system

Abstract

We provide the system. [Solution] Means of receiving drug information from users, A means of analyzing drug interactions based on input drug information, A means for generating optimal medication suggestions based on analysis results, A means of notifying users of the generated suggestions, A means of updating the database with new drug information and health information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, many people are using multiple drugs and supplements in combination. However, knowledge about their interactions, reduced effects, or adverse effects on health in such combinations is not well-known to the general consumer, and as a result, there is a risk of drug misuse and health damage. Under such circumstances, there is a need for a system that ensures the safety of drug combinations and presents the most effective dosing method so that consumers can use multiple drugs with confidence.

Means for Solving the Problems

[0005] This invention provides a system that receives drug information from users and analyzes drug interactions based on that information. This system has the function of generating optimal dosage suggestions based on the analysis results and notifying the user. Furthermore, by constantly updating the database with new drug and health information, it maintains access to the latest information at all times. This enables users to obtain safe and effective drug dosage methods and realize a system that reduces health risks.

[0006] "User" refers to an individual or organization that inputs medication information and receives medication suggestions through the system.

[0007] "Drug information" refers to data that includes information about the names, dosages, frequency, and timing of administration of over-the-counter drugs, prescription drugs, supplements, etc.

[0008] "Interaction" refers to the enhancement or reduction of effects, or the occurrence of side effects, that may occur when multiple drugs or supplements are used simultaneously.

[0009] "Analysis" refers to the process of evaluating drug interactions and effects by referring to a database based on the entered drug information.

[0010] "Suggestions" refer to information provided to users, such as the optimal medication schedule or alternative options.

[0011] A "database" refers to a collection of information that stores the latest data on drug information, interactions, and health-related topics, making it searchable within the system.

[0012] A "system" refers to an integration of hardware and software that implements a series of processes, including user information input, data analysis, proposal generation, notification, and data updates. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

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

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0034] To implement the present invention, a system is constructed for inputting drug data from users, analyzing the data, generating suggestions, notifying information, and updating data.

[0035] System configuration:

[0036] Terminal: Provides an interface for users to input drug information. Terminals consist of internet-connected devices such as smartphones, tablets, and PCs.

[0037] Server: This is the central component that stores information submitted by users in a database and executes analysis algorithms. The server holds information on multiple drugs and performs analysis based on the latest interaction data obtained from pharmaceutical manufacturers and public institutions.

[0038] Database: Manages drug information, interaction data, and health advice. It is regularly updated and has a structure that allows new information to be immediately reflected throughout the system.

[0039] Detailed program processing:

[0040] 1. The user enters information about medications and supplements they are currently taking via their device. This includes the name of the medication, dosage, frequency of administration, and time of administration.

[0041] 2. After the terminal formats the input information, it sends it to the server after going through an authentication process.

[0042] 3. The server stores the received information in the database as a unique user profile. It also starts referencing and analyzing the latest database based on the received data to check for interactions.

[0043] 4. Based on the analysis results, the server will suggest the optimal medication schedule and alternative drugs to the user. In addition, it will also provide advice on diet and exercise for overall health.

[0044] 5. The server sends the generated proposal to the user's terminal and notifies them.

[0045] 6. The user reviews the suggestions and adjusts their medication plan as needed to help improve their health.

[0046] Specific example:

[0047] For example, suppose a user is taking both a vitamin D supplement and heart medication. The user enters this product information into their device, and the server analyzes it based on an interaction database. As a result, it confirms that vitamin D does not interfere with the effectiveness of the heart medication and suggests continuing with the same schedule. However, it also provides new health advice, notifying the user that sun exposure and exercise can help improve cardiac function.

[0048] This system enables users to take their medication safely and effectively. In this way, the present invention improves the safety of drug use for users and makes it possible to maximize the benefits derived from pharmaceuticals.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] Users use their devices to enter detailed information about over-the-counter medications, prescription drugs, and supplements. This includes drug names, dosages, frequency of administration, and timing of administration.

[0052] Step 2:

[0053] The terminal converts the input information into a specified format and prepares to send it to the server via the communication line. During this process, the accuracy and completeness of the data are checked.

[0054] Step 3:

[0055] The server retrieves drug information received from the terminal and stores it in a secure database. Simultaneously, it creates a unique profile for each user to manage their information.

[0056] Step 4:

[0057] The server references the latest drug database and performs analysis to identify interactions and contraindications related to multiple entered drugs. AI algorithms are used to optimize this process.

[0058] Step 5:

[0059] The server generates optimal dosage suggestions based on the analysis results. These suggestions include adjustments to the timing of administration, recommended alternative medications, and risks of drug interactions. They also include general health advice (e.g., recommendations for exercise and diet).

[0060] Step 6:

[0061] The server generates medication suggestions and related information, sends them to the user's device, and notifies the user. Notifications are sent via push notifications, email, or other methods depending on the user's settings.

