaying detailed information about the proposed over-the-counter medicine or meal menu to the user, such as its effects, side effects, nutritional value, etc.

[0228] The "means for transmitting symptom information to a server" is an interface equipped with a communication function for transmitting symptom information input by a user to a terminal to a server.

[0229] The "means for acquiring associations" refers to a processing device or program for searching and acquiring association data between symptoms and over-the-counter drugs or meal menus from an internal database.

[0230] The "means for generating a candidate list" is a program for using the acquired association data to generate a list of over-the-counter medications or meal menus to suggest to the user.

[0231] The "means for linking with external database API" is an interface for communicating with an external database service to obtain detailed information.

[0232] The present invention is a system that utilizes generative AI to suggest optimal over-the-counter medications and meal plans when a user inputs their current symptoms. A specific embodiment of this system is described below.

[0233] System configuration and program overview

[0234] The system includes a user terminal, a server, a generating AI, an internal database, and an interface, and includes the following main processing steps:

[0235] 1. User symptom input

[0236] Device: Users use a device such as a smartphone, tablet, or computer to input symptoms via an application or web interface. Examples might include "sore throat" or "runny nose."

[0237] Server: Receives symptom information sent from the device, checks the data format, and prepares it for analysis.

[0238] 2. Symptom analysis

[0239] Server: Makes an API call to pass the received symptom information to the generation AI.

[0240] Generative AI: Analyzes the received data and retrieves data relating to symptoms and over-the-counter medicines or meal plans from an internal database. For example, if the user's symptoms include "sore throat," it will select over-the-counter medicines and meal plans that are good for the throat.

[0241] 3. Over-the-counter medication or meal suggestions

[0242] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications and meals that are best suited to the user's symptoms. Examples include "lozenges," "lemon tea," and "chicken soup."

[0243] Server: Receives the list returned by the generation AI, formats it for the user, and returns it to the device.

[0244] 4. Providing additional information

[0245] Users: Review the suggested list and if they want more information about a particular item, click on the more information button for that item.

[0246] Terminal: Sends a detailed information request to the server.

[0247] Server: Refers to an internal database or external information API to obtain detailed information about the item, such as precautions for use, side effects, nutritional value, and when to seek medical advice.

[0248] Terminal: Display the obtained details to the user.

[0249] Hardware and software used

[0250] Hardware: smartphones, tablets, PCs (user devices), servers

[0251] Software: Mobile applications, generative AI systems (e.g., ChatGPT), databases (e.g., MySQL), API interfaces (e.g., RESTful APIs)

[0252] Examples of specific examples and prompts

[0253] Examples:

[0254] If a user types "sore throat" into the app, the generative AI will suggest "lozenges," "lemon tea," "chicken soup," etc. If the user selects "lemon tea," detailed information about the tea (such as how to make it, its ingredients, and its effects) will be displayed.

[0255] Example prompt sentence:

[0256] "The current symptom is a sore throat. Please suggest some over-the-counter medicines or meals that will help with the sore throat."

[0257] As described above, the system of the present invention can utilize a generative AI model to suggest over-the-counter medications and meal menus in real time based on the user's health condition, thereby supporting health management.

[0258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0259] Step 1:

[0260] User symptom input

[0261] Users can use a device such as a smartphone, tablet, or PC to input their symptoms via an application or web interface. For example, they can enter specific symptoms such as "sore throat" or "runny nose" into the input form and click the submit button. At this point, the input symptom information is converted into JSON format and sent as an API request.

[0262] Input: The user inputs symptoms. For example, they input "sore throat" and "runny nose."

[0263] Output: Symptom information in JSON format.

[0264] Step 2:

[0265] Data reception by the server

[0266] The server receives symptom information in JSON format sent from the device, stores the received information in a database, checks the data format, and formats it for analysis.

[0267] Input: API request containing symptom information in JSON format.

[0268] Output: Symptom information formatted for analysis.

[0269] Step 3:

[0270] Sending data to the generation AI

[0271] The server makes an API call to send the formatted symptom information to the generation AI. Through the API call, the symptom data is passed to the generation AI system (e.g., ChatGPT).

[0272] Input: Formatted symptom information.

[0273] Output: Symptom information received by the generation AI.

[0274] Step 4:

[0275] Symptom analysis and over-the-counter medication or meal plan suggestions

[0276] The AI ​​analyzes the received symptom data, retrieves data relating to symptoms and over-the-counter medications or meal plans from an internal database, and generates a list of candidates for over-the-counter medications and meal plans that are optimal for the user's symptoms based on the analysis.

[0277] Input: Symptom data.

[0278] Output: A list of over-the-counter medications or meal options.

[0279] Step 5:

[0280] Server formatting and returning the list

[0281] The server receives the list of over-the-counter medications or meal options returned by the generation AI, formats it into a user-friendly format, and then sends the formatted list to the device.

[0282] Input: A list of over-the-counter medications or meal options.

[0283] Output: A user-friendly formatted list.

[0284] Step 6:

[0285] View the list and request more information

[0286] The user checks the list of suggested over-the-counter medications or meal plans on the device, and if they want more information about a particular item, they click the details button for that item, and the device sends a request for more information to the server.

[0287] Input: User clicks the more info button.

[0288] Output: More information request.

[0289] Step 7:

[0290] Retrieving and displaying detailed information from the server

[0291] The server receives the request for more information, retrieves the details about the suggested over-the-counter medications and meal plans by referencing an internal database or an external information API, and then sends the retrieved details back to the device, which then displays them to the user.

[0292] Input: More information request.

[0293] Output: Detailed information (precautions for use, side effects, nutritional value, when to seek medical advice, etc.).

[0294] Through these steps, users will be recommended the most appropriate over-the-counter medications and meal plans based on their health condition, enabling comprehensive health management with detailed information.

[0295] 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.

[0296] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. In a specific embodiment, the system includes the following main components: a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface.

[0297] Program processing

[0298] User symptom input

[0299] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0300] Server: Receives symptom information and emotion data sent from the terminal, checks the data format and prepares it for analysis.

[0301] Symptom analysis

[0302] Server: Makes an API call to pass the received symptom information and emotion data to the generation AI means.

[0303] Generative AI: Analyzes the received symptom data, references the symptom-to-over-the-counter drug association data stored in an internal database, generates an index for selecting appropriate over-the-counter drugs, and adjusts the list of suggested over-the-counter drugs by taking into account emotional data.

[0304] Over-the-counter medication suggestions

[0305] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes suggested over-the-counter medication names and common uses.

[0306] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[0307] Device: Displays a list of over-the-counter medications that are best suited to the user.

[0308] Providing additional information

[0309] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0310] Terminal: Sends the user's request to the server.

[0311] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[0312] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[0313] Specific examples

[0314] Scenario: A user enters the symptoms "sore throat" and "runny nose" and the emotion of being stressed.

[0315] 1. User: Launches the app, enters "sore throat" and "runny nose" into the form, and the emotion engine recognizes that the user is feeling stressed. The user clicks the submit button.

[0316] 2. Terminal: Converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[0317] 3. Server: Receives data and sends symptom and emotion data via an interface to provide to the generation AI.

[0318] 4. Generative AI: Analyzes the received data and retrieves symptom and over-the-counter drug information from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a runny nose. Furthermore, if the user is feeling stressed, it also suggests products with a relaxation effect.

[0319] 5. Generation AI: Returns the selection results to the server.

[0320] 6. Server: Generates a response to return the results of the generated AI to the device.

[0321] 7. Terminal: Show the user a list of "lozenges," "nasal sprays," and "relaxation teas."

[0322] 8. User: If you want to know more information about "lozenge" from the list, click the More Information button.

[0323] 9. Terminal: Sends a detailed information request to the server.

[0324] 10. Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[0325] 11. Terminal: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[0326] In this way, users can self-diagnose and select the appropriate over-the-counter medication, and are provided with detailed information to support their health management, taking into account their emotional state.

[0327] The processing flow will be explained below.

[0328] Step 1:

[0329] User: Launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0330] Step 2:

[0331] Terminal: Converts the input symptom data and emotion data into JSON format and prepares an API request to send to the server. Sends the symptom data and emotion data to the API endpoint.

[0332] Step 3:

[0333] Server: Receives symptom data and emotion data sent from the device and checks the data format. If there are no problems with the format, prepares to transfer it to the generation AI means.

[0334] Step 4:

[0335] Server: Sends symptom and emotion data to the API endpoint of the generative AI method. Establishes a secure connection using the endpoint URL and authentication information.

[0336] Step 5:

[0337] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for "sore throat" and "nasal spray" for "runny nose." It also takes into account emotional data and suggests additional products with a relaxation effect for users who are feeling stressed.

[0338] Step 6:

[0339] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and common uses of the suggested over-the-counter medications. The generated over-the-counter medication list is returned to the server in JSON format.

[0340] Step 7:

[0341] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[0342] Step 8:

[0343] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[0344] Step 9:

[0345] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0346] Step 10:

[0347] Device: Sends a request for user details to the server.

[0348] Step 11:

[0349] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[0350] Step 12:

[0351] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[0352] In this way, users can easily obtain over-the-counter medications that suit their symptoms and feelings, and also check the necessary detailed information.

[0353] Example 2

[0354] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0355] In modern society, many people face a variety of symptoms and health problems on a daily basis. It is also known that various emotions and psychological states affect health, but it is difficult to comprehensively evaluate these and select appropriate over-the-counter medications. It is particularly difficult for general users without specialized knowledge to select appropriate over-the-counter medications taking into account emotional states and symptoms, raising concerns about inaccurate self-diagnosis and overmedication.

[0356] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input symptoms and emotions, a generation AI means for analyzing the symptoms and emotions, means for the generation AI means to suggest appropriate over-the-counter drugs based on the analysis results, means for displaying detailed information about the suggested over-the-counter drugs, means for transmitting the user's symptom and emotion information to the server, means for the server to pass the symptom and emotion information to the generation AI means, means for the generation AI means to acquire associations between symptoms and emotions and over-the-counter drugs from an internal database so that the generation AI means can evaluate the suitability of the over-the-counter drugs, and means for generating a candidate list of over-the-counter drugs using the acquired association information. This allows the user to easily select an appropriate over-the-counter drug taking into account their symptoms and emotions.

[0357] "User" refers to a person who utilizes the system to input symptoms and emotions.

[0358] "Symptom" means physical or mental health information entered by a User.

[0359] "Emotion" refers to the psychological state analyzed from the user's facial expressions and voice.

[0360] "Terminal" refers to a device used by a user to enter input, including a smartphone, tablet, PC, etc.

[0361] "Server" refers to the infrastructure that receives data from users and passes it on to the Generative AI Means.

[0362] "Generative AI" refers to artificial intelligence that analyzes input symptoms and emotions and suggests appropriate over-the-counter medications.

[0363] "Internal database" refers to data storage that stores information on the relationship between symptoms and over-the-counter medications.

[0364] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies.

[0365] "List" refers to a list of over-the-counter medications suggested to the user by the generating AI.

[0366] "Detailed information" refers to information about product specifications, such as precautions for use, side effects, and when to seek medical advice.

[0367] An "emotion engine" refers to technology that recognizes and analyzes emotions from a user's facial expressions and voice.

[0368] "Interface" refers to the operation screen and input form that allow the user to interact with the system.

[0369] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. The system's main components are a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface. The hardware used includes user terminals such as smartphones, tablets, and PCs, as well as a server system. The software applied to this hardware includes an application (or web interface), a generative AI model, an emotion engine, and so on.

[0370] The user launches the application or web interface using a device such as a smartphone, tablet, or PC. The user enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. The emotion engine then analyzes the user's facial expressions and voice to collect emotional data. The device then converts the entered symptom and emotional data into JSON format for transmission to the server.

[0371] The server receives the symptom and emotion data sent from the device and makes an API call to pass it to the generation AI for analysis. The generation AI analyzes the received data and retrieves association data between symptoms and over-the-counter medications from an internal database. Based on the analysis results, it then generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and general uses of the suggested over-the-counter medications.

[0372] The over-the-counter medication list returned from the generation AI to the server is formatted for the user and sent to the device as a response. The device displays the over-the-counter medication list that best suits the user. If the user wants more information about a specific medication, they click the more information button, which sends a request for more information to the server. The server calls an internal database or an external medical information API to obtain detailed information about the requested medication. The obtained information is returned to the device in JSON format, and the device generates a UI to display the detailed information and displays it to the user.

[0373] For example, if a user inputs the symptoms of "sore throat" and "runny nose" and the emotion engine recognizes the user's emotion as "stress," the generative AI will analyze this information and select a "lozenge" for the "sore throat" and a "nasal spray" for the "runny nose," and also suggest "relaxation tea" to relieve stress. If the user wants more information about the "lozenge," they can click the more information button, which will display detailed information such as precautions for use, side effects, and when to see a doctor.

[0374] Examples of prompts to input into a generative AI model include:

[0375] Example prompt sentence:

[0376] If a user enters symptoms such as "sore throat" or "runny nose," the emotion engine recognizes that this is "stressed." Suggest the best over-the-counter medication for this user.

[0377] This concludes the detailed description of the embodiment of the present invention, which allows users to select appropriate over-the-counter medications based on self-diagnosis and manage their health while taking into account their emotional state.

[0378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0379] Step 1: User enters symptoms and feelings

[0380] User: Launches the app or web interface from a device such as a smartphone, tablet, or PC. Enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. At the same time, the emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[0381] Input: Symptom information such as "sore throat" and "runny nose" and emotion data.

[0382] Output: Symptom and emotion data in JSON format.

[0383] Step 2: Send data from the device to the server

[0384] Terminal: Converts the input symptom data and emotion data into JSON format and sends it to the server as an API request.

[0385] Input: User-entered symptom and emotion data.

[0386] Output: The API request in JSON format sent to the server.

[0387] Step 3: Server receives and prepares data

[0388] Server: Receives symptom data and emotion data sent from the device, checks the format of the received data, and prepares an interface to pass it to the generation AI for analysis.

[0389] Input: Symptom and emotion data received from the device in JSON format.

[0390] Output: Symptom and emotion data prepared for analysis.

[0391] Step 4: Generative AI analyzes symptoms and emotions

[0392] Server: Makes API calls to pass symptom data and emotion data to the generation AI.

[0393] Generative AI: Analyzes the received symptom and emotion data, retrieves data relating to symptoms and over-the-counter medications from an internal database, and generates a list of over-the-counter medications that best fit the symptoms and emotions based on the analysis results.

[0394] Input: Symptom data and emotion data sent from the server.

[0395] Output: A list of appropriate over-the-counter medications.

[0396] Step 5: Sending analysis results from the generation AI to the server

[0397] Generative AI: Returns a list of over-the-counter drugs based on the analysis results to the server.

[0398] Server: Receives the list of over-the-counter drugs returned by the generation AI, formats it for the user, and generates a response.

[0399] Input: A list of over-the-counter medications returned by the generation AI.

[0400] Output: A formatted response with a list of over-the-counter medications.

[0401] Step 6: Sending a response from the server to the device

[0402] Server: Sends the prepared response to the device via API.

[0403] Input: A formatted response of a list of over-the-counter medications.

[0404] Output: The response data sent to the device.

[0405] Step 7: Displaying a list of over-the-counter medications on your device

[0406] Terminal: Displays the over-the-counter drug list received from the server to the user.

[0407] Input: The response data sent by the server.

[0408] Output: The over-the-counter medication list displayed to the user.

[0409] Step 8: User requests additional information

[0410] User: If the user wants to know more information about a particular drug from the list of over-the-counter drugs displayed, he or she clicks on the More Information button.

[0411] Input: Select the drug you want more information about.

[0412] Output: Generate a request for more information.

[0413] Step 9: Request for more information from the device to the server

[0414] Terminal: Sends a detailed information request to the server.

[0415] Input: A request for more information about the drug selected by the user.

[0416] Output: The request data sent to the server.

[0417] Step 10: Server Gets More Information

[0418] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the target drug.

[0419] Input: More information request received from the device.

[0420] Output: Detailed information data obtained.

[0421] Step 11: Sending detailed information from the server to the device

[0422] Server: Returns the acquired detailed information to the terminal in JSON format.

[0423] Input: Detailed information data obtained.

[0424] Output: Detailed information returned in JSON format.

[0425] Step 12: Viewing detailed information via terminal

[0426] Terminal: Based on the received detailed information, a UI is generated to display detailed information such as precautions for use, side effects, and when to seek medical attention.

[0427] Input: The details sent by the server.

[0428] Output: Detailed information that is displayed to the user.

[0429] (Application example 2)

[0430] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0431] Systems that simply suggest over-the-counter medications based on the symptoms entered by the user are unable to take the user's emotional state into account when making suggestions, making it difficult to provide optimal over-the-counter medications, health foods, and food delivery options that reflect the user's overall health and psychological state. Furthermore, they are unable to suggest health foods other than over-the-counter medications or provide options for food delivery, which means they are unable to fully meet the user's needs.

[0432] The identification processing by the identification 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 means for the user to input symptoms, a generation AI means for analyzing the symptoms, means for the generation AI means to suggest appropriate over-the-counter medications based on the analysis results, means for displaying detailed information about the suggested over-the-counter medications, means for recognizing and analyzing the user's emotions, and means for suggesting over-the-counter medications, health foods, and food delivery options using the emotion data. This makes it possible to comprehensively analyze the user's symptoms and emotions and provide optimal over-the-counter medications, health foods, and food delivery options.

[0433] A "means for user input of symptoms" is a device or software that provides an interface for a user to input their specific symptoms.

[0434] A "generative AI means for analyzing symptoms" is a device or software that uses artificial intelligence technology to analyze symptoms entered by a user and make appropriate suggestions based on the results of that analysis.