[0062] Step 7:

[0063] Users can review suggestions via their devices and incorporate them into their own medication plans. If necessary, they can adjust their schedules and receive advice on daily life.

[0064] Step 8:

[0065] The server regularly updates the drug database and health information, instantly reflecting new data in the user's medication information. This ensures that the information provided is always up-to-date and reliable.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] In modern society, accurately understanding the interactions between multiple medications and supplements taken by individual users and developing safe and effective dosage plans is crucial. However, this is often left to the user's own judgment, leading to the risk of health problems based on misinformation. Furthermore, obtaining appropriate advice on exercise and diet for individual users is difficult. Therefore, there is a need for a system that allows users to easily access accurate information and obtain concrete guidance.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes a device for receiving drug information from the user, a device for analyzing drug interactions based on the input drug information, and a device for generating optimal drug dosage suggestions based on the analysis results. This makes it possible for users to easily create a safe and optimized drug dosage plan and obtain specific advice that helps improve their health.

[0071] "User" refers to an individual who uses the system to input medication information and receives health-related suggestions and advice.

[0072] "Drug information" refers to detailed data that users enter into the system, such as the name of the drug or supplement, dosage, frequency of administration, and time of administration.

[0073] "Device" refers to a hardware or software component designed to perform a specific function.

[0074] "Interaction" refers to the phenomenon where the effects of multiple drugs or supplements change when they are used simultaneously.

[0075] A "generative AI model" refers to artificial intelligence technology that automatically makes specific suggestions or predictions based on data.

[0076] A "database" refers to a system for systematically storing information and making it accessible as needed.

[0077] A "profile" refers to a record that includes all medication and health information associated with a user.

[0078] "Health maintenance advice" refers to guidelines that include suggestions for improving exercise and diet, with the aim of improving the user's health.

[0079] This system is an advanced information processing system that analyzes drug interactions based on drug information entered by the user, generates optimal drug dosage suggestions, and notifies the user. The system is implemented using the following hardware and software components.

[0080] First, users enter information about the medications and supplements they are taking using a device such as a smartphone, tablet, or PC. This input process includes the name of the medication, dosage, frequency of administration, and time of administration. This information is entered via a dedicated application on the device or a web interface.

[0081] Next, the input data is processed by a server to ensure data consistency and security. The server has built-in advanced analysis programs that rapidly analyze drug interactions while referencing the latest interaction databases. Cloud-based databases and high-performance computing servers are used for the analysis.

[0082] Furthermore, the server uses a generative AI model to create a medication schedule and alternative drug suggestions tailored to the user's health condition based on the analysis results. These suggestions are customized based on the user profile and include advice on exercise and diet related to maintaining health.

[0083] The server sends these generated suggestions to the user's terminal, providing immediate notification. The user can then adjust their medication plan based on the information received. This feedback loop allows the system to maintain the refined accuracy of its suggestions.

[0084] Specific example

[0085] For example, suppose a user is taking vitamin D supplements and heart medication. This user enters information about these products into their device. The server analyzes the interaction database and confirms that vitamin D does not interfere with the effectiveness of the heart medication. As a result, it suggests maintaining the original schedule and provides new health advice, notifying the user that sun exposure and exercise can help improve heart function.

[0086] Example of a prompt

[0087] The user enters information about the medications they are currently taking in the following format:

[0088] Drug name: Vitamin D

[0089] Dose: 500 IU

[0090] Frequency of use: Daily

[0091] Dosage time: Morning

[0092] Based on this information, please check for interactions between vitamin D and heart medications and, if necessary, suggest an optimal dosage schedule.

[0093] This system aims to raise users' awareness of drug interactions and support safe and effective medication use.

[0094] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0095] Step 1:

[0096] Users enter information about the medications and supplements they are taking using a dedicated application or web interface on their device. This information includes the name of the medication, dosage, frequency of administration, and time of administration. The entered information is stored on the device as data for subsequent processing.

[0097] Step 2:

[0098] The terminal converts the information entered by the user into a standard format. This format conversion ensures data consistency and facilitates analysis on the server. Next, before sending the converted data to the server, the terminal performs user authentication according to security protocols to confirm that the user has legitimate authority.

[0099] Step 3:

[0100] The server receives data sent from the terminal and stores it in the database as individual user profiles. This storage makes the data available for future reference and analysis. The server also uses the received data to reference the pre-created interaction database and begin analysis.

[0101] Step 4:

[0102] The server analyzes drug interactions based on the latest medical data. This analysis utilizes cloud-based databases and high-performance computing resources. Using generative AI models, the server generates medication suggestions and health advice based on the analysis results. The resulting suggestions are customized to each user's specific health condition.