[0435] "Means for suggesting appropriate over-the-counter medications based on the analysis results" refers to a device or software that selects and suggests the over-the-counter medication that is most suitable for the user's symptoms from the analysis results output by the generation AI means.

[0436] "Means for displaying detailed information about suggested over-the-counter drugs" means a device or software for displaying detailed information about the over-the-counter drugs suggested by the generating AI means to the user, such as their usage and side effects.

[0437] "Means for recognizing and analyzing user emotions" refers to a device or software that analyzes emotions from the user's facial expressions, voice, etc., and collects that emotional data.

[0438] The "means for using emotional data to suggest over-the-counter medications, health foods, and food delivery options" refers to a device or software that uses the analyzed emotional data to suggest optimal over-the-counter medications, health foods, and food delivery options, taking into account the user's emotional state.

[0439] The "means for transmitting the user's symptom information to the server" refers to a device or software for transmitting the symptom information entered by the user to the server.

[0440] "Means for the server to pass symptom information and emotional data to the generating AI means" refers to a device or software that enables the server to provide the user's symptom information and emotional data to the generating AI means.

[0441] "Means for obtaining from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods" refers to a device or software that enables the generating AI means to obtain from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods.

[0442] The "means for generating a candidate list of over-the-counter drugs and health foods using the acquired relevance information" refers to a device or software for generating a candidate list of over-the-counter drugs and health foods based on the acquired relevance information.

[0443] This invention relates to a system that allows users to input their symptoms and then suggests optimal over-the-counter medications, health foods, and food delivery options based on the input. The system includes a user terminal, a server, a generative AI, an internal database, an emotion engine, and an interface.

[0444] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.) and inputs specific symptoms (e.g., "sore throat" or "runny nose"). The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0445] The server receives the symptom information and emotion data sent from the device, checks the data format, and prepares it for analysis. The prepared data is then passed to the generation AI, which analyzes the received data and references the association data between symptoms and related products stored in an internal database. The server generates an index for selecting appropriate over-the-counter medications, health foods, and food delivery options, and adjusts the suggested list taking the emotion data into account.

[0446] Based on the analysis results, the Generator AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions. This list includes the names and common uses of the suggested items. The server receives the list returned by the Generator AI, formats it for the user, and sends it to the device.

[0447] The device displays a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user. If the user wants to check more information about a suggested item, they click the item's more information button. The more information request is sent to the server. The server receives the more information request, calls an internal database or an external information API to retrieve more information (such as precautions for use, side effects, nutritional information, and ingredient information), and returns the retrieved details in JSON format to the device. The device generates a UI to display the retrieved details and displays them to the user.

[0448] As a concrete example, consider the case where a user inputs the symptoms of "sore throat" and "runny nose" and the emotion of feeling stressed. The user launches the app and enters "sore throat" and "runny nose" into the form, and the emotion engine detects the user's stress. When the user clicks the submit button, the device converts the input symptom data and emotion data into JSON format and sends an API request to the server. The server receives the data and sends the symptom data and emotion data via an interface to provide it to the generation AI.

[0449] The AI ​​analyzes the received data and retrieves symptoms, over-the-counter medications, health foods, and food delivery options from an internal database. For example, it might select a lozenge for a sore throat and a nasal spray for a runny nose. It might also suggest products with a relaxation effect (e.g., relaxation tea) if the user is feeling stressed.

[0450] The generation AI returns the selection results to the server. The server generates a response to return the generation AI's results to the device, and the device displays a list of "lozenges," "nasal spray," and "relaxation tea" to the user. If the user wants to know more information about "lozenges" from the list, they click the more information button. The device sends a more information request to the server, and the server references an internal database or external API to obtain more information about "lozenges" and returns it to the device. The device displays the detailed information about "lozenges" to the user, providing information such as precautions for use, side effects, and when to see a doctor.

[0451] Example prompt sentence:

[0452] "Symptoms: sore throat, tired. Emotion: stress. Based on this information, use generative AI to suggest the best over-the-counter medications, health foods, and delivery options."

[0453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0454] Step 1:

[0455] The user launches the application or web interface on their device and inputs their symptoms. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0456] Input: User symptom data (e.g., "sore throat" or "runny nose"), facial expressions, and voice data

[0457] Output: Symptom and emotion data in JSON format

[0458] Step 2:

[0459] The device converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[0460] Input: Symptom and emotion data in JSON format

[0461] Output: API request to the server

[0462] Step 3:

[0463] The server sends the received symptom information and emotion data via an interface to provide it to the generation AI, and checks the data format and prepares it for analysis.

[0464] Input: Symptom and emotion data sent from the device in JSON format

[0465] Output: Data to be passed to the generation AI

[0466] Step 4:

[0467] The generation AI analyzes the received data, retrieves data relating to symptoms and related products from an internal database, and generates an index based on the symptom data and emotion data.

[0468] Input: Symptom and emotion data sent from the server

[0469] Output: Index and relevance information

[0470] Specific operation: The generative AI analyzes symptom data using natural language processing and extracts relevant data from an internal database.

[0471] Step 5:

[0472] Based on the analysis results, generative AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions.

[0473] Input: Relevance information and index

[0474] Output: A shortlist of over-the-counter medications, health foods, and food delivery options

[0475] Specific operation: The generative AI refers to related data and lists high-priority products.

[0476] Step 6:

[0477] The server receives the candidate list returned by the generation AI, formats it for the user, and sends it to the device.

[0478] Input: Candidate list sent from the generation AI

[0479] Output: Response data to the terminal

[0480] Specific operation: The server converts the candidate list into a user-friendly format and sends it to the device as an API response.

[0481] Step 7:

[0482] The device will display a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user.

[0483] Input: Response data received from the server

[0484] Output: A list of products that can be viewed by the user

[0485] Specific operation: The device converts the received data into HTML and UI components for the app, and displays them on the screen.

[0486] Step 8:

[0487] If the user wants to check the detailed information of the suggested item, he / she clicks the detailed information button of the item, and the detailed information request is sent from the terminal to the server.

[0488] Input: User clicks item details button

[0489] Output: Request for more information from the server

[0490] Specific operation: The device receives the user's request and sends a detailed information request to the server.

[0491] Step 9:

[0492] The server receives the detailed information request, retrieves the detailed information by calling an internal database or an external information API, and returns it to the terminal.

[0493] Input: Request for more information from the terminal

[0494] Output: JSON data of detailed information

[0495] What happens: The server performs a database query or external API call to get the details and sends them to the device.

[0496] Step 10:

[0497] A UI is generated to display the detailed information acquired by the device and displayed to the user.

[0498] Input: JSON data of detailed information received from the server

[0499] Output: Detailed information displayed to the user

[0500] Specific operation: The device analyzes the received data, generates the components necessary to properly display detailed information on the UI, and displays them on the screen.

[0501] 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.

[0502] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0503] 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.

[0504] [Second embodiment]

[0505] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0506] 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.

[0507] 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).

[0508] 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.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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.

[0516] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0517] The present invention is a system that utilizes a generative AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generative AI, an internal database, and an interface.

[0518] Program processing

[0519] User symptom input

[0520] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[0521] Server: Receives symptom information sent from the terminal, checks the data format and prepares it for analysis.

[0522] Symptom analysis

[0523] Server: Makes an API call to pass the received symptom information to the generation AI.

[0524] Generative AI: Analyzes the received symptom data, references the data relating to symptoms and over-the-counter medications stored in an internal database, and generates an index for selecting appropriate over-the-counter medications.

[0525] Over-the-counter medication suggestions

[0526] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. This list includes suggested over-the-counter medication names and common uses.

[0527] Server: Receives the over-the-counter drug list returned by the generation AI, formats it for the user, and returns it to the device.

[0528] Device: Displays a list of over-the-counter medications that are best suited to the user.

[0529] Providing additional information

[0530] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0531] Terminal: Sends the user's request to the server.

[0532] Server: Refers to an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and when to see a doctor).

[0533] Terminal: Display the obtained details to the user.

[0534] Specific examples

[0535] Scenario: User enters symptoms of "sore throat" and "runny nose"

[0536] User: Launches the app, enters "sore throat" and "runny nose," and clicks the send button.

[0537] Terminal: Converts input content into JSON format and sends an API request to the server.

[0538] Server: Receives data and sends symptom data via an interface to provide to the generation AI.

[0539] Generative AI: Analyzes the received data and retrieves over-the-counter drug information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[0540] Generation AI: Returns the selection results to the server.

[0541] Server: Generates a response to return the results of the generated AI to the device.

[0542] Terminal: Show the user a list of "lozenges" and "nasal sprays."

[0543] User: If you want to know more information about "lozenge" from the list, click the More Information button.

[0544] Terminal: Sends a detailed information request to the server.

[0545] Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[0546] Device: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[0547] In this way, users can self-diagnose and select appropriate over-the-counter medications, and are also provided with detailed information to support their health management.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] User: Using a device (smartphone, tablet, PC, etc.), the user launches the application or web interface. The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button.

[0551] Step 2:

[0552] Terminal: Converts the input symptom data into JSON format and prepares an API request to send to the server. Sends the symptom data to the API endpoint (e.g., https: / / example.com / api / symptoms).

[0553] Step 3:

[0554] Server: Receives symptom data sent from the device and checks the data format. If there are no problems with the format, prepares it for transfer to the generating AI means for analysis.

[0555] Step 4:

[0556] Server: Sends symptom data to the API endpoint of the generative AI solution. Establishes a secure connection using the endpoint URL and authentication information.

[0557] Step 5:

[0558] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for the symptom "sore throat" and "nasal spray" for the symptom "runny nose."

[0559] Step 6:

[0560] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. The generated list of over-the-counter medications is returned to the server in JSON format.

[0561] Step 7:

[0562] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[0563] Step 8:

[0564] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[0565] Step 9:

[0566] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0567] Step 10:

[0568] Device: Sends a request for user details to the server.

[0569] Step 11:

[0570] Server: Receives the detailed information request, calls an internal database or an external medical information API to obtain detailed information about the drug, and returns the obtained details in JSON format to the terminal.

[0571] Step 12:

[0572] Terminal: Generates a UI to display the detailed information received from the server. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[0573] In this way, the system allows users to easily obtain information on over-the-counter medications that suit their symptoms and check the detailed information they need.

[0574] Example 1

[0575] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] In the past, users needed specialized knowledge to select the appropriate over-the-counter medication based on their symptoms, and using the wrong medication could pose health risks. Furthermore, obtaining detailed drug information required searching multiple sources, which was time-consuming and laborious. Therefore, there is a need for a system that allows users to easily select the appropriate over-the-counter medication and obtain detailed information about it.

[0577] 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.

[0578] In this invention, the server includes a means for converting the symptom data entered by the user into JSON format, a means for preparing the received symptom data for checking and analysis, and a means for retrieving detailed information from an internal database or an external information API and displaying it to the user, thereby enabling the user to easily select an appropriate over-the-counter medicine based on their symptoms and quickly obtain detailed information about it.

[0579] A "user" is an entity that uses the system to input their symptoms and receive suggestions for over-the-counter medications.

[0580] "Symptom" refers to any physical or mental discomfort experienced by the user, including, for example, "sore throat" or "runny nose."

[0581] "Generative AI" is part of a system that uses machine learning and artificial intelligence techniques to analyze symptom data entered by the user and suggest the most appropriate over-the-counter medication.

[0582] "Medicine" refers to over-the-counter medicines intended for the relief or treatment of symptoms.

[0583] The "server" is a computer system that manages the exchange of data between the user's device and the generating AI, and performs tasks such as preparing input data for analysis and obtaining detailed information.

[0584] A "database" is a collection of information that stores information on the relationship between symptoms and drugs and detailed information on drugs.

[0585] "API" stands for Application Program Interface, a means for different software applications to communicate with each other.

[0586] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data exchange format and a text format that is easy to read and write.

[0587] A "list" refers to an ordered collection of items, and in this system refers to a list containing suitable over-the-counter drug candidates.

[0588] "Detailed information" refers to additional information that users want to know, such as precautions for use, side effects, and when to seek medical attention for a drug.

[0589] An "interface" is the means by which a user interacts with a system, including screens, input forms, etc.

[0590] The present invention is a system that utilizes a generating AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generating AI, an internal database, and an interface.

[0591] Hardware and software used

[0592] Hardware: User devices (smartphones, tablets, PCs)

[0593] Software: web or application interface, server software, generative AI, internal database

[0594] Data processing and calculation

[0595] Data format conversion: JSON format

[0596] API Call: Generate AI API for analytics

[0597] Internal database access: symptom and over-the-counter drug association data and detailed information

[0598] Overview of program processing

[0599] User symptom input

[0600] The user launches the application or web interface using a device such as a smartphone or PC, enters their symptoms into the input form, and clicks the submit button.

[0601] Submitting symptom data and preparing for analysis

[0602] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[0603] The server checks and formats the received symptom data, cleaning and formatting the data as needed.

[0604] Symptom analysis

[0605] The server makes an API call to pass the clean data to the generation AI.

[0606] The generated AI analyzes the symptom data it receives and compares it with an internal database to select the most appropriate over-the-counter medication.

[0607] Over-the-counter medication suggestions

[0608] Based on the results of the symptom analysis, the generative AI creates a list of over-the-counter medications that are best suited for the user, including the name of each drug and its common uses.

[0609] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[0610] Providing additional information

[0611] If the user wants more information about a particular drug from the list of suggested over-the-counter medications, they can click on the drug's more information button.

[0612] The device sends the user's request to the server.

[0613] The server references an internal database or an external medical information API to obtain detailed information about the drug.

[0614] The device displays the obtained details to the user.

[0615] Specific examples

[0616] Scenario: User enters symptoms of "sore throat" and "runny nose"

[0617] The user launches the app, types in "sore throat" or "runny nose," and clicks the send button.

[0618] The terminal converts the input content into JSON format and sends an API request to the server.

[0619] The server receives the data and sends the symptom data via an interface to provide to the generation AI.

[0620] The generative AI analyzes the received data and retrieves over-the-counter medication information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[0621] The generation AI returns the selection results to the server.

[0622] The server generates a response to return the results of the generation AI to the terminal.

[0623] The device will present the user with a list of "lozenges" and "nasal sprays."

[0624] If the user wants to know more information about a "lozenge" from the list, he or she can click on the more information button.

[0625] The terminal sends a detailed information request to the server.

[0626] The server references an internal database or an external API to obtain detailed information about the "lozenge" and returns it to the device.

[0627] The device displays detailed information about the lozenge to the user (such as precautions for use, side effects, and when to seek medical advice).

[0628] Prompt Sentence Examples

[0629] User: Suggest suitable over-the-counter medications for symptoms like "sore throat" and "runny nose."

[0630] This system allows users to self-diagnose and select appropriate over-the-counter medications to help manage their health. It also provides detailed drug information, allowing users to choose medications with confidence.

[0631] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0632] Step 1:

[0633] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[0634] Input: User-entered symptom data (e.g., "sore throat" or "runny nose").

[0635] Output: Trigger when the submit button is clicked and symptom data is sent.

[0636] Specific actions: The user checks the symptom information entered in the interface and clicks the submit button.

[0637] Step 2:

[0638] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[0639] Input: User-submitted symptom data.

[0640] Output: Symptom data converted to JSON format.

[0641] Specific operation: The terminal receives input data, runs a formatter to convert it into JSON format, and then generates an API request and sends it to the server.

[0642] Step 3:

[0643] The server checks the received symptom data, performs any necessary cleaning (e.g., removing special characters and standardizing formatting), and prepares the data for analysis.

[0644] Input: Symptom data submitted in JSON format.

[0645] Output: Clean data format.

[0646] What happens: The server validates the format of the data it receives, checks for invalid data, and, if necessary, removes extra spaces and special characters and formats the data.

[0647] Step 4:

[0648] The server makes an API call to pass the prepared data to the generative AI for analysis.

[0649] Input: Clean symptom data.

[0650] Output: API request to the generating AI.

[0651] Specific operation: The server converts the clean data back into API request format and sends it to the generative AI's analysis engine.

[0652] Step 5:

[0653] The generated AI analyzes the symptom data it receives and refers to an internal database to select the most appropriate over-the-counter medication.

[0654] Input: Clean symptom data sent from the server.

[0655] Output: A list of the best over-the-counter medications.

[0656] How it works: The generative AI passes symptom data to an analysis algorithm, searches an internal database for symptom-drug correlation data, and generates a list of the most suitable over-the-counter medications.

[0657] Step 6:

[0658] Based on the results of symptom analysis, the generative AI creates a list of over-the-counter medications that are optimal for the user and returns it to the server.

[0659] Input: Analysis results.

[0660] Output: List of over-the-counter medications.

[0661] Specific operation: The generation AI formats the analysis results into a list and sends the data back to the server.

[0662] Step 7:

[0663] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[0664] Input: List of over-the-counter medications received from the generation AI.

[0665] Output: A formatted list for display to the user.

[0666] Specific operation: The server formats the list received from the generation AI into a user-friendly format, generates an API response, and sends it to the device.

[0667] Step 8:

[0668] The device displays the response received from the server to the user, showing a list of over-the-counter medications such as "lozenges for sore throats" and "nasal spray for runny noses."

[0669] Input: A formatted over-the-counter medication list from the server.

[0670] Output: A list of over-the-counter medications displayed in the user interface.

[0671] What happens: The device renders the received data into a user interface and displays a list of appropriate over-the-counter medications.

[0672] Step 9:

[0673] If the user wishes to know more information about a particular drug from the list, he or she clicks on the drug's detailed information button.

[0674] Input: User click.

[0675] Output: Request for more information.

[0676] What happens: When the user clicks, a request for more information is sent to the server.

[0677] Step 10:

[0678] The terminal sends a detailed information request to the server.

[0679] Input: The user's request for more information.

[0680] Output: API request to the server.

[0681] Specific operation: The device sends the user's request to the server in the corresponding API request format.

[0682] Step 11:

[0683] The server receives the request and retrieves detailed information about the drug by referencing an internal database or an external medical information API.

[0684] Input: More information request.

[0685] Output: Detailed information.

[0686] What happens: The server performs a database lookup to get the required information, and if an external API is required, calls it to get the information.

[0687] Step 12:

[0688] The server generates a response to return the acquired detailed information to the terminal.

[0689] Input: More information.

[0690] Output: The response data.

[0691] Specific operation: The server formats the detailed information and sends the response data to the terminal.

[0692] Step 13:

[0693] The device displays the obtained details to the user.

[0694] Input: Detailed information response from the server.

[0695] Output: Detailed information displayed in the user interface.

[0696] What happens: The device renders the details in its user interface and displays them to the user.

[0697] (Application example 1)

[0698] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0699] Conventional over-the-counter drug recommendation systems only suggest the most appropriate over-the-counter drug based on the symptoms entered by the user. However, there is a need for a system that can suggest meal menus suited to the user's health condition, especially in the field of food delivery. The lack of such a system makes it difficult for users to choose the appropriate meal to improve their health condition.

[0700] 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.

[0701] In this invention, the server includes a means for the generation AI means to suggest appropriate over-the-counter drugs or meal menus based on the analysis results, a means for transmitting the user's symptom information to the server, and a means for the generation AI means to acquire from an internal database the association between the symptoms and over-the-counter drugs or meal menus so that the association can evaluate the suitability of the over-the-counter drugs or meal menus. This allows the user to be suggested the optimal over-the-counter drugs or meal menus based on their health condition, thereby supporting health management.

[0702] "Means for users to input symptoms" refers to a device or application that provides an interface for users to input their own health condition and specific symptoms.

[0703] The "generative AI means" is an artificial intelligence system that analyzes input symptom data and suggests appropriate over-the-counter medications and meal menus.

[0704] The "server" is a central processing unit that receives information sent from a user terminal, passes the information to the generation AI means, and returns the analysis results of the generation AI to the user.

[0705] The "internal database" is a data storage system that stores data relating to symptoms, over-the-counter medications, and meal plans.

[0706] "Means for suggesting over-the-counter medications or meal menus" refers to a device or application that presents users with candidates for over-the-counter medications or meal menus that suit the user's symptoms based on the results of analysis by the generative AI means.

[0707] The "means for displaying detailed information" refers to a device or interface for displaying detailed information about the proposed over-the-counter medicine or meal menu to the user, such as its effects, side effects, nutritional value, etc.

[0708] The "means for transmitting symptom information to a server" is an interface equipped with a communication function for transmitting symptom information input by a user to a terminal to a server.

[0709] The "means for acquiring associations" refers to a processing device or program for searching and acquiring association data between symptoms and over-the-counter drugs or meal menus from an internal database.

[0710] The "means for generating a candidate list" is a program for using the acquired association data to generate a list of over-the-counter medications or meal menus to suggest to the user.

[0711] The "means for linking with external database API" is an interface for communicating with an external database service to obtain detailed information.

[0712] The present invention is a system that utilizes generative AI to suggest optimal over-the-counter medications and meal plans when a user inputs their current symptoms. A specific embodiment of this system is described below.

[0713] System configuration and program overview

[0714] The system includes a user terminal, a server, a generating AI, an internal database, and an interface, and includes the following main processing steps:

[0715] 1. User symptom input

[0716] Device: Users use a device such as a smartphone, tablet, or computer to input symptoms via an application or web interface. Examples might include "sore throat" or "runny nose."

[0717] Server: Receives symptom information sent from the device, checks the data format, and prepares it for analysis.

[0718] 2. Symptom analysis

[0719] Server: Makes an API call to pass the received symptom information to the generation AI.

[0720] Generative AI: Analyzes the received data and retrieves data relating to symptoms and over-the-counter medicines or meal plans from an internal database. For example, if the user's symptoms include "sore throat," it will select over-the-counter medicines and meal plans that are good for the throat.

[0721] 3. Over-the-counter medication or meal suggestions

[0722] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications and meals that are best suited to the user's symptoms. Examples include "lozenges," "lemon tea," and "chicken soup."

[0723] Server: Receives the list returned by the generation AI, formats it for the user, and returns it to the device.

[0724] 4. Providing additional information

[0725] Users: Review the suggested list and if they want more information about a particular item, click on the more information button for that item.

[0726] Terminal: Sends a detailed information request to the server.

[0727] Server: Refers to an internal database or external information API to obtain detailed information about the item, such as precautions for use, side effects, nutritional value, and when to seek medical advice.

[0728] Terminal: Display the obtained details to the user.

[0729] Hardware and software used

[0730] Hardware: smartphones, tablets, PCs (user devices), servers

[0731] Software: Mobile applications, generative AI systems (e.g., ChatGPT), databases (e.g., MySQL), API interfaces (e.g., RESTful APIs)

[0732] Examples of specific examples and prompts

[0733] Examples:

[0734] If a user types "sore throat" into the app, the generative AI will suggest "lozenges," "lemon tea," "chicken soup," etc. If the user selects "lemon tea," detailed information about the tea (such as how to make it, its ingredients, and its effects) will be displayed.

[0735] Example prompt sentence:

[0736] "The current symptom is a sore throat. Please suggest some over-the-counter medicines or meals that will help with the sore throat."

[0737] As described above, the system of the present invention can utilize a generative AI model to suggest over-the-counter medications and meal menus in real time based on the user's health condition, thereby supporting health management.

[0738] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0739] Step 1:

[0740] User symptom input

[0741] Users can use a device such as a smartphone, tablet, or PC to input their symptoms via an application or web interface. For example, they can enter specific symptoms such as "sore throat" or "runny nose" into the input form and click the submit button. At this point, the input symptom information is converted into JSON format and sent as an API request.

[0742] Input: The user inputs symptoms. For example, they input "sore throat" and "runny nose."

[0743] Output: Symptom information in JSON format.

[0744] Step 2:

[0745] Data reception by the server

[0746] The server receives symptom information in JSON format sent from the device, stores the received information in a database, checks the data format, and formats it for analysis.

[0747] Input: API request containing symptom information in JSON format.

[0748] Output: Symptom information formatted for analysis.

[0749] Step 3:

[0750] Sending data to the generation AI

[0751] The server makes an API call to send the formatted symptom information to the generation AI. Through the API call, the symptom data is passed to the generation AI system (e.g., ChatGPT).

[0752] Input: Formatted symptom information.

[0753] Output: Symptom information received by the generation AI.

[0754] Step 4:

[0755] Symptom analysis and over-the-counter medication or meal plan suggestions

[0756] The AI ​​analyzes the received symptom data, retrieves data relating to symptoms and over-the-counter medications or meal plans from an internal database, and generates a list of candidates for over-the-counter medications and meal plans that are optimal for the user's symptoms based on the analysis.

[0757] Input: Symptom data.

[0758] Output: A list of over-the-counter medications or meal options.

[0759] Step 5:

[0760] Server formatting and returning the list

[0761] The server receives the list of over-the-counter medications or meal options returned by the generation AI, formats it into a user-friendly format, and then sends the formatted list to the device.

[0762] Input: A list of over-the-counter medications or meal options.

[0763] Output: A user-friendly formatted list.

[0764] Step 6:

[0765] View the list and request more information

[0766] The user checks the list of suggested over-the-counter medications or meal plans on the device, and if they want more information about a particular item, they click the details button for that item, and the device sends a request for more information to the server.

[0767] Input: User clicks the more info button.

[0768] Output: More information request.

[0769] Step 7:

[0770] Retrieving and displaying detailed information from the server

[0771] The server receives the request for more information, retrieves the details about the suggested over-the-counter medications and meal plans by referencing an internal database or an external information API, and then sends the retrieved details back to the device, which then displays them to the user.

[0772] Input: More information request.

[0773] Output: Detailed information (precautions for use, side effects, nutritional value, when to seek medical advice, etc.).

[0774] Through these steps, users will be recommended the most appropriate over-the-counter medications and meal plans based on their health condition, enabling comprehensive health management with detailed information.

[0775] 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.

[0776] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. In a specific embodiment, the system includes the following main components: a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface.

[0777] Program processing

[0778] User symptom input

[0779] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0780] Server: Receives symptom information and emotion data sent from the terminal, checks the data format and prepares it for analysis.

[0781] Symptom analysis

[0782] Server: Makes an API call to pass the received symptom information and emotion data to the generation AI means.

[0783] Generative AI: Analyzes the received symptom data, references the symptom-to-over-the-counter drug association data stored in an internal database, generates an index for selecting appropriate over-the-counter drugs, and adjusts the list of suggested over-the-counter drugs by taking into account emotional data.

[0784] Over-the-counter medication suggestions

[0785] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes suggested over-the-counter medication names and common uses.

[0786] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[0787] Device: Displays a list of over-the-counter medications that are best suited to the user.

[0788] Providing additional information

[0789] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0790] Terminal: Sends the user's request to the server.

[0791] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[0792] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[0793] Specific examples

[0794] Scenario: A user enters the symptoms "sore throat" and "runny nose" and the emotion of being stressed.

[0795] 1. User: Launches the app, enters "sore throat" and "runny nose" into the form, and the emotion engine recognizes that the user is feeling stressed. The user clicks the submit button.

[0796] 2. Terminal: Converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[0797] 3. Server: Receives data and sends symptom and emotion data via an interface to provide to the generation AI.

[0798] 4. Generative AI: Analyzes the received data and retrieves symptom and over-the-counter drug information from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a runny nose. Furthermore, if the user is feeling stressed, it also suggests products with a relaxation effect.

[0799] 5. Generation AI: Returns the selection results to the server.

[0800] 6. Server: Generates a response to return the results of the generated AI to the device.

[0801] 7. Terminal: Show the user a list of "lozenges," "nasal sprays," and "relaxation teas."

[0802] 8. User: If you want to know more information about "lozenge" from the list, click the More Information button.

[0803] 9. Terminal: Sends a detailed information request to the server.

[0804] 10. Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[0805] 11. Terminal: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[0806] In this way, users can self-diagnose and select the appropriate over-the-counter medication, and are provided with detailed information to support their health management, taking into account their emotional state.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] User: Launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0810] Step 2:

[0811] Terminal: Converts the input symptom data and emotion data into JSON format and prepares an API request to send to the server. Sends the symptom data and emotion data to the API endpoint.

[0812] Step 3:

[0813] Server: Receives symptom data and emotion data sent from the device and checks the data format. If there are no problems with the format, prepares to transfer it to the generation AI means.

[0814] Step 4:

[0815] Server: Sends symptom and emotion data to the API endpoint of the generative AI method. Establishes a secure connection using the endpoint URL and authentication information.

[0816] Step 5:

[0817] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for "sore throat" and "nasal spray" for "runny nose." It also takes into account emotional data and suggests additional products with a relaxation effect for users who are feeling stressed.

[0818] Step 6:

[0819] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and common uses of the suggested over-the-counter medications. The generated over-the-counter medication list is returned to the server in JSON format.

[0820] Step 7:

[0821] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[0822] Step 8:

[0823] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[0824] Step 9:

[0825] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[0826] Step 10:

[0827] Device: Sends a request for user details to the server.

[0828] Step 11:

[0829] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[0830] Step 12:

[0831] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[0832] In this way, users can easily obtain over-the-counter medications that suit their symptoms and feelings, and also check the necessary detailed information.

[0833] Example 2

[0834] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0835] In modern society, many people face a variety of symptoms and health problems on a daily basis. It is also known that various emotions and psychological states affect health, but it is difficult to comprehensively evaluate these and select appropriate over-the-counter medications. It is particularly difficult for general users without specialized knowledge to select appropriate over-the-counter medications taking into account emotional states and symptoms, raising concerns about inaccurate self-diagnosis and overmedication.

[0836] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input symptoms and emotions, a generation AI means for analyzing the symptoms and emotions, means for the generation AI means to suggest appropriate over-the-counter drugs based on the analysis results, means for displaying detailed information about the suggested over-the-counter drugs, means for transmitting the user's symptom and emotion information to the server, means for the server to pass the symptom and emotion information to the generation AI means, means for the generation AI means to acquire associations between symptoms and emotions and over-the-counter drugs from an internal database so that the generation AI means can evaluate the suitability of the over-the-counter drugs, and means for generating a candidate list of over-the-counter drugs using the acquired association information. This allows the user to easily select an appropriate over-the-counter drug taking into account their symptoms and emotions.

[0837] "User" refers to a person who utilizes the system to input symptoms and emotions.

[0838] "Symptom" means physical or mental health information entered by a User.

[0839] "Emotion" refers to the psychological state analyzed from the user's facial expressions and voice.

[0840] "Terminal" refers to a device used by a user to enter input, including a smartphone, tablet, PC, etc.

[0841] "Server" refers to the infrastructure that receives data from users and passes it on to the Generative AI Means.

[0842] "Generative AI" refers to artificial intelligence that analyzes input symptoms and emotions and suggests appropriate over-the-counter medications.

[0843] "Internal database" refers to data storage that stores information on the relationship between symptoms and over-the-counter medications.

[0844] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies.

[0845] "List" refers to a list of over-the-counter medications suggested to the user by the generating AI.

[0846] "Detailed information" refers to information about product specifications, such as precautions for use, side effects, and when to seek medical advice.

[0847] An "emotion engine" refers to technology that recognizes and analyzes emotions from a user's facial expressions and voice.

[0848] "Interface" refers to the operation screen and input form that allow the user to interact with the system.

[0849] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. The system's main components are a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface. The hardware used includes user terminals such as smartphones, tablets, and PCs, as well as a server system. The software applied to this hardware includes an application (or web interface), a generative AI model, an emotion engine, and so on.

[0850] The user launches the application or web interface using a device such as a smartphone, tablet, or PC. The user enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. The emotion engine then analyzes the user's facial expressions and voice to collect emotional data. The device then converts the entered symptom and emotional data into JSON format for transmission to the server.

[0851] The server receives the symptom and emotion data sent from the device and makes an API call to pass it to the generation AI for analysis. The generation AI analyzes the received data and retrieves association data between symptoms and over-the-counter medications from an internal database. Based on the analysis results, it then generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and general uses of the suggested over-the-counter medications.

[0852] The over-the-counter medication list returned from the generation AI to the server is formatted for the user and sent to the device as a response. The device displays the over-the-counter medication list that best suits the user. If the user wants more information about a specific medication, they click the more information button, which sends a request for more information to the server. The server calls an internal database or an external medical information API to obtain detailed information about the requested medication. The obtained information is returned to the device in JSON format, and the device generates a UI to display the detailed information and displays it to the user.

[0853] For example, if a user inputs the symptoms of "sore throat" and "runny nose" and the emotion engine recognizes the user's emotion as "stress," the generative AI will analyze this information and select a "lozenge" for the "sore throat" and a "nasal spray" for the "runny nose," and also suggest "relaxation tea" to relieve stress. If the user wants more information about the "lozenge," they can click the more information button, which will display detailed information such as precautions for use, side effects, and when to see a doctor.

[0854] Examples of prompts to input into a generative AI model include:

[0855] Example prompt sentence:

[0856] If a user enters symptoms such as "sore throat" or "runny nose," the emotion engine recognizes that this is "stressed." Suggest the best over-the-counter medication for this user.

[0857] This concludes the detailed description of the embodiment of the present invention, which allows users to select appropriate over-the-counter medications based on self-diagnosis and manage their health while taking into account their emotional state.

[0858] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0859] Step 1: User enters symptoms and feelings

[0860] User: Launches the app or web interface from a device such as a smartphone, tablet, or PC. Enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. At the same time, the emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[0861] Input: Symptom information such as "sore throat" and "runny nose" and emotion data.

[0862] Output: Symptom and emotion data in JSON format.

[0863] Step 2: Send data from the device to the server

[0864] Terminal: Converts the input symptom data and emotion data into JSON format and sends it to the server as an API request.

[0865] Input: User-entered symptom and emotion data.

[0866] Output: The API request in JSON format sent to the server.

[0867] Step 3: Server receives and prepares data

[0868] Server: Receives symptom data and emotion data sent from the device, checks the format of the received data, and prepares an interface to pass it to the generation AI for analysis.

[0869] Input: Symptom and emotion data received from the device in JSON format.

[0870] Output: Symptom and emotion data prepared for analysis.

[0871] Step 4: Generative AI analyzes symptoms and emotions

[0872] Server: Makes API calls to pass symptom data and emotion data to the generation AI.

[0873] Generative AI: Analyzes the received symptom and emotion data, retrieves data relating to symptoms and over-the-counter medications from an internal database, and generates a list of over-the-counter medications that best fit the symptoms and emotions based on the analysis results.

[0874] Input: Symptom data and emotion data sent from the server.

[0875] Output: A list of appropriate over-the-counter medications.

[0876] Step 5: Sending analysis results from the generation AI to the server

[0877] Generative AI: Returns a list of over-the-counter drugs based on the analysis results to the server.

[0878] Server: Receives the list of over-the-counter drugs returned by the generation AI, formats it for the user, and generates a response.

[0879] Input: A list of over-the-counter medications returned by the generation AI.

[0880] Output: A formatted response with a list of over-the-counter medications.

[0881] Step 6: Sending a response from the server to the device

[0882] Server: Sends the prepared response to the device via API.

[0883] Input: A formatted response of a list of over-the-counter medications.

[0884] Output: The response data sent to the device.

[0885] Step 7: Displaying a list of over-the-counter medications on your device

[0886] Terminal: Displays the over-the-counter drug list received from the server to the user.