[0103] Step 5:

[0104] The server sends the generated proposal to the terminal. This transmission uses the latest communication protocols to ensure data security and deliverability. The terminal receives a notification from the server and displays the information so the user can review the proposal.

[0105] Step 6:

[0106] Users review suggestions notified via their devices and revise their medication schedules. Based on these suggestions, users adjust their medication schedules or decide on specific health actions (e.g., exercise or dietary changes). This allows users to utilize health management information and achieve safe and effective medication use.

[0107] (Application Example 1)

[0108] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0109] To ensure the safety of medication use while comprehensively improving users' health, a system is needed that examines interactions between various medications and supplements and provides optimal dosage schedules and health advice. However, existing technologies have challenges in ensuring the timeliness of data and providing appropriate notifications to mobile devices.

[0110] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0111] In this invention, the server includes means for receiving drug information from the user, means for delivering generated suggestions to the user's mobile device, and means for analyzing drug interaction information using a generative model. This enables appropriate health management and safe drug use in response to the user's dynamic state.

[0112] A "user" is an individual or organization that provides drug information and receives suggestions through the system.

[0113] "Means for receiving drug information" refers to a function for collecting data on drugs and supplements entered by users.

[0114] "Means for analyzing interactions" refers to a function that evaluates and analyzes interactions between drugs based on the input drug information.

[0115] "Means for generating medication suggestions" refers to a function that creates the optimal medication schedule or alternatives based on the analysis results.

[0116] "Means of notifying users of proposals" refers to a function that sends generated proposals to the user's device to notify them.

[0117] A "mobile device" refers to a portable electronic device such as a smartphone, which is a device used by users to receive information.

[0118] "Methods for analyzing drug interaction information" refers to functions that utilize generative models to evaluate data on drug interactions and create suggestions regarding drug combinations.

[0119] A "generative model" is a technology of artificial intelligence and machine learning models designed to support the analysis of drug information and the generation of recommendations.

[0120] This invention provides a system for users to take medication safely and effectively. This system operates by having the user input medication information via a mobile device such as a smartphone, and a server analyzes that information to provide optimal dosage suggestions.

[0121] The server is built using programming languages ​​such as Python and uses AI models to analyze drug interaction data received from medical professionals. The server also stores the received drug data in a database management system (e.g., MySQL®) and generates personalized medication suggestions and health advice based on the analysis results. This makes it possible to provide information tailored to the individual needs of each user.

[0122] Users' smartphones can connect to the internet and receive notifications from the server. Upon receiving a notification, users can review the suggested medications through the application and adjust their medication schedule as needed. Furthermore, the server also provides suggestions for improving lifestyle habits that can contribute to health, based on the analysis results.

[0123] As a concrete example, suppose a user registers information about heart medication and vitamin supplements on their smartphone. The server receives this information, uses a generative AI model to analyze whether there are any interactions, and proposes an optimal dosage schedule. The server then notifies the user of the results on their smartphone and also provides advice on exercise and diet in their daily life.

[0124] An example of a prompt message is, "Provide safe usage advice and lifestyle modification suggestions to users taking heart medication and vitamin supplements."

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] Users input information such as the name, dosage, and frequency of their current medications through a smartphone application. The entered medication information is temporarily stored within the application and formatted. The output is a list of the medication information entered by the user.

[0128] Step 2:

[0129] The terminal sends the formatted drug information to the server. The server receives this information, goes through an authentication process, and stores it in its database. To confirm that the entered drug information has been received correctly, the server outputs a message indicating that it has been received.

[0130] Step 3:

[0131] The server retrieves drug information stored in the database and analyzes drug interactions using a generative AI model. The input consists of drug information and existing interaction data. Based on this, it performs calculations to derive new drug interaction information. The output is the analysis result regarding the presence or absence of interactions.

[0132] Step 4:

[0133] The server generates an optimal dosage schedule and health advice based on the analysis results. An example prompt used here is the input: "Provide safe dosage advice and lifestyle improvement suggestions for a user taking heart medication and vitamin supplements." The output is suggested data detailing a specific dosage schedule and advice.

[0134] Step 5:

[0135] The server sends the generated suggestion data to the terminal. The terminal receives this data and notifies the user. The server outputs a confirmation message indicating that the notification was successful. The user receives the notification, reviews the information generated within the system, and takes action.

[0136] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0137] This invention provides a system that offers more effective and personalized healthcare support by considering the user's emotional state in addition to managing and suggesting drug information. This system incorporates an emotion engine that recognizes the user's emotions when inputting drug information and considers this information along with the analysis of the drug data. The system configuration and details of each component are described below.

[0138] System configuration:

[0139] Terminal: The user inputs medication information and provides emotional data. The terminal is equipped with a camera and microphone, and analyzes facial expressions and voice tone to determine the user's emotions.