[0887] Input: The response data sent by the server.

[0888] Output: The over-the-counter medication list displayed to the user.

[0889] Step 8: User requests additional information

[0890] User: If the user wants to know more information about a particular drug from the list of over-the-counter drugs displayed, he or she clicks on the More Information button.

[0891] Input: Select the drug you want more information about.

[0892] Output: Generate a request for more information.

[0893] Step 9: Request for more information from the device to the server

[0894] Terminal: Sends a detailed information request to the server.

[0895] Input: A request for more information about the drug selected by the user.

[0896] Output: The request data sent to the server.

[0897] Step 10: Server Gets More Information

[0898] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the target drug.

[0899] Input: More information request received from the device.

[0900] Output: Detailed information data obtained.

[0901] Step 11: Sending detailed information from the server to the device

[0902] Server: Returns the acquired detailed information to the terminal in JSON format.

[0903] Input: Detailed information data obtained.

[0904] Output: Detailed information returned in JSON format.

[0905] Step 12: Viewing detailed information via terminal

[0906] Terminal: Based on the received detailed information, a UI is generated to display detailed information such as precautions for use, side effects, and when to seek medical attention.

[0907] Input: The details sent by the server.

[0908] Output: Detailed information that is displayed to the user.

[0909] (Application example 2)

[0910] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0911] Systems that simply suggest over-the-counter medications based on the symptoms entered by the user are unable to take the user's emotional state into account when making suggestions, making it difficult to provide optimal over-the-counter medications, health foods, and food delivery options that reflect the user's overall health and psychological state. Furthermore, they are unable to suggest health foods other than over-the-counter medications or provide options for food delivery, which means they are unable to fully meet the user's needs.

[0912] The identification processing by the identification 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 means for the user to input symptoms, a generation AI means for analyzing the symptoms, means for the generation AI means to suggest appropriate over-the-counter medications based on the analysis results, means for displaying detailed information about the suggested over-the-counter medications, means for recognizing and analyzing the user's emotions, and means for suggesting over-the-counter medications, health foods, and food delivery options using the emotion data. This makes it possible to comprehensively analyze the user's symptoms and emotions and provide optimal over-the-counter medications, health foods, and food delivery options.

[0913] A "means for user input of symptoms" is a device or software that provides an interface for a user to input their specific symptoms.

[0914] A "generative AI means for analyzing symptoms" is a device or software that uses artificial intelligence technology to analyze symptoms entered by a user and make appropriate suggestions based on the results of that analysis.

[0915] "Means for suggesting appropriate over-the-counter medications based on the analysis results" refers to a device or software that selects and suggests the over-the-counter medication that is most suitable for the user's symptoms from the analysis results output by the generation AI means.

[0916] "Means for displaying detailed information about suggested over-the-counter drugs" means a device or software for displaying detailed information about the over-the-counter drugs suggested by the generating AI means to the user, such as their usage and side effects.

[0917] "Means for recognizing and analyzing user emotions" refers to a device or software that analyzes emotions from the user's facial expressions, voice, etc., and collects that emotional data.

[0918] The "means for using emotional data to suggest over-the-counter medications, health foods, and food delivery options" refers to a device or software that uses the analyzed emotional data to suggest optimal over-the-counter medications, health foods, and food delivery options, taking into account the user's emotional state.

[0919] The "means for transmitting the user's symptom information to the server" refers to a device or software for transmitting the symptom information entered by the user to the server.

[0920] "Means for the server to pass symptom information and emotional data to the generating AI means" refers to a device or software that enables the server to provide the user's symptom information and emotional data to the generating AI means.

[0921] "Means for obtaining from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods" refers to a device or software that enables the generating AI means to obtain from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods.

[0922] The "means for generating a candidate list of over-the-counter drugs and health foods using the acquired relevance information" refers to a device or software for generating a candidate list of over-the-counter drugs and health foods based on the acquired relevance information.

[0923] This invention relates to a system that allows users to input their symptoms and then suggests optimal over-the-counter medications, health foods, and food delivery options based on the input. The system includes a user terminal, a server, a generative AI, an internal database, an emotion engine, and an interface.

[0924] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.) and inputs specific symptoms (e.g., "sore throat" or "runny nose"). The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0925] The server receives the symptom information and emotion data sent from the device, checks the data format, and prepares it for analysis. The prepared data is then passed to the generation AI, which analyzes the received data and references the association data between symptoms and related products stored in an internal database. The server generates an index for selecting appropriate over-the-counter medications, health foods, and food delivery options, and adjusts the suggested list taking the emotion data into account.

[0926] Based on the analysis results, the Generator AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions. This list includes the names and common uses of the suggested items. The server receives the list returned by the Generator AI, formats it for the user, and sends it to the device.

[0927] The device displays a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user. If the user wants to check more information about a suggested item, they click the item's more information button. The more information request is sent to the server. The server receives the more information request, calls an internal database or an external information API to retrieve more information (such as precautions for use, side effects, nutritional information, and ingredient information), and returns the retrieved details in JSON format to the device. The device generates a UI to display the retrieved details and displays them to the user.

[0928] As a concrete example, consider the case where a user inputs the symptoms of "sore throat" and "runny nose" and the emotion of feeling stressed. The user launches the app and enters "sore throat" and "runny nose" into the form, and the emotion engine detects the user's stress. When the user clicks the submit button, the device converts the input symptom data and emotion data into JSON format and sends an API request to the server. The server receives the data and sends the symptom data and emotion data via an interface to provide it to the generation AI.

[0929] The AI ​​analyzes the received data and retrieves symptoms, over-the-counter medications, health foods, and food delivery options from an internal database. For example, it might select a lozenge for a sore throat and a nasal spray for a runny nose. It might also suggest products with a relaxation effect (e.g., relaxation tea) if the user is feeling stressed.

[0930] The generation AI returns the selection results to the server. The server generates a response to return the generation AI's results to the device, and the device displays a list of "lozenges," "nasal spray," and "relaxation tea" to the user. If the user wants to know more information about "lozenges" from the list, they click the more information button. The device sends a more information request to the server, and the server references an internal database or external API to obtain more information about "lozenges" and returns it to the device. The device displays the detailed information about "lozenges" to the user, providing information such as precautions for use, side effects, and when to see a doctor.

[0931] Example prompt sentence:

[0932] "Symptoms: sore throat, tired. Emotion: stress. Based on this information, use generative AI to suggest the best over-the-counter medications, health foods, and delivery options."

[0933] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0934] Step 1:

[0935] The user launches the application or web interface on their device and inputs their symptoms. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[0936] Input: User symptom data (e.g., "sore throat" or "runny nose"), facial expressions, and voice data

[0937] Output: Symptom and emotion data in JSON format

[0938] Step 2:

[0939] The device converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[0940] Input: Symptom and emotion data in JSON format

[0941] Output: API request to the server

[0942] Step 3:

[0943] The server sends the received symptom information and emotion data via an interface to provide it to the generation AI, and checks the data format and prepares it for analysis.

[0944] Input: Symptom and emotion data sent from the device in JSON format

[0945] Output: Data to be passed to the generation AI

[0946] Step 4:

[0947] The generation AI analyzes the received data, retrieves data relating to symptoms and related products from an internal database, and generates an index based on the symptom data and emotion data.

[0948] Input: Symptom and emotion data sent from the server

[0949] Output: Index and relevance information

[0950] Specific operation: The generative AI analyzes symptom data using natural language processing and extracts relevant data from an internal database.

[0951] Step 5:

[0952] Based on the analysis results, generative AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions.

[0953] Input: Relevance information and index

[0954] Output: A shortlist of over-the-counter medications, health foods, and food delivery options

[0955] Specific operation: The generative AI refers to related data and lists high-priority products.

[0956] Step 6:

[0957] The server receives the candidate list returned by the generation AI, formats it for the user, and sends it to the device.

[0958] Input: Candidate list sent from the generation AI

[0959] Output: Response data to the terminal

[0960] Specific operation: The server converts the candidate list into a user-friendly format and sends it to the device as an API response.

[0961] Step 7:

[0962] The device will display a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user.

[0963] Input: Response data received from the server

[0964] Output: A list of products that can be viewed by the user

[0965] Specific operation: The device converts the received data into HTML and UI components for the app, and displays them on the screen.

[0966] Step 8:

[0967] If the user wants to check the detailed information of the suggested item, he / she clicks the detailed information button of the item, and the detailed information request is sent from the terminal to the server.

[0968] Input: User clicks item details button

[0969] Output: Request for more information from the server

[0970] Specific operation: The device receives the user's request and sends a detailed information request to the server.

[0971] Step 9:

[0972] The server receives the detailed information request, retrieves the detailed information by calling an internal database or an external information API, and returns it to the terminal.

[0973] Input: Request for more information from the terminal

[0974] Output: JSON data of detailed information

[0975] What happens: The server performs a database query or external API call to get the details and sends them to the device.

[0976] Step 10:

[0977] A UI is generated to display the detailed information acquired by the device and displayed to the user.

[0978] Input: JSON data of detailed information received from the server

[0979] Output: Detailed information displayed to the user

[0980] Specific operation: The device analyzes the received data, generates the components necessary to properly display detailed information on the UI, and displays them on the screen.

[0981] 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.

[0982] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0983] 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.

[0984] [Third embodiment]

[0985] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0986] 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.

[0987] 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).

[0988] 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.

[0989] 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.

[0990] 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).

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0997] The present invention is a system that utilizes a generative AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generative AI, an internal database, and an interface.

[0998] Program processing

[0999] User symptom input

[1000] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[1001] Server: Receives symptom information sent from the terminal, checks the data format and prepares it for analysis.

[1002] Symptom analysis

[1003] Server: Makes an API call to pass the received symptom information to the generation AI.

[1004] Generative AI: Analyzes the received symptom data, references the data relating to symptoms and over-the-counter medications stored in an internal database, and generates an index for selecting appropriate over-the-counter medications.

[1005] Over-the-counter medication suggestions

[1006] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. This list includes suggested over-the-counter medication names and common uses.

[1007] Server: Receives the over-the-counter drug list returned by the generation AI, formats it for the user, and returns it to the device.

[1008] Device: Displays a list of over-the-counter medications that are best suited to the user.

[1009] Providing additional information

[1010] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1011] Terminal: Sends the user's request to the server.

[1012] Server: Refers to an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and when to see a doctor).

[1013] Terminal: Display the obtained details to the user.

[1014] Specific examples

[1015] Scenario: User enters symptoms of "sore throat" and "runny nose"

[1016] User: Launches the app, enters "sore throat" and "runny nose," and clicks the send button.

[1017] Terminal: Converts input content into JSON format and sends an API request to the server.

[1018] Server: Receives data and sends symptom data via an interface to provide to the generation AI.

[1019] Generative AI: Analyzes the received data and retrieves over-the-counter drug information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[1020] Generation AI: Returns the selection results to the server.

[1021] Server: Generates a response to return the results of the generated AI to the device.

[1022] Terminal: Show the user a list of "lozenges" and "nasal sprays."

[1023] User: If you want to know more information about "lozenge" from the list, click the More Information button.

[1024] Terminal: Sends a detailed information request to the server.

[1025] Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[1026] Device: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[1027] In this way, users can self-diagnose and select appropriate over-the-counter medications, and are also provided with detailed information to support their health management.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] User: Using a device (smartphone, tablet, PC, etc.), the user launches the application or web interface. The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button.

[1031] Step 2:

[1032] Terminal: Converts the input symptom data into JSON format and prepares an API request to send to the server. Sends the symptom data to the API endpoint (e.g., https: / / example.com / api / symptoms).

[1033] Step 3:

[1034] Server: Receives symptom data sent from the device and checks the data format. If there are no problems with the format, prepares it for transfer to the generating AI means for analysis.

[1035] Step 4:

[1036] Server: Sends symptom data to the API endpoint of the generative AI solution. Establishes a secure connection using the endpoint URL and authentication information.

[1037] Step 5:

[1038] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for the symptom "sore throat" and "nasal spray" for the symptom "runny nose."

[1039] Step 6:

[1040] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. The generated list of over-the-counter medications is returned to the server in JSON format.

[1041] Step 7:

[1042] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[1043] Step 8:

[1044] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[1045] Step 9:

[1046] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1047] Step 10:

[1048] Device: Sends a request for user details to the server.

[1049] Step 11:

[1050] Server: Receives the detailed information request, calls an internal database or an external medical information API to obtain detailed information about the drug, and returns the obtained details in JSON format to the terminal.

[1051] Step 12:

[1052] Terminal: Generates a UI to display the detailed information received from the server. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[1053] In this way, the system allows users to easily obtain information on over-the-counter medications that suit their symptoms and check the detailed information they need.

[1054] Example 1

[1055] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1056] In the past, users needed specialized knowledge to select the appropriate over-the-counter medication based on their symptoms, and using the wrong medication could pose health risks. Furthermore, obtaining detailed drug information required searching multiple sources, which was time-consuming and laborious. Therefore, there is a need for a system that allows users to easily select the appropriate over-the-counter medication and obtain detailed information about it.

[1057] 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.

[1058] In this invention, the server includes a means for converting the symptom data entered by the user into JSON format, a means for preparing the received symptom data for checking and analysis, and a means for retrieving detailed information from an internal database or an external information API and displaying it to the user, thereby enabling the user to easily select an appropriate over-the-counter medicine based on their symptoms and quickly obtain detailed information about it.

[1059] A "user" is an entity that uses the system to input their symptoms and receive suggestions for over-the-counter medications.

[1060] "Symptom" refers to any physical or mental discomfort experienced by the user, including, for example, "sore throat" or "runny nose."

[1061] "Generative AI" is part of a system that uses machine learning and artificial intelligence techniques to analyze symptom data entered by the user and suggest the most appropriate over-the-counter medication.

[1062] "Medicine" refers to over-the-counter medicines intended for the relief or treatment of symptoms.

[1063] The "server" is a computer system that manages the exchange of data between the user's device and the generating AI, and performs tasks such as preparing input data for analysis and obtaining detailed information.

[1064] A "database" is a collection of information that stores information on the relationship between symptoms and drugs and detailed information on drugs.

[1065] "API" stands for Application Program Interface, a means for different software applications to communicate with each other.

[1066] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data exchange format and a text format that is easy to read and write.

[1067] A "list" refers to an ordered collection of items, and in this system refers to a list containing suitable over-the-counter drug candidates.

[1068] "Detailed information" refers to additional information that users want to know, such as precautions for use, side effects, and when to seek medical attention for a drug.

[1069] An "interface" is the means by which a user interacts with a system, including screens, input forms, etc.

[1070] The present invention is a system that utilizes a generating AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generating AI, an internal database, and an interface.

[1071] Hardware and software used

[1072] Hardware: User devices (smartphones, tablets, PCs)

[1073] Software: web or application interface, server software, generative AI, internal database

[1074] Data processing and calculation

[1075] Data format conversion: JSON format

[1076] API Call: Generate AI API for analytics

[1077] Internal database access: symptom and over-the-counter drug association data and detailed information

[1078] Overview of program processing

[1079] User symptom input

[1080] The user launches the application or web interface using a device such as a smartphone or PC, enters their symptoms into the input form, and clicks the submit button.

[1081] Submitting symptom data and preparing for analysis

[1082] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[1083] The server checks and formats the received symptom data, cleaning and formatting the data as needed.

[1084] Symptom analysis

[1085] The server makes an API call to pass the clean data to the generation AI.

[1086] The generated AI analyzes the symptom data it receives and compares it with an internal database to select the most appropriate over-the-counter medication.

[1087] Over-the-counter medication suggestions

[1088] Based on the results of the symptom analysis, the generative AI creates a list of over-the-counter medications that are best suited for the user, including the name of each drug and its common uses.

[1089] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[1090] Providing additional information

[1091] If the user wants more information about a particular drug from the list of suggested over-the-counter medications, they can click on the drug's more information button.

[1092] The device sends the user's request to the server.

[1093] The server references an internal database or an external medical information API to obtain detailed information about the drug.

[1094] The device displays the obtained details to the user.

[1095] Specific examples

[1096] Scenario: User enters symptoms of "sore throat" and "runny nose"

[1097] The user launches the app, types in "sore throat" or "runny nose," and clicks the send button.

[1098] The terminal converts the input content into JSON format and sends an API request to the server.

[1099] The server receives the data and sends the symptom data via an interface to provide to the generation AI.

[1100] The generative AI analyzes the received data and retrieves over-the-counter medication information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[1101] The generation AI returns the selection results to the server.

[1102] The server generates a response to return the results of the generation AI to the terminal.

[1103] The device will present the user with a list of "lozenges" and "nasal sprays."

[1104] If the user wants to know more information about a "lozenge" from the list, he or she can click on the more information button.

[1105] The terminal sends a detailed information request to the server.

[1106] The server references an internal database or an external API to obtain detailed information about the "lozenge" and returns it to the device.

[1107] The device displays detailed information about the lozenge to the user (such as precautions for use, side effects, and when to seek medical advice).

[1108] Prompt Sentence Examples

[1109] User: Suggest suitable over-the-counter medications for symptoms like "sore throat" and "runny nose."

[1110] This system allows users to self-diagnose and select appropriate over-the-counter medications to help manage their health. It also provides detailed drug information, allowing users to choose medications with confidence.

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] Step 1:

[1113] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[1114] Input: User-entered symptom data (e.g., "sore throat" or "runny nose").

[1115] Output: Trigger when the submit button is clicked and symptom data is sent.

[1116] Specific actions: The user checks the symptom information entered in the interface and clicks the submit button.

[1117] Step 2:

[1118] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[1119] Input: User-submitted symptom data.

[1120] Output: Symptom data converted to JSON format.

[1121] Specific operation: The terminal receives input data, runs a formatter to convert it into JSON format, and then generates an API request and sends it to the server.

[1122] Step 3:

[1123] The server checks the received symptom data, performs any necessary cleaning (e.g., removing special characters and standardizing formatting), and prepares the data for analysis.

[1124] Input: Symptom data submitted in JSON format.

[1125] Output: Clean data format.

[1126] What happens: The server validates the format of the data it receives, checks for invalid data, and, if necessary, removes extra spaces and special characters and formats the data.

[1127] Step 4:

[1128] The server makes an API call to pass the prepared data to the generative AI for analysis.

[1129] Input: Clean symptom data.

[1130] Output: API request to the generating AI.

[1131] Specific operation: The server converts the clean data back into API request format and sends it to the generative AI's analysis engine.

[1132] Step 5:

[1133] The generated AI analyzes the symptom data it receives and refers to an internal database to select the most appropriate over-the-counter medication.

[1134] Input: Clean symptom data sent from the server.

[1135] Output: A list of the best over-the-counter medications.

[1136] How it works: The generative AI passes symptom data to an analysis algorithm, searches an internal database for symptom-drug correlation data, and generates a list of the most suitable over-the-counter medications.

[1137] Step 6:

[1138] Based on the results of symptom analysis, the generative AI creates a list of over-the-counter medications that are optimal for the user and returns it to the server.

[1139] Input: Analysis results.

[1140] Output: List of over-the-counter medications.

[1141] Specific operation: The generation AI formats the analysis results into a list and sends the data back to the server.

[1142] Step 7:

[1143] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[1144] Input: List of over-the-counter medications received from the generation AI.

[1145] Output: A formatted list for display to the user.

[1146] Specific operation: The server formats the list received from the generation AI into a user-friendly format, generates an API response, and sends it to the device.

[1147] Step 8:

[1148] The device displays the response received from the server to the user, showing a list of over-the-counter medications such as "lozenges for sore throats" and "nasal spray for runny noses."

[1149] Input: A formatted over-the-counter medication list from the server.

[1150] Output: A list of over-the-counter medications displayed in the user interface.

[1151] What happens: The device renders the received data into a user interface and displays a list of appropriate over-the-counter medications.

[1152] Step 9:

[1153] If the user wishes to know more information about a particular drug from the list, he or she clicks on the drug's detailed information button.

[1154] Input: User click.

[1155] Output: Request for more information.

[1156] What happens: When the user clicks, a request for more information is sent to the server.

[1157] Step 10:

[1158] The terminal sends a detailed information request to the server.

[1159] Input: The user's request for more information.

[1160] Output: API request to the server.

[1161] Specific operation: The device sends the user's request to the server in the corresponding API request format.

[1162] Step 11:

[1163] The server receives the request and retrieves detailed information about the drug by referencing an internal database or an external medical information API.

[1164] Input: More information request.

[1165] Output: Detailed information.

[1166] What happens: The server performs a database lookup to get the required information, and if an external API is required, calls it to get the information.

[1167] Step 12:

[1168] The server generates a response to return the acquired detailed information to the terminal.

[1169] Input: More information.

[1170] Output: The response data.

[1171] Specific operation: The server formats the detailed information and sends the response data to the terminal.

[1172] Step 13:

[1173] The device displays the obtained details to the user.

[1174] Input: Detailed information response from the server.

[1175] Output: Detailed information displayed in the user interface.

[1176] What happens: The device renders the details in its user interface and displays them to the user.

[1177] (Application example 1)

[1178] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1179] Conventional over-the-counter drug recommendation systems only suggest the most appropriate over-the-counter drug based on the symptoms entered by the user. However, there is a need for a system that can suggest meal menus suited to the user's health condition, especially in the field of food delivery. The lack of such a system makes it difficult for users to choose the appropriate meal to improve their health condition.

[1180] 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.

[1181] In this invention, the server includes a means for the generation AI means to suggest appropriate over-the-counter drugs or meal menus based on the analysis results, a means for transmitting the user's symptom information to the server, and a means for the generation AI means to acquire from an internal database the association between the symptoms and over-the-counter drugs or meal menus so that the association can evaluate the suitability of the over-the-counter drugs or meal menus. This allows the user to be suggested the optimal over-the-counter drugs or meal menus based on their health condition, thereby supporting health management.

[1182] "Means for users to input symptoms" refers to a device or application that provides an interface for users to input their own health condition and specific symptoms.

[1183] The "generative AI means" is an artificial intelligence system that analyzes input symptom data and suggests appropriate over-the-counter medications and meal menus.

[1184] The "server" is a central processing unit that receives information sent from a user terminal, passes the information to the generation AI means, and returns the analysis results of the generation AI to the user.

[1185] The "internal database" is a data storage system that stores data relating to symptoms, over-the-counter medications, and meal plans.

[1186] "Means for suggesting over-the-counter medications or meal menus" refers to a device or application that presents users with candidates for over-the-counter medications or meal menus that suit the user's symptoms based on the results of analysis by the generative AI means.

[1187] The "means for displaying detailed information" refers to a device or interface for displaying detailed information about the proposed over-the-counter medicine or meal menu to the user, such as its effects, side effects, nutritional value, etc.

[1188] The "means for transmitting symptom information to a server" is an interface equipped with a communication function for transmitting symptom information input by a user to a terminal to a server.

[1189] The "means for acquiring associations" refers to a processing device or program for searching and acquiring association data between symptoms and over-the-counter drugs or meal menus from an internal database.

[1190] The "means for generating a candidate list" is a program for using the acquired association data to generate a list of over-the-counter medications or meal menus to suggest to the user.

[1191] The "means for linking with external database API" is an interface for communicating with an external database service to obtain detailed information.

[1192] The present invention is a system that utilizes generative AI to suggest optimal over-the-counter medications and meal plans when a user inputs their current symptoms. A specific embodiment of this system is described below.

[1193] System configuration and program overview

[1194] The system includes a user terminal, a server, a generating AI, an internal database, and an interface, and includes the following main processing steps:

[1195] 1. User symptom input

[1196] Device: Users use a device such as a smartphone, tablet, or computer to input symptoms via an application or web interface. Examples might include "sore throat" or "runny nose."

[1197] Server: Receives symptom information sent from the device, checks the data format, and prepares it for analysis.

[1198] 2. Symptom analysis

[1199] Server: Makes an API call to pass the received symptom information to the generation AI.

[1200] Generative AI: Analyzes the received data and retrieves data relating to symptoms and over-the-counter medicines or meal plans from an internal database. For example, if the user's symptoms include "sore throat," it will select over-the-counter medicines and meal plans that are good for the throat.

[1201] 3. Over-the-counter medication or meal suggestions

[1202] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications and meals that are best suited to the user's symptoms. Examples include "lozenges," "lemon tea," and "chicken soup."

[1203] Server: Receives the list returned by the generation AI, formats it for the user, and returns it to the device.

[1204] 4. Providing additional information

[1205] Users: Review the suggested list and if they want more information about a particular item, click on the more information button for that item.

[1206] Terminal: Sends a detailed information request to the server.

[1207] Server: Refers to an internal database or external information API to obtain detailed information about the item, such as precautions for use, side effects, nutritional value, and when to seek medical advice.

[1208] Terminal: Display the obtained details to the user.

[1209] Hardware and software used

[1210] Hardware: smartphones, tablets, PCs (user devices), servers

[1211] Software: Mobile applications, generative AI systems (e.g., ChatGPT), databases (e.g., MySQL), API interfaces (e.g., RESTful APIs)

[1212] Examples of specific examples and prompts

[1213] Examples:

[1214] If a user types "sore throat" into the app, the generative AI will suggest "lozenges," "lemon tea," "chicken soup," etc. If the user selects "lemon tea," detailed information about the tea (such as how to make it, its ingredients, and its effects) will be displayed.

[1215] Example prompt sentence:

[1216] "The current symptom is a sore throat. Please suggest some over-the-counter medicines or meals that will help with the sore throat."

[1217] As described above, the system of the present invention can utilize a generative AI model to suggest over-the-counter medications and meal menus in real time based on the user's health condition, thereby supporting health management.

[1218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1219] Step 1:

[1220] User symptom input

[1221] Users can use a device such as a smartphone, tablet, or PC to input their symptoms via an application or web interface. For example, they can enter specific symptoms such as "sore throat" or "runny nose" into the input form and click the submit button. At this point, the input symptom information is converted into JSON format and sent as an API request.

[1222] Input: The user inputs symptoms. For example, they input "sore throat" and "runny nose."

[1223] Output: Symptom information in JSON format.

[1224] Step 2:

[1225] Data reception by the server

[1226] The server receives symptom information in JSON format sent from the device, stores the received information in a database, checks the data format, and formats it for analysis.

[1227] Input: API request containing symptom information in JSON format.

[1228] Output: Symptom information formatted for analysis.

[1229] Step 3:

[1230] Sending data to the generation AI

[1231] The server makes an API call to send the formatted symptom information to the generation AI. Through the API call, the symptom data is passed to the generation AI system (e.g., ChatGPT).

[1232] Input: Formatted symptom information.

[1233] Output: Symptom information received by the generation AI.

[1234] Step 4:

[1235] Symptom analysis and over-the-counter medication or meal plan suggestions

[1236] The AI ​​analyzes the received symptom data, retrieves data relating to symptoms and over-the-counter medications or meal plans from an internal database, and generates a list of candidates for over-the-counter medications and meal plans that are optimal for the user's symptoms based on the analysis.

[1237] Input: Symptom data.

[1238] Output: A list of over-the-counter medications or meal options.

[1239] Step 5:

[1240] Server formatting and returning the list

[1241] The server receives the list of over-the-counter medications or meal options returned by the generation AI, formats it into a user-friendly format, and then sends the formatted list to the device.

[1242] Input: A list of over-the-counter medications or meal options.

[1243] Output: A user-friendly formatted list.

[1244] Step 6:

[1245] View the list and request more information

[1246] The user checks the list of suggested over-the-counter medications or meal plans on the device, and if they want more information about a particular item, they click the details button for that item, and the device sends a request for more information to the server.

[1247] Input: User clicks the more info button.

[1248] Output: More information request.

[1249] Step 7:

[1250] Retrieving and displaying detailed information from the server

[1251] The server receives the request for more information, retrieves the details about the suggested over-the-counter medications and meal plans by referencing an internal database or an external information API, and then sends the retrieved details back to the device, which then displays them to the user.

[1252] Input: More information request.

[1253] Output: Detailed information (precautions for use, side effects, nutritional value, when to seek medical advice, etc.).

[1254] Through these steps, users will be recommended the most appropriate over-the-counter medications and meal plans based on their health condition, enabling comprehensive health management with detailed information.

[1255] 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.

[1256] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. In a specific embodiment, the system includes the following main components: a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface.

[1257] Program processing

[1258] User symptom input

[1259] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1260] Server: Receives symptom information and emotion data sent from the terminal, checks the data format and prepares it for analysis.

[1261] Symptom analysis

[1262] Server: Makes an API call to pass the received symptom information and emotion data to the generation AI means.

[1263] Generative AI: Analyzes the received symptom data, references the symptom-to-over-the-counter drug association data stored in an internal database, generates an index for selecting appropriate over-the-counter drugs, and adjusts the list of suggested over-the-counter drugs by taking into account emotional data.

[1264] Over-the-counter medication suggestions

[1265] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes suggested over-the-counter medication names and common uses.

[1266] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[1267] Device: Displays a list of over-the-counter medications that are best suited to the user.

[1268] Providing additional information

[1269] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1270] Terminal: Sends the user's request to the server.

[1271] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[1272] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[1273] Specific examples

[1274] Scenario: A user enters the symptoms "sore throat" and "runny nose" and the emotion of being stressed.

[1275] 1. User: Launches the app, enters "sore throat" and "runny nose" into the form, and the emotion engine recognizes that the user is feeling stressed. The user clicks the submit button.

[1276] 2. Terminal: Converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[1277] 3. Server: Receives data and sends symptom and emotion data via an interface to provide to the generation AI.

[1278] 4. Generative AI: Analyzes the received data and retrieves symptom and over-the-counter drug information from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a runny nose. Furthermore, if the user is feeling stressed, it also suggests products with a relaxation effect.

[1279] 5. Generation AI: Returns the selection results to the server.

[1280] 6. Server: Generates a response to return the results of the generated AI to the device.

[1281] 7. Terminal: Show the user a list of "lozenges," "nasal sprays," and "relaxation teas."

[1282] 8. User: If you want to know more information about "lozenge" from the list, click the More Information button.

[1283] 9. Terminal: Sends a detailed information request to the server.

[1284] 10. Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[1285] 11. Terminal: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[1286] In this way, users can self-diagnose and select the appropriate over-the-counter medication, and are provided with detailed information to support their health management, taking into account their emotional state.

[1287] The processing flow will be explained below.

[1288] Step 1:

[1289] User: Launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1290] Step 2:

[1291] Terminal: Converts the input symptom data and emotion data into JSON format and prepares an API request to send to the server. Sends the symptom data and emotion data to the API endpoint.

[1292] Step 3:

[1293] Server: Receives symptom data and emotion data sent from the device and checks the data format. If there are no problems with the format, prepares to transfer it to the generation AI means.

[1294] Step 4:

[1295] Server: Sends symptom and emotion data to the API endpoint of the generative AI method. Establishes a secure connection using the endpoint URL and authentication information.

[1296] Step 5:

[1297] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for "sore throat" and "nasal spray" for "runny nose." It also takes into account emotional data and suggests additional products with a relaxation effect for users who are feeling stressed.

[1298] Step 6:

[1299] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and common uses of the suggested over-the-counter medications. The generated over-the-counter medication list is returned to the server in JSON format.

[1300] Step 7:

[1301] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[1302] Step 8:

[1303] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[1304] Step 9:

[1305] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1306] Step 10:

[1307] Device: Sends a request for user details to the server.

[1308] Step 11:

[1309] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[1310] Step 12:

[1311] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[1312] In this way, users can easily obtain over-the-counter medications that suit their symptoms and feelings, and also check the necessary detailed information.

[1313] Example 2

[1314] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1315] In modern society, many people face a variety of symptoms and health problems on a daily basis. It is also known that various emotions and psychological states affect health, but it is difficult to comprehensively evaluate these and select appropriate over-the-counter medications. It is particularly difficult for general users without specialized knowledge to select appropriate over-the-counter medications taking into account emotional states and symptoms, raising concerns about inaccurate self-diagnosis and overmedication.

[1316] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input symptoms and emotions, a generation AI means for analyzing the symptoms and emotions, means for the generation AI means to suggest appropriate over-the-counter drugs based on the analysis results, means for displaying detailed information about the suggested over-the-counter drugs, means for transmitting the user's symptom and emotion information to the server, means for the server to pass the symptom and emotion information to the generation AI means, means for the generation AI means to acquire associations between symptoms and emotions and over-the-counter drugs from an internal database so that the generation AI means can evaluate the suitability of the over-the-counter drugs, and means for generating a candidate list of over-the-counter drugs using the acquired association information. This allows the user to easily select an appropriate over-the-counter drug taking into account their symptoms and emotions.

[1317] "User" refers to a person who utilizes the system to input symptoms and emotions.

[1318] "Symptom" means physical or mental health information entered by a User.

[1319] "Emotion" refers to the psychological state analyzed from the user's facial expressions and voice.

[1320] "Terminal" refers to a device used by a user to enter input, including a smartphone, tablet, PC, etc.

[1321] "Server" refers to the infrastructure that receives data from users and passes it on to the Generative AI Means.

[1322] "Generative AI" refers to artificial intelligence that analyzes input symptoms and emotions and suggests appropriate over-the-counter medications.

[1323] "Internal database" refers to data storage that stores information on the relationship between symptoms and over-the-counter medications.

[1324] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies.

[1325] "List" refers to a list of over-the-counter medications suggested to the user by the generating AI.

[1326] "Detailed information" refers to information about product specifications, such as precautions for use, side effects, and when to seek medical advice.

[1327] An "emotion engine" refers to technology that recognizes and analyzes emotions from a user's facial expressions and voice.

[1328] "Interface" refers to the operation screen and input form that allow the user to interact with the system.

[1329] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. The system's main components are a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface. The hardware used includes user terminals such as smartphones, tablets, and PCs, as well as a server system. The software applied to this hardware includes an application (or web interface), a generative AI model, an emotion engine, and so on.

[1330] The user launches the application or web interface using a device such as a smartphone, tablet, or PC. The user enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. The emotion engine then analyzes the user's facial expressions and voice to collect emotional data. The device then converts the entered symptom and emotional data into JSON format for transmission to the server.

[1331] The server receives the symptom and emotion data sent from the device and makes an API call to pass it to the generation AI for analysis. The generation AI analyzes the received data and retrieves association data between symptoms and over-the-counter medications from an internal database. Based on the analysis results, it then generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and general uses of the suggested over-the-counter medications.

[1332] The over-the-counter medication list returned from the generation AI to the server is formatted for the user and sent to the device as a response. The device displays the over-the-counter medication list that best suits the user. If the user wants more information about a specific medication, they click the more information button, which sends a request for more information to the server. The server calls an internal database or an external medical information API to obtain detailed information about the requested medication. The obtained information is returned to the device in JSON format, and the device generates a UI to display the detailed information and displays it to the user.

[1333] For example, if a user inputs the symptoms of "sore throat" and "runny nose" and the emotion engine recognizes the user's emotion as "stress," the generative AI will analyze this information and select a "lozenge" for the "sore throat" and a "nasal spray" for the "runny nose," and also suggest "relaxation tea" to relieve stress. If the user wants more information about the "lozenge," they can click the more information button, which will display detailed information such as precautions for use, side effects, and when to see a doctor.

[1334] Examples of prompts to input into a generative AI model include:

[1335] Example prompt sentence:

[1336] If a user enters symptoms such as "sore throat" or "runny nose," the emotion engine recognizes that this is "stressed." Suggest the best over-the-counter medication for this user.

[1337] This concludes the detailed description of the embodiment of the present invention, which allows users to select appropriate over-the-counter medications based on self-diagnosis and manage their health while taking into account their emotional state.

[1338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1339] Step 1: User enters symptoms and feelings

[1340] User: Launches the app or web interface from a device such as a smartphone, tablet, or PC. Enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. At the same time, the emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[1341] Input: Symptom information such as "sore throat" and "runny nose" and emotion data.

[1342] Output: Symptom and emotion data in JSON format.

[1343] Step 2: Send data from the device to the server

[1344] Terminal: Converts the input symptom data and emotion data into JSON format and sends it to the server as an API request.

[1345] Input: User-entered symptom and emotion data.

[1346] Output: The API request in JSON format sent to the server.

[1347] Step 3: Server receives and prepares data

[1348] Server: Receives symptom data and emotion data sent from the device, checks the format of the received data, and prepares an interface to pass it to the generation AI for analysis.

[1349] Input: Symptom and emotion data received from the device in JSON format.

[1350] Output: Symptom and emotion data prepared for analysis.

[1351] Step 4: Generative AI analyzes symptoms and emotions

[1352] Server: Makes API calls to pass symptom data and emotion data to the generation AI.

[1353] Generative AI: Analyzes the received symptom and emotion data, retrieves data relating to symptoms and over-the-counter medications from an internal database, and generates a list of over-the-counter medications that best fit the symptoms and emotions based on the analysis results.

[1354] Input: Symptom data and emotion data sent from the server.

[1355] Output: A list of appropriate over-the-counter medications.

[1356] Step 5: Sending analysis results from the generation AI to the server

[1357] Generative AI: Returns a list of over-the-counter drugs based on the analysis results to the server.

[1358] Server: Receives the list of over-the-counter drugs returned by the generation AI, formats it for the user, and generates a response.

[1359] Input: A list of over-the-counter medications returned by the generation AI.

[1360] Output: A formatted response with a list of over-the-counter medications.

[1361] Step 6: Sending a response from the server to the device

[1362] Server: Sends the prepared response to the device via API.

[1363] Input: A formatted response of a list of over-the-counter medications.

[1364] Output: The response data sent to the device.

[1365] Step 7: Displaying a list of over-the-counter medications on your device

[1366] Terminal: Displays the over-the-counter drug list received from the server to the user.

[1367] Input: The response data sent by the server.

[1368] Output: The over-the-counter medication list displayed to the user.

[1369] Step 8: User requests additional information

[1370] User: If the user wants to know more information about a particular drug from the list of over-the-counter drugs displayed, he or she clicks on the More Information button.

[1371] Input: Select the drug you want more information about.

[1372] Output: Generate a request for more information.

[1373] Step 9: Request for more information from the device to the server

[1374] Terminal: Sends a detailed information request to the server.

[1375] Input: A request for more information about the drug selected by the user.

[1376] Output: The request data sent to the server.

[1377] Step 10: Server Gets More Information

[1378] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the target drug.

[1379] Input: More information request received from the device.

[1380] Output: Detailed information data obtained.

[1381] Step 11: Sending detailed information from the server to the device

[1382] Server: Returns the acquired detailed information to the terminal in JSON format.

[1383] Input: Detailed information data obtained.

[1384] Output: Detailed information returned in JSON format.

[1385] Step 12: Viewing detailed information via terminal

[1386] Terminal: Based on the received detailed information, a UI is generated to display detailed information such as precautions for use, side effects, and when to seek medical attention.

[1387] Input: The details sent by the server.

[1388] Output: Detailed information that is displayed to the user.

[1389] (Application example 2)

[1390] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1391] Systems that simply suggest over-the-counter medications based on the symptoms entered by the user are unable to take the user's emotional state into account when making suggestions, making it difficult to provide optimal over-the-counter medications, health foods, and food delivery options that reflect the user's overall health and psychological state. Furthermore, they are unable to suggest health foods other than over-the-counter medications or provide options for food delivery, which means they are unable to fully meet the user's needs.

[1392] The identification processing by the identification 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 means for the user to input symptoms, a generation AI means for analyzing the symptoms, means for the generation AI means to suggest appropriate over-the-counter medications based on the analysis results, means for displaying detailed information about the suggested over-the-counter medications, means for recognizing and analyzing the user's emotions, and means for suggesting over-the-counter medications, health foods, and food delivery options using the emotion data. This makes it possible to comprehensively analyze the user's symptoms and emotions and provide optimal over-the-counter medications, health foods, and food delivery options.

[1393] A "means for user input of symptoms" is a device or software that provides an interface for a user to input their specific symptoms.

[1394] A "generative AI means for analyzing symptoms" is a device or software that uses artificial intelligence technology to analyze symptoms entered by a user and make appropriate suggestions based on the results of that analysis.

[1395] "Means for suggesting appropriate over-the-counter medications based on the analysis results" refers to a device or software that selects and suggests the over-the-counter medication that is most suitable for the user's symptoms from the analysis results output by the generation AI means.

[1396] "Means for displaying detailed information about suggested over-the-counter drugs" means a device or software for displaying detailed information about the over-the-counter drugs suggested by the generating AI means to the user, such as their usage and side effects.

[1397] "Means for recognizing and analyzing user emotions" refers to a device or software that analyzes emotions from the user's facial expressions, voice, etc., and collects that emotional data.

[1398] The "means for using emotional data to suggest over-the-counter medications, health foods, and food delivery options" refers to a device or software that uses the analyzed emotional data to suggest optimal over-the-counter medications, health foods, and food delivery options, taking into account the user's emotional state.

[1399] The "means for transmitting the user's symptom information to the server" refers to a device or software for transmitting the symptom information entered by the user to the server.

[1400] "Means for the server to pass symptom information and emotional data to the generating AI means" refers to a device or software that enables the server to provide the user's symptom information and emotional data to the generating AI means.

[1401] "Means for obtaining from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods" refers to a device or software that enables the generating AI means to obtain from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods.

[1402] The "means for generating a candidate list of over-the-counter drugs and health foods using the acquired relevance information" refers to a device or software for generating a candidate list of over-the-counter drugs and health foods based on the acquired relevance information.

[1403] This invention relates to a system that allows users to input their symptoms and then suggests optimal over-the-counter medications, health foods, and food delivery options based on the input. The system includes a user terminal, a server, a generative AI, an internal database, an emotion engine, and an interface.

[1404] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.) and inputs specific symptoms (e.g., "sore throat" or "runny nose"). The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1405] The server receives the symptom information and emotion data sent from the device, checks the data format, and prepares it for analysis. The prepared data is then passed to the generation AI, which analyzes the received data and references the association data between symptoms and related products stored in an internal database. The server generates an index for selecting appropriate over-the-counter medications, health foods, and food delivery options, and adjusts the suggested list taking the emotion data into account.

[1406] Based on the analysis results, the Generator AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions. This list includes the names and common uses of the suggested items. The server receives the list returned by the Generator AI, formats it for the user, and sends it to the device.

[1407] The device displays a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user. If the user wants to check more information about a suggested item, they click the item's more information button. The more information request is sent to the server. The server receives the more information request, calls an internal database or an external information API to retrieve more information (such as precautions for use, side effects, nutritional information, and ingredient information), and returns the retrieved details in JSON format to the device. The device generates a UI to display the retrieved details and displays them to the user.

[1408] As a concrete example, consider the case where a user inputs the symptoms of "sore throat" and "runny nose" and the emotion of feeling stressed. The user launches the app and enters "sore throat" and "runny nose" into the form, and the emotion engine detects the user's stress. When the user clicks the submit button, the device converts the input symptom data and emotion data into JSON format and sends an API request to the server. The server receives the data and sends the symptom data and emotion data via an interface to provide it to the generation AI.

[1409] The AI ​​analyzes the received data and retrieves symptoms, over-the-counter medications, health foods, and food delivery options from an internal database. For example, it might select a lozenge for a sore throat and a nasal spray for a runny nose. It might also suggest products with a relaxation effect (e.g., relaxation tea) if the user is feeling stressed.

[1410] The generation AI returns the selection results to the server. The server generates a response to return the generation AI's results to the device, and the device displays a list of "lozenges," "nasal spray," and "relaxation tea" to the user. If the user wants to know more information about "lozenges" from the list, they click the more information button. The device sends a more information request to the server, and the server references an internal database or external API to obtain more information about "lozenges" and returns it to the device. The device displays the detailed information about "lozenges" to the user, providing information such as precautions for use, side effects, and when to see a doctor.

[1411] Example prompt sentence:

[1412] "Symptoms: sore throat, tired. Emotion: stress. Based on this information, use generative AI to suggest the best over-the-counter medications, health foods, and delivery options."

[1413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1414] Step 1:

[1415] The user launches the application or web interface on their device and inputs their symptoms. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1416] Input: User symptom data (e.g., "sore throat" or "runny nose"), facial expressions, and voice data

[1417] Output: Symptom and emotion data in JSON format

[1418] Step 2:

[1419] The device converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[1420] Input: Symptom and emotion data in JSON format

[1421] Output: API request to the server

[1422] Step 3:

[1423] The server sends the received symptom information and emotion data via an interface to provide it to the generation AI, and checks the data format and prepares it for analysis.

[1424] Input: Symptom and emotion data sent from the device in JSON format

[1425] Output: Data to be passed to the generation AI

[1426] Step 4:

[1427] The generation AI analyzes the received data, retrieves data relating to symptoms and related products from an internal database, and generates an index based on the symptom data and emotion data.

[1428] Input: Symptom and emotion data sent from the server

[1429] Output: Index and relevance information

[1430] Specific operation: The generative AI analyzes symptom data using natural language processing and extracts relevant data from an internal database.

[1431] Step 5:

[1432] Based on the analysis results, generative AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions.

[1433] Input: Relevance information and index

[1434] Output: A shortlist of over-the-counter medications, health foods, and food delivery options

[1435] Specific operation: The generative AI refers to related data and lists high-priority products.

[1436] Step 6:

[1437] The server receives the candidate list returned by the generation AI, formats it for the user, and sends it to the device.

[1438] Input: Candidate list sent from the generation AI

[1439] Output: Response data to the terminal

[1440] Specific operation: The server converts the candidate list into a user-friendly format and sends it to the device as an API response.

[1441] Step 7:

[1442] The device will display a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user.

[1443] Input: Response data received from the server

[1444] Output: A list of products that can be viewed by the user

[1445] Specific operation: The device converts the received data into HTML and UI components for the app, and displays them on the screen.

[1446] Step 8:

[1447] If the user wants to check the detailed information of the suggested item, he / she clicks the detailed information button of the item, and the detailed information request is sent from the terminal to the server.

[1448] Input: User clicks item details button

[1449] Output: Request for more information from the server

[1450] Specific operation: The device receives the user's request and sends a detailed information request to the server.

[1451] Step 9:

[1452] The server receives the detailed information request, retrieves the detailed information by calling an internal database or an external information API, and returns it to the terminal.

[1453] Input: Request for more information from the terminal

[1454] Output: JSON data of detailed information

[1455] What happens: The server performs a database query or external API call to get the details and sends them to the device.

[1456] Step 10:

[1457] A UI is generated to display the detailed information acquired by the device and displayed to the user.

[1458] Input: JSON data of detailed information received from the server

[1459] Output: Detailed information displayed to the user

[1460] Specific operation: The device analyzes the received data, generates the components necessary to properly display detailed information on the UI, and displays them on the screen.

[1461] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1462] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1463] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1464] [Fourth embodiment]

[1465] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1466] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1467] 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).

[1468] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1469] 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.

[1470] 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).

[1471] 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.

[1472] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1473] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1474] 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.

[1475] 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.

[1476] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1477] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1478] The present invention is a system that utilizes a generative AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generative AI, an internal database, and an interface.

[1479] Program processing

[1480] User symptom input

[1481] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[1482] Server: Receives symptom information sent from the terminal, checks the data format and prepares it for analysis.

[1483] Symptom analysis

[1484] Server: Makes an API call to pass the received symptom information to the generation AI.

[1485] Generative AI: Analyzes the received symptom data, references the data relating to symptoms and over-the-counter medications stored in an internal database, and generates an index for selecting appropriate over-the-counter medications.

[1486] Over-the-counter medication suggestions

[1487] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. This list includes suggested over-the-counter medication names and common uses.

[1488] Server: Receives the over-the-counter drug list returned by the generation AI, formats it for the user, and returns it to the device.

[1489] Device: Displays a list of over-the-counter medications that are best suited to the user.

[1490] Providing additional information

[1491] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1492] Terminal: Sends the user's request to the server.

[1493] Server: Refers to an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and when to see a doctor).

[1494] Terminal: Display the obtained details to the user.

[1495] Specific examples

[1496] Scenario: User enters symptoms of "sore throat" and "runny nose"

[1497] User: Launches the app, enters "sore throat" and "runny nose," and clicks the send button.

[1498] Terminal: Converts input content into JSON format and sends an API request to the server.

[1499] Server: Receives data and sends symptom data via an interface to provide to the generation AI.

[1500] Generative AI: Analyzes the received data and retrieves over-the-counter drug information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[1501] Generation AI: Returns the selection results to the server.

[1502] Server: Generates a response to return the results of the generated AI to the device.

[1503] Terminal: Show the user a list of "lozenges" and "nasal sprays."

[1504] User: If you want to know more information about "lozenge" from the list, click the More Information button.

[1505] Terminal: Sends a detailed information request to the server.

[1506] Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[1507] Device: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[1508] In this way, users can self-diagnose and select appropriate over-the-counter medications, and are also provided with detailed information to support their health management.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] User: Using a device (smartphone, tablet, PC, etc.), the user launches the application or web interface. The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button.

[1512] Step 2:

[1513] Terminal: Converts the input symptom data into JSON format and prepares an API request to send to the server. Sends the symptom data to the API endpoint (e.g., https: / / example.com / api / symptoms).

[1514] Step 3:

[1515] Server: Receives symptom data sent from the device and checks the data format. If there are no problems with the format, prepares it for transfer to the generating AI means for analysis.

[1516] Step 4:

[1517] Server: Sends symptom data to the API endpoint of the generative AI solution. Establishes a secure connection using the endpoint URL and authentication information.

[1518] Step 5:

[1519] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for the symptom "sore throat" and "nasal spray" for the symptom "runny nose."

[1520] Step 6:

[1521] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms. The generated list of over-the-counter medications is returned to the server in JSON format.

[1522] Step 7:

[1523] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[1524] Step 8:

[1525] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[1526] Step 9:

[1527] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1528] Step 10:

[1529] Device: Sends a request for user details to the server.

[1530] Step 11:

[1531] Server: Receives the detailed information request, calls an internal database or an external medical information API to obtain detailed information about the drug, and returns the obtained details in JSON format to the terminal.

[1532] Step 12:

[1533] Terminal: Generates a UI to display the detailed information received from the server. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[1534] In this way, the system allows users to easily obtain information on over-the-counter medications that suit their symptoms and check the detailed information they need.

[1535] Example 1

[1536] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1537] In the past, users needed specialized knowledge to select the appropriate over-the-counter medication based on their symptoms, and using the wrong medication could pose health risks. Furthermore, obtaining detailed drug information required searching multiple sources, which was time-consuming and laborious. Therefore, there is a need for a system that allows users to easily select the appropriate over-the-counter medication and obtain detailed information about it.

[1538] 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.

[1539] In this invention, the server includes a means for converting the symptom data entered by the user into JSON format, a means for preparing the received symptom data for checking and analysis, and a means for retrieving detailed information from an internal database or an external information API and displaying it to the user, thereby enabling the user to easily select an appropriate over-the-counter medicine based on their symptoms and quickly obtain detailed information about it.

[1540] A "user" is an entity that uses the system to input their symptoms and receive suggestions for over-the-counter medications.

[1541] "Symptom" refers to any physical or mental discomfort experienced by the user, including, for example, "sore throat" or "runny nose."

[1542] "Generative AI" is part of a system that uses machine learning and artificial intelligence techniques to analyze symptom data entered by the user and suggest the most appropriate over-the-counter medication.

[1543] "Medicine" refers to over-the-counter medicines intended for the relief or treatment of symptoms.

[1544] The "server" is a computer system that manages the exchange of data between the user's device and the generating AI, and performs tasks such as preparing input data for analysis and obtaining detailed information.

[1545] A "database" is a collection of information that stores information on the relationship between symptoms and drugs and detailed information on drugs.

[1546] "API" stands for Application Program Interface, a means for different software applications to communicate with each other.

[1547] "JSON format" is an abbreviation for JavaScript Object Notation, a lightweight data exchange format and a text format that is easy to read and write.

[1548] A "list" refers to an ordered collection of items, and in this system refers to a list containing suitable over-the-counter drug candidates.

[1549] "Detailed information" refers to additional information that users want to know, such as precautions for use, side effects, and when to seek medical attention for a drug.

[1550] An "interface" is the means by which a user interacts with a system, including screens, input forms, etc.

[1551] The present invention is a system that utilizes a generating AI to suggest the most suitable over-the-counter medication when a user inputs their current symptoms. In a specific embodiment, the system includes the following main components: a user terminal, a server, a generating AI, an internal database, and an interface.

[1552] Hardware and software used

[1553] Hardware: User devices (smartphones, tablets, PCs)

[1554] Software: web or application interface, server software, generative AI, internal database

[1555] Data processing and calculation

[1556] Data format conversion: JSON format

[1557] API Call: Generate AI API for analytics

[1558] Internal database access: symptom and over-the-counter drug association data and detailed information

[1559] Overview of program processing

[1560] User symptom input

[1561] The user launches the application or web interface using a device such as a smartphone or PC, enters their symptoms into the input form, and clicks the submit button.

[1562] Submitting symptom data and preparing for analysis

[1563] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[1564] The server checks and formats the received symptom data, cleaning and formatting the data as needed.

[1565] Symptom analysis

[1566] The server makes an API call to pass the clean data to the generation AI.

[1567] The generated AI analyzes the symptom data it receives and compares it with an internal database to select the most appropriate over-the-counter medication.

[1568] Over-the-counter medication suggestions

[1569] Based on the results of the symptom analysis, the generative AI creates a list of over-the-counter medications that are best suited for the user, including the name of each drug and its common uses.

[1570] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[1571] Providing additional information

[1572] If the user wants more information about a particular drug from the list of suggested over-the-counter medications, they can click on the drug's more information button.

[1573] The device sends the user's request to the server.

[1574] The server references an internal database or an external medical information API to obtain detailed information about the drug.

[1575] The device displays the obtained details to the user.

[1576] Specific examples

[1577] Scenario: User enters symptoms of "sore throat" and "runny nose"

[1578] The user launches the app, types in "sore throat" or "runny nose," and clicks the send button.

[1579] The terminal converts the input content into JSON format and sends an API request to the server.

[1580] The server receives the data and sends the symptom data via an interface to provide to the generation AI.

[1581] The generative AI analyzes the received data and retrieves over-the-counter medication information related to the symptoms from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a "runny nose."

[1582] The generation AI returns the selection results to the server.

[1583] The server generates a response to return the results of the generation AI to the terminal.

[1584] The device will present the user with a list of "lozenges" and "nasal sprays."

[1585] If the user wants to know more information about a "lozenge" from the list, he or she can click on the more information button.

[1586] The terminal sends a detailed information request to the server.

[1587] The server references an internal database or an external API to obtain detailed information about the "lozenge" and returns it to the device.

[1588] The device displays detailed information about the lozenge to the user (such as precautions for use, side effects, and when to seek medical advice).

[1589] Prompt Sentence Examples

[1590] User: Suggest suitable over-the-counter medications for symptoms like "sore throat" and "runny nose."

[1591] This system allows users to self-diagnose and select appropriate over-the-counter medications to help manage their health. It also provides detailed drug information, allowing users to choose medications with confidence.

[1592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1593] Step 1:

[1594] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.), enters specific symptoms such as "sore throat" or "runny nose" into the input form, and clicks the submit button.

[1595] Input: User-entered symptom data (e.g., "sore throat" or "runny nose").

[1596] Output: Trigger when the submit button is clicked and symptom data is sent.

[1597] Specific actions: The user checks the symptom information entered in the interface and clicks the submit button.

[1598] Step 2:

[1599] The device converts the symptoms entered by the user into JSON format, creates an API request, and sends it to the server.

[1600] Input: User-submitted symptom data.

[1601] Output: Symptom data converted to JSON format.

[1602] Specific operation: The terminal receives input data, runs a formatter to convert it into JSON format, and then generates an API request and sends it to the server.

[1603] Step 3:

[1604] The server checks the received symptom data, performs any necessary cleaning (e.g., removing special characters and standardizing formatting), and prepares the data for analysis.

[1605] Input: Symptom data submitted in JSON format.

[1606] Output: Clean data format.

[1607] What happens: The server validates the format of the data it receives, checks for invalid data, and, if necessary, removes extra spaces and special characters and formats the data.

[1608] Step 4:

[1609] The server makes an API call to pass the prepared data to the generative AI for analysis.

[1610] Input: Clean symptom data.

[1611] Output: API request to the generating AI.

[1612] Specific operation: The server converts the clean data back into API request format and sends it to the generative AI's analysis engine.

[1613] Step 5:

[1614] The generated AI analyzes the symptom data it receives and refers to an internal database to select the most appropriate over-the-counter medication.

[1615] Input: Clean symptom data sent from the server.

[1616] Output: A list of the best over-the-counter medications.

[1617] How it works: The generative AI passes symptom data to an analysis algorithm, searches an internal database for symptom-drug correlation data, and generates a list of the most suitable over-the-counter medications.

[1618] Step 6:

[1619] Based on the results of symptom analysis, the generative AI creates a list of over-the-counter medications that are optimal for the user and returns it to the server.

[1620] Input: Analysis results.

[1621] Output: List of over-the-counter medications.

[1622] Specific operation: The generation AI formats the analysis results into a list and sends the data back to the server.

[1623] Step 7:

[1624] The server formats the list of over-the-counter drugs received from the generation AI into a format that is easy for the user to understand and returns it to the terminal.

[1625] Input: List of over-the-counter medications received from the generation AI.

[1626] Output: A formatted list for display to the user.

[1627] Specific operation: The server formats the list received from the generation AI into a user-friendly format, generates an API response, and sends it to the device.

[1628] Step 8:

[1629] The device displays the response received from the server to the user, showing a list of over-the-counter medications such as "lozenges for sore throats" and "nasal spray for runny noses."

[1630] Input: A formatted over-the-counter medication list from the server.

[1631] Output: A list of over-the-counter medications displayed in the user interface.

[1632] What happens: The device renders the received data into a user interface and displays a list of appropriate over-the-counter medications.

[1633] Step 9:

[1634] If the user wishes to know more information about a particular drug from the list, he or she clicks on the drug's detailed information button.

[1635] Input: User click.

[1636] Output: Request for more information.

[1637] What happens: When the user clicks, a request for more information is sent to the server.

[1638] Step 10:

[1639] The terminal sends a detailed information request to the server.

[1640] Input: The user's request for more information.

[1641] Output: API request to the server.

[1642] Specific operation: The device sends the user's request to the server in the corresponding API request format.

[1643] Step 11:

[1644] The server receives the request and retrieves detailed information about the drug by referencing an internal database or an external medical information API.

[1645] Input: More information request.

[1646] Output: Detailed information.

[1647] What happens: The server performs a database lookup to get the required information, and if an external API is required, calls it to get the information.

[1648] Step 12:

[1649] The server generates a response to return the acquired detailed information to the terminal.

[1650] Input: More information.

[1651] Output: The response data.

[1652] Specific operation: The server formats the detailed information and sends the response data to the terminal.

[1653] Step 13:

[1654] The device displays the obtained details to the user.

[1655] Input: Detailed information response from the server.

[1656] Output: Detailed information displayed in the user interface.

[1657] What happens: The device renders the details in its user interface and displays them to the user.

[1658] (Application example 1)

[1659] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1660] Conventional over-the-counter drug recommendation systems only suggest the most appropriate over-the-counter drug based on the symptoms entered by the user. However, there is a need for a system that can suggest meal menus suited to the user's health condition, especially in the field of food delivery. The lack of such a system makes it difficult for users to choose the appropriate meal to improve their health condition.

[1661] 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.

[1662] In this invention, the server includes a means for the generation AI means to suggest appropriate over-the-counter drugs or meal menus based on the analysis results, a means for transmitting the user's symptom information to the server, and a means for the generation AI means to acquire from an internal database the association between the symptoms and over-the-counter drugs or meal menus so that the association can evaluate the suitability of the over-the-counter drugs or meal menus. This allows the user to be suggested the optimal over-the-counter drugs or meal menus based on their health condition, thereby supporting health management.

[1663] "Means for users to input symptoms" refers to a device or application that provides an interface for users to input their own health condition and specific symptoms.

[1664] The "generative AI means" is an artificial intelligence system that analyzes input symptom data and suggests appropriate over-the-counter medications and meal menus.

[1665] The "server" is a central processing unit that receives information sent from a user terminal, passes the information to the generation AI means, and returns the analysis results of the generation AI to the user.

[1666] The "internal database" is a data storage system that stores data relating to symptoms, over-the-counter medications, and meal plans.

[1667] "Means for suggesting over-the-counter medications or meal menus" refers to a device or application that presents users with candidates for over-the-counter medications or meal menus that suit the user's symptoms based on the results of analysis by the generative AI means.

[1668] The "means for displaying detailed information" refers to a device or interface for displaying detailed information about the proposed over-the-counter medicine or meal menu to the user, such as its effects, side effects, nutritional value, etc.

[1669] The "means for transmitting symptom information to a server" is an interface equipped with a communication function for transmitting symptom information input by a user to a terminal to a server.

[1670] The "means for acquiring associations" refers to a processing device or program for searching and acquiring association data between symptoms and over-the-counter drugs or meal menus from an internal database.

[1671] The "means for generating a candidate list" is a program for using the acquired association data to generate a list of over-the-counter medications or meal menus to suggest to the user.

[1672] The "means for linking with external database API" is an interface for communicating with an external database service to obtain detailed information.

[1673] The present invention is a system that utilizes generative AI to suggest optimal over-the-counter medications and meal plans when a user inputs their current symptoms. A specific embodiment of this system is described below.

[1674] System configuration and program overview

[1675] The system includes a user terminal, a server, a generating AI, an internal database, and an interface, and includes the following main processing steps:

[1676] 1. User symptom input

[1677] Device: Users use a device such as a smartphone, tablet, or computer to input symptoms via an application or web interface. Examples might include "sore throat" or "runny nose."

[1678] Server: Receives symptom information sent from the device, checks the data format, and prepares it for analysis.

[1679] 2. Symptom analysis

[1680] Server: Makes an API call to pass the received symptom information to the generation AI.

[1681] Generative AI: Analyzes the received data and retrieves data relating to symptoms and over-the-counter medicines or meal plans from an internal database. For example, if the user's symptoms include "sore throat," it will select over-the-counter medicines and meal plans that are good for the throat.

[1682] 3. Over-the-counter medication or meal suggestions

[1683] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications and meals that are best suited to the user's symptoms. Examples include "lozenges," "lemon tea," and "chicken soup."

[1684] Server: Receives the list returned by the generation AI, formats it for the user, and returns it to the device.

[1685] 4. Providing additional information

[1686] Users: Review the suggested list and if they want more information about a particular item, click on the more information button for that item.

[1687] Terminal: Sends a detailed information request to the server.

[1688] Server: Refers to an internal database or external information API to obtain detailed information about the item, such as precautions for use, side effects, nutritional value, and when to seek medical advice.

[1689] Terminal: Display the obtained details to the user.

[1690] Hardware and software used

[1691] Hardware: smartphones, tablets, PCs (user devices), servers

[1692] Software: Mobile applications, generative AI systems (e.g., ChatGPT), databases (e.g., MySQL), API interfaces (e.g., RESTful APIs)

[1693] Examples of specific examples and prompts

[1694] Examples:

[1695] If a user types "sore throat" into the app, the generative AI will suggest "lozenges," "lemon tea," "chicken soup," etc. If the user selects "lemon tea," detailed information about the tea (such as how to make it, its ingredients, and its effects) will be displayed.

[1696] Example prompt sentence:

[1697] "The current symptom is a sore throat. Please suggest some over-the-counter medicines or meals that will help with the sore throat."

[1698] As described above, the system of the present invention can utilize a generative AI model to suggest over-the-counter medications and meal menus in real time based on the user's health condition, thereby supporting health management.

[1699] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1700] Step 1:

[1701] User symptom input

[1702] Users can use a device such as a smartphone, tablet, or PC to input their symptoms via an application or web interface. For example, they can enter specific symptoms such as "sore throat" or "runny nose" into the input form and click the submit button. At this point, the input symptom information is converted into JSON format and sent as an API request.

[1703] Input: The user inputs symptoms. For example, they input "sore throat" and "runny nose."

[1704] Output: Symptom information in JSON format.

[1705] Step 2:

[1706] Data reception by the server

[1707] The server receives symptom information in JSON format sent from the device, stores the received information in a database, checks the data format, and formats it for analysis.

[1708] Input: API request containing symptom information in JSON format.

[1709] Output: Symptom information formatted for analysis.

[1710] Step 3:

[1711] Sending data to the generation AI

[1712] The server makes an API call to send the formatted symptom information to the generation AI. Through the API call, the symptom data is passed to the generation AI system (e.g., ChatGPT).

[1713] Input: Formatted symptom information.

[1714] Output: Symptom information received by the generation AI.

[1715] Step 4:

[1716] Symptom analysis and over-the-counter medication or meal plan suggestions

[1717] The AI ​​analyzes the received symptom data, retrieves data relating to symptoms and over-the-counter medications or meal plans from an internal database, and generates a list of candidates for over-the-counter medications and meal plans that are optimal for the user's symptoms based on the analysis.

[1718] Input: Symptom data.

[1719] Output: A list of over-the-counter medications or meal options.

[1720] Step 5:

[1721] Server formatting and returning the list

[1722] The server receives the list of over-the-counter medications or meal options returned by the generation AI, formats it into a user-friendly format, and then sends the formatted list to the device.

[1723] Input: A list of over-the-counter medications or meal options.

[1724] Output: A user-friendly formatted list.

[1725] Step 6:

[1726] View the list and request more information

[1727] The user checks the list of suggested over-the-counter medications or meal plans on the device, and if they want more information about a particular item, they click the details button for that item, and the device sends a request for more information to the server.

[1728] Input: User clicks the more info button.

[1729] Output: More information request.

[1730] Step 7:

[1731] Retrieving and displaying detailed information from the server

[1732] The server receives the request for more information, retrieves the details about the suggested over-the-counter medications and meal plans by referencing an internal database or an external information API, and then sends the retrieved details back to the device, which then displays them to the user.

[1733] Input: More information request.

[1734] Output: Detailed information (precautions for use, side effects, nutritional value, when to seek medical advice, etc.).

[1735] Through these steps, users will be recommended the most appropriate over-the-counter medications and meal plans based on their health condition, enabling comprehensive health management with detailed information.

[1736] 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.

[1737] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. In a specific embodiment, the system includes the following main components: a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface.

[1738] Program processing

[1739] User symptom input

[1740] Device: The user launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1741] Server: Receives symptom information and emotion data sent from the terminal, checks the data format and prepares it for analysis.

[1742] Symptom analysis

[1743] Server: Makes an API call to pass the received symptom information and emotion data to the generation AI means.

[1744] Generative AI: Analyzes the received symptom data, references the symptom-to-over-the-counter drug association data stored in an internal database, generates an index for selecting appropriate over-the-counter drugs, and adjusts the list of suggested over-the-counter drugs by taking into account emotional data.

[1745] Over-the-counter medication suggestions

[1746] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes suggested over-the-counter medication names and common uses.

[1747] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[1748] Device: Displays a list of over-the-counter medications that are best suited to the user.

[1749] Providing additional information

[1750] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1751] Terminal: Sends the user's request to the server.

[1752] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[1753] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[1754] Specific examples

[1755] Scenario: A user enters the symptoms "sore throat" and "runny nose" and the emotion of being stressed.

[1756] 1. User: Launches the app, enters "sore throat" and "runny nose" into the form, and the emotion engine recognizes that the user is feeling stressed. The user clicks the submit button.

[1757] 2. Terminal: Converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[1758] 3. Server: Receives data and sends symptom and emotion data via an interface to provide to the generation AI.

[1759] 4. Generative AI: Analyzes the received data and retrieves symptom and over-the-counter drug information from an internal database. For example, it selects "lozenges" as a suitable medicine for a "sore throat" and "nasal spray" as a suitable medicine for a runny nose. Furthermore, if the user is feeling stressed, it also suggests products with a relaxation effect.

[1760] 5. Generation AI: Returns the selection results to the server.

[1761] 6. Server: Generates a response to return the results of the generated AI to the device.

[1762] 7. Terminal: Show the user a list of "lozenges," "nasal sprays," and "relaxation teas."

[1763] 8. User: If you want to know more information about "lozenge" from the list, click the More Information button.

[1764] 9. Terminal: Sends a detailed information request to the server.

[1765] 10. Server: Refers to an internal database or external API to obtain detailed information about the "lozenge" and returns it to the device.

[1766] 11. Terminal: Displays detailed information about the "lozenge" to the user (such as precautions for use, side effects, and when to seek medical advice).

[1767] In this way, users can self-diagnose and select the appropriate over-the-counter medication, and are provided with detailed information to support their health management, taking into account their emotional state.

[1768] The processing flow will be explained below.

[1769] Step 1:

[1770] User: Launches the application or web interface using a device (smartphone, tablet, PC, etc.). The user enters specific symptoms, such as "sore throat" or "runny nose," into the input form and clicks the submit button. The emotion engine recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1771] Step 2:

[1772] Terminal: Converts the input symptom data and emotion data into JSON format and prepares an API request to send to the server. Sends the symptom data and emotion data to the API endpoint.

[1773] Step 3:

[1774] Server: Receives symptom data and emotion data sent from the device and checks the data format. If there are no problems with the format, prepares to transfer it to the generation AI means.

[1775] Step 4:

[1776] Server: Sends symptom and emotion data to the API endpoint of the generative AI method. Establishes a secure connection using the endpoint URL and authentication information.

[1777] Step 5:

[1778] Generative AI: Analyzes the received symptom data and references the association data between symptoms and over-the-counter medications stored in an internal database. For example, it selects "lozenges" for "sore throat" and "nasal spray" for "runny nose." It also takes into account emotional data and suggests additional products with a relaxation effect for users who are feeling stressed.

[1779] Step 6:

[1780] Generative AI: Based on the analysis results, it generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and common uses of the suggested over-the-counter medications. The generated over-the-counter medication list is returned to the server in JSON format.

[1781] Step 7:

[1782] Server: Receives the list of over-the-counter medications returned by the generation AI, formats it for the user, and sends the formatted response to the terminal.

[1783] Step 8:

[1784] Terminal: Generates a UI to display the over-the-counter medication list received from the server. Displays the list of over-the-counter medications that are most suitable for the user.

[1785] Step 9:

[1786] Users: Review the list of suggested over-the-counter medications and, if they want more information about a particular medication, click on the More Info button for that medication.

[1787] Step 10:

[1788] Device: Sends a request for user details to the server.

[1789] Step 11:

[1790] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the drug (such as precautions for use, side effects, and recommended doctor visits). The obtained detailed information is returned to the terminal in JSON format.

[1791] Step 12:

[1792] Device: Generates a UI to display the acquired detailed information. Detailed information such as precautions for use of the drug, side effects, and recommended doctor visits is displayed to the user.

[1793] In this way, users can easily obtain over-the-counter medications that suit their symptoms and feelings, and also check the necessary detailed information.

[1794] Example 2

[1795] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1796] In modern society, many people face a variety of symptoms and health problems on a daily basis. It is also known that various emotions and psychological states affect health, but it is difficult to comprehensively evaluate these and select appropriate over-the-counter medications. It is particularly difficult for general users without specialized knowledge to select appropriate over-the-counter medications taking into account emotional states and symptoms, raising concerns about inaccurate self-diagnosis and overmedication.

[1797] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input symptoms and emotions, a generation AI means for analyzing the symptoms and emotions, means for the generation AI means to suggest appropriate over-the-counter drugs based on the analysis results, means for displaying detailed information about the suggested over-the-counter drugs, means for transmitting the user's symptom and emotion information to the server, means for the server to pass the symptom and emotion information to the generation AI means, means for the generation AI means to acquire associations between symptoms and emotions and over-the-counter drugs from an internal database so that the generation AI means can evaluate the suitability of the over-the-counter drugs, and means for generating a candidate list of over-the-counter drugs using the acquired association information. This allows the user to easily select an appropriate over-the-counter drug taking into account their symptoms and emotions.

[1798] "User" refers to a person who utilizes the system to input symptoms and emotions.

[1799] "Symptom" means physical or mental health information entered by a User.

[1800] "Emotion" refers to the psychological state analyzed from the user's facial expressions and voice.

[1801] "Terminal" refers to a device used by a user to enter input, including a smartphone, tablet, PC, etc.

[1802] "Server" refers to the infrastructure that receives data from users and passes it on to the Generative AI Means.

[1803] "Generative AI" refers to artificial intelligence that analyzes input symptoms and emotions and suggests appropriate over-the-counter medications.

[1804] "Internal database" refers to data storage that stores information on the relationship between symptoms and over-the-counter medications.

[1805] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies.

[1806] "List" refers to a list of over-the-counter medications suggested to the user by the generating AI.

[1807] "Detailed information" refers to information about product specifications, such as precautions for use, side effects, and when to seek medical advice.

[1808] An "emotion engine" refers to technology that recognizes and analyzes emotions from a user's facial expressions and voice.

[1809] "Interface" refers to the operation screen and input form that allow the user to interact with the system.

[1810] The present invention is a system that analyzes symptoms and emotions entered by a user and uses generative AI to suggest the most appropriate over-the-counter medication. The system's main components are a user terminal, a server, generative AI, an internal database, an emotion engine, and an interface. The hardware used includes user terminals such as smartphones, tablets, and PCs, as well as a server system. The software applied to this hardware includes an application (or web interface), a generative AI model, an emotion engine, and so on.

[1811] The user launches the application or web interface using a device such as a smartphone, tablet, or PC. The user enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. The emotion engine then analyzes the user's facial expressions and voice to collect emotional data. The device then converts the entered symptom and emotional data into JSON format for transmission to the server.

[1812] The server receives the symptom and emotion data sent from the device and makes an API call to pass it to the generation AI for analysis. The generation AI analyzes the received data and retrieves association data between symptoms and over-the-counter medications from an internal database. Based on the analysis results, it then generates a list of over-the-counter medications that are optimal for the user's symptoms and emotions. This list includes the names and general uses of the suggested over-the-counter medications.

[1813] The over-the-counter medication list returned from the generation AI to the server is formatted for the user and sent to the device as a response. The device displays the over-the-counter medication list that best suits the user. If the user wants more information about a specific medication, they click the more information button, which sends a request for more information to the server. The server calls an internal database or an external medical information API to obtain detailed information about the requested medication. The obtained information is returned to the device in JSON format, and the device generates a UI to display the detailed information and displays it to the user.

[1814] For example, if a user inputs the symptoms of "sore throat" and "runny nose" and the emotion engine recognizes the user's emotion as "stress," the generative AI will analyze this information and select a "lozenge" for the "sore throat" and a "nasal spray" for the "runny nose," and also suggest "relaxation tea" to relieve stress. If the user wants more information about the "lozenge," they can click the more information button, which will display detailed information such as precautions for use, side effects, and when to see a doctor.

[1815] Examples of prompts to input into a generative AI model include:

[1816] Example prompt sentence:

[1817] If a user enters symptoms such as "sore throat" or "runny nose," the emotion engine recognizes that this is "stressed." Suggest the best over-the-counter medication for this user.

[1818] This concludes the detailed description of the embodiment of the present invention, which allows users to select appropriate over-the-counter medications based on self-diagnosis and manage their health while taking into account their emotional state.

[1819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1820] Step 1: User enters symptoms and feelings

[1821] User: Launches the app or web interface from a device such as a smartphone, tablet, or PC. Enters symptoms such as "sore throat" or "runny nose" into the form and clicks the submit button. At the same time, the emotion engine analyzes the user's facial expressions and voice to collect emotional data.

[1822] Input: Symptom information such as "sore throat" and "runny nose" and emotion data.

[1823] Output: Symptom and emotion data in JSON format.

[1824] Step 2: Send data from the device to the server

[1825] Terminal: Converts the input symptom data and emotion data into JSON format and sends it to the server as an API request.

[1826] Input: User-entered symptom and emotion data.

[1827] Output: The API request in JSON format sent to the server.

[1828] Step 3: Server receives and prepares data

[1829] Server: Receives symptom data and emotion data sent from the device, checks the format of the received data, and prepares an interface to pass it to the generation AI for analysis.

[1830] Input: Symptom and emotion data received from the device in JSON format.

[1831] Output: Symptom and emotion data prepared for analysis.

[1832] Step 4: Generative AI analyzes symptoms and emotions

[1833] Server: Makes API calls to pass symptom data and emotion data to the generation AI.

[1834] Generative AI: Analyzes the received symptom and emotion data, retrieves data relating to symptoms and over-the-counter medications from an internal database, and generates a list of over-the-counter medications that best fit the symptoms and emotions based on the analysis results.

[1835] Input: Symptom data and emotion data sent from the server.

[1836] Output: A list of appropriate over-the-counter medications.

[1837] Step 5: Sending analysis results from the generation AI to the server

[1838] Generative AI: Returns a list of over-the-counter drugs based on the analysis results to the server.

[1839] Server: Receives the list of over-the-counter drugs returned by the generation AI, formats it for the user, and generates a response.

[1840] Input: A list of over-the-counter medications returned by the generation AI.

[1841] Output: A formatted response with a list of over-the-counter medications.

[1842] Step 6: Sending a response from the server to the device

[1843] Server: Sends the prepared response to the device via API.

[1844] Input: A formatted response of a list of over-the-counter medications.

[1845] Output: The response data sent to the device.

[1846] Step 7: Displaying a list of over-the-counter medications on your device

[1847] Terminal: Displays the over-the-counter drug list received from the server to the user.

[1848] Input: The response data sent by the server.

[1849] Output: The over-the-counter medication list displayed to the user.

[1850] Step 8: User requests additional information

[1851] User: If the user wants to know more information about a particular drug from the list of over-the-counter drugs displayed, he or she clicks on the More Information button.

[1852] Input: Select the drug you want more information about.

[1853] Output: Generate a request for more information.

[1854] Step 9: Request for more information from the device to the server

[1855] Terminal: Sends a detailed information request to the server.

[1856] Input: A request for more information about the drug selected by the user.

[1857] Output: The request data sent to the server.

[1858] Step 10: Server Gets More Information

[1859] Server: Receives the detailed information request and calls an internal database or an external medical information API to obtain detailed information about the target drug.

[1860] Input: More information request received from the device.

[1861] Output: Detailed information data obtained.

[1862] Step 11: Sending detailed information from the server to the device

[1863] Server: Returns the acquired detailed information to the terminal in JSON format.

[1864] Input: Detailed information data obtained.

[1865] Output: Detailed information returned in JSON format.

[1866] Step 12: Viewing detailed information via terminal

[1867] Terminal: Based on the received detailed information, a UI is generated to display detailed information such as precautions for use, side effects, and when to seek medical attention.

[1868] Input: The details sent by the server.

[1869] Output: Detailed information that is displayed to the user.

[1870] (Application example 2)

[1871] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1872] Systems that simply suggest over-the-counter medications based on the symptoms entered by the user are unable to take the user's emotional state into account when making suggestions, making it difficult to provide optimal over-the-counter medications, health foods, and food delivery options that reflect the user's overall health and psychological state. Furthermore, they are unable to suggest health foods other than over-the-counter medications or provide options for food delivery, which means they are unable to fully meet the user's needs.

[1873] The identification processing by the identification 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 means for the user to input symptoms, a generation AI means for analyzing the symptoms, means for the generation AI means to suggest appropriate over-the-counter medications based on the analysis results, means for displaying detailed information about the suggested over-the-counter medications, means for recognizing and analyzing the user's emotions, and means for suggesting over-the-counter medications, health foods, and food delivery options using the emotion data. This makes it possible to comprehensively analyze the user's symptoms and emotions and provide optimal over-the-counter medications, health foods, and food delivery options.

[1874] A "means for user input of symptoms" is a device or software that provides an interface for a user to input their specific symptoms.

[1875] A "generative AI means for analyzing symptoms" is a device or software that uses artificial intelligence technology to analyze symptoms entered by a user and make appropriate suggestions based on the results of that analysis.

[1876] "Means for suggesting appropriate over-the-counter medications based on the analysis results" refers to a device or software that selects and suggests the over-the-counter medication that is most suitable for the user's symptoms from the analysis results output by the generation AI means.

[1877] "Means for displaying detailed information about suggested over-the-counter drugs" means a device or software for displaying detailed information about the over-the-counter drugs suggested by the generating AI means to the user, such as their usage and side effects.

[1878] "Means for recognizing and analyzing user emotions" refers to a device or software that analyzes emotions from the user's facial expressions, voice, etc., and collects that emotional data.

[1879] The "means for using emotional data to suggest over-the-counter medications, health foods, and food delivery options" refers to a device or software that uses the analyzed emotional data to suggest optimal over-the-counter medications, health foods, and food delivery options, taking into account the user's emotional state.

[1880] The "means for transmitting the user's symptom information to the server" refers to a device or software for transmitting the symptom information entered by the user to the server.

[1881] "Means for the server to pass symptom information and emotional data to the generating AI means" refers to a device or software that enables the server to provide the user's symptom information and emotional data to the generating AI means.

[1882] "Means for obtaining from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods" refers to a device or software that enables the generating AI means to obtain from an internal database the association between symptoms and related products in order to evaluate the suitability of over-the-counter drugs and health foods.

[1883] The "means for generating a candidate list of over-the-counter drugs and health foods using the acquired relevance information" refers to a device or software for generating a candidate list of over-the-counter drugs and health foods based on the acquired relevance information.

[1884] This invention relates to a system that allows users to input their symptoms and then suggests optimal over-the-counter medications, health foods, and food delivery options based on the input. The system includes a user terminal, a server, a generative AI, an internal database, an emotion engine, and an interface.

[1885] The user launches the application or web interface using a device (smartphone, tablet, PC, etc.) and inputs specific symptoms (e.g., "sore throat" or "runny nose"). The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1886] The server receives the symptom information and emotion data sent from the device, checks the data format, and prepares it for analysis. The prepared data is then passed to the generation AI, which analyzes the received data and references the association data between symptoms and related products stored in an internal database. The server generates an index for selecting appropriate over-the-counter medications, health foods, and food delivery options, and adjusts the suggested list taking the emotion data into account.

[1887] Based on the analysis results, the Generator AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions. This list includes the names and common uses of the suggested items. The server receives the list returned by the Generator AI, formats it for the user, and sends it to the device.

[1888] The device displays a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user. If the user wants to check more information about a suggested item, they click the item's more information button. The more information request is sent to the server. The server receives the more information request, calls an internal database or an external information API to retrieve more information (such as precautions for use, side effects, nutritional information, and ingredient information), and returns the retrieved details in JSON format to the device. The device generates a UI to display the retrieved details and displays them to the user.

[1889] As a concrete example, consider the case where a user inputs the symptoms of "sore throat" and "runny nose" and the emotion of feeling stressed. The user launches the app and enters "sore throat" and "runny nose" into the form, and the emotion engine detects the user's stress. When the user clicks the submit button, the device converts the input symptom data and emotion data into JSON format and sends an API request to the server. The server receives the data and sends the symptom data and emotion data via an interface to provide it to the generation AI.

[1890] The AI ​​analyzes the received data and retrieves symptoms, over-the-counter medications, health foods, and food delivery options from an internal database. For example, it might select a lozenge for a sore throat and a nasal spray for a runny nose. It might also suggest products with a relaxation effect (e.g., relaxation tea) if the user is feeling stressed.

[1891] The generation AI returns the selection results to the server. The server generates a response to return the generation AI's results to the device, and the device displays a list of "lozenges," "nasal spray," and "relaxation tea" to the user. If the user wants to know more information about "lozenges" from the list, they click the more information button. The device sends a more information request to the server, and the server references an internal database or external API to obtain more information about "lozenges" and returns it to the device. The device displays the detailed information about "lozenges" to the user, providing information such as precautions for use, side effects, and when to see a doctor.

[1892] Example prompt sentence:

[1893] "Symptoms: sore throat, tired. Emotion: stress. Based on this information, use generative AI to suggest the best over-the-counter medications, health foods, and delivery options."

[1894] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1895] Step 1:

[1896] The user launches the application or web interface on their device and inputs their symptoms. The emotion engine then recognizes emotions from the user's facial expressions and voice and collects emotion data.

[1897] Input: User symptom data (e.g., "sore throat" or "runny nose"), facial expressions, and voice data

[1898] Output: Symptom and emotion data in JSON format

[1899] Step 2:

[1900] The device converts the input symptom data and emotion data into JSON format and sends an API request to the server.

[1901] Input: Symptom and emotion data in JSON format

[1902] Output: API request to the server

[1903] Step 3:

[1904] The server sends the received symptom information and emotion data via an interface to provide it to the generation AI, and checks the data format and prepares it for analysis.

[1905] Input: Symptom and emotion data sent from the device in JSON format

[1906] Output: Data to be passed to the generation AI

[1907] Step 4:

[1908] The generation AI analyzes the received data, retrieves data relating to symptoms and related products from an internal database, and generates an index based on the symptom data and emotion data.

[1909] Input: Symptom and emotion data sent from the server

[1910] Output: Index and relevance information

[1911] Specific operation: The generative AI analyzes symptom data using natural language processing and extracts relevant data from an internal database.

[1912] Step 5:

[1913] Based on the analysis results, generative AI generates a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user's symptoms and emotions.

[1914] Input: Relevance information and index

[1915] Output: A shortlist of over-the-counter medications, health foods, and food delivery options

[1916] Specific operation: The generative AI refers to related data and lists high-priority products.

[1917] Step 6:

[1918] The server receives the candidate list returned by the generation AI, formats it for the user, and sends it to the device.

[1919] Input: Candidate list sent from the generation AI

[1920] Output: Response data to the terminal

[1921] Specific operation: The server converts the candidate list into a user-friendly format and sends it to the device as an API response.

[1922] Step 7:

[1923] The device will display a list of over-the-counter medications, health foods, and food delivery options that are best suited to the user.

[1924] Input: Response data received from the server

[1925] Output: A list of products that can be viewed by the user

[1926] Specific operation: The device converts the received data into HTML and UI components for the app, and displays them on the screen.

[1927] Step 8:

[1928] If the user wants to check the detailed information of the suggested item, he / she clicks the detailed information button of the item, and the detailed information request is sent from the terminal to the server.

[1929] Input: User clicks item details button

[1930] Output: Request for more information from the server

[1931] Specific operation: The device receives the user's request and sends a detailed information request to the server.

[1932] Step 9:

[1933] The server receives the detailed information request, retrieves the detailed information by calling an internal database or an external information API, and returns it to the terminal.

[1934] Input: Request for more information from the terminal

[1935] Output: JSON data of detailed information

[1936] What happens: The server performs a database query or external API call to get the details and sends them to the device.

[1937] Step 10:

[1938] A UI is generated to display the detailed information acquired by the device and displayed to the user.

[1939] Input: JSON data of detailed information received from the server

[1940] Output: Detailed information displayed to the user

[1941] Specific operation: The device analyzes the received data, generates the components necessary to properly display detailed information on the UI, and displays them on the screen.

[1942] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1943] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1944] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1945] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1946] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1947] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1948] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1949] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1950] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1951] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1952] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1953] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1954] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1955] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1956] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1957] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1958] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific pro...

Claims

1. a means for a user to input symptoms; A generating AI means for analyzing the symptoms; A means for the generating AI means to suggest an appropriate over-the-counter drug based on the analysis result; means for displaying detailed information about the suggested over-the-counter drug; A system including:

2. means for transmitting user symptom information to a server; A means for the server to transfer symptom information to the generating AI means; The system of claim 1 further comprising:

3. A means for acquiring associations between symptoms and over-the-counter drugs from an internal database for the generating AI means to evaluate the suitability of the over-the-counter drugs; means for generating a candidate list of commercially available drugs using the obtained relevance information; The system of claim 1 further comprising:

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