[0140] Server: Retrieves entered drug information and emotion data and stores it in the database. Emotion data is used to customize the suggested content.

[0141] Emotion Engine: Determines the user's emotions through facial recognition and voice analysis technologies. Measures the user's stress and sense of security and generates appropriate feedback.

[0142] Database: Manages drug information, sentiment data, and interaction information. New drug information is updated to maintain its current state at all times.

[0143] Detailed program processing:

[0144] 1. As the user inputs medication information, the emotion engine analyzes facial expressions and voice tone through the device's camera and microphone to generate emotion data.

[0145] 2. The device formats medication information and emotional data and sends it to the server. The data is stored as an individual user profile.

[0146] 3. Based on the information received by the server, drug interaction analysis is performed. The analysis results are linked to drug dosage recommendations that take into account the user's emotional state.

[0147] 4. The server adjusts the generated suggestions according to the user's emotional state. If the user is feeling stressed, it will include advice that promotes relaxation and reassuring elements.

[0148] 5. The server sends the adjusted proposal to the terminal and notifies the user.

[0149] Specific example:

[0150] For example, suppose a user is taking a new heart medication and vitamin C while feeling anxious. In this case, the device detects the user's anxiety from their facial expressions, and the emotion engine collects that data. The server performs a standard interaction analysis to confirm that vitamin C does not affect the heart medication. However, to alleviate anxiety, the suggestions include simple relaxation exercises and reassuring support messages. Based on these suggestions, the user can take their medication with peace of mind.

[0151] Thus, by taking into account the emotional state of the user, the present invention provides more personalized support and creates an environment in which medications can be used with peace of mind.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] When users input information about over-the-counter medications, prescription drugs, and supplements using their devices, their facial expressions and voice tone are simultaneously recorded through the device's camera and microphone. An emotion engine uses this data to analyze the user's emotional state and determine their stress and sense of security levels.

[0155] Step 2:

[0156] The terminal converts the entered drug information and analyzed emotional data into a predetermined format and sends it to the server via a secure communication channel.

[0157] Step 3:

[0158] The server stores the received drug information in a database, and also stores emotional data in association with the user profile.

[0159] Step 4:

[0160] The server uses drug information to refer to an interaction database and analyzes drug interactions and contraindications. An AI algorithm is used to identify potential risks.

[0161] Step 5:

[0162] The server uses the drug interaction analysis results to generate standard dosage suggestions. These suggestions are then customized as needed based on the received emotional data. For example, if the user is feeling anxious, additional messages or simple relaxation advice may be added to alleviate this anxiety.

[0163] Step 6:

[0164] The server sends the final proposal to the device and notifies the user. The proposal is communicated to the user via push notification or in-app message.

[0165] Step 7:

[0166] Users can review the suggested content on their devices and adjust their medication schedule based on that information. They can also use the feedback function to provide feedback to the system regarding the usefulness of the suggestions.

[0167] Through these steps, the system can provide users with safe and personalized medication recommendations, while also addressing their emotional needs.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] There is a need to provide a more individualized approach to the health problems faced by each user. Conventional systems do not consider the user's emotional state when suggesting medications, potentially leading to users taking medication while experiencing anxiety and stress. Furthermore, the lack of adequate provision of appropriate improvement plans based on health conditions is also a challenge.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for receiving drug information from the user, means for recognizing the user's emotional state and generating emotional data, and means for analyzing interactions based on the input drug information. This makes it possible to provide the user with personalized drug dosage suggestions that take their emotional state into account, thereby reducing the user's anxiety and stress and providing a sense of security regarding their health.

[0173] "Users" refer to individuals who provide drug information and emotional data to the system.

[0174] "Drug information" refers to data related to a drug, such as its name, frequency of administration, and dosage.

[0175] "Interaction" refers to the chemical and physiological effects that can occur when multiple drugs act simultaneously in the body.

[0176] "Emotional state" refers to the user's psychological state, including stress, anxiety, and sense of security.

[0177] "Emotional data" refers to information that quantifies and qualitatively represents the emotional state of users.

[0178] A "database" refers to a collection of information that stores and manages drug information, emotional data, and health information.

[0179] A "specialist" refers to a professional in the medical or chemical field who possesses knowledge of drug interactions and can provide data to the system.

[0180] This invention is a system that provides personalized health management to users. This system mainly consists of a terminal, a server, an emotion engine, and a database.

[0181] 1. Device functions:

[0182] The device is used for inputting user medication information and collecting emotional data. The device has a built-in camera and microphone, which are used to analyze the user's facial expressions and voice. This generates emotional data in real time.

[0183] 2. Server role:

[0184] The server receives medication information and emotional data transmitted from the terminal and stores them in a database. The server also analyzes the emotional data using an emotion engine. Specifically, it uses a generative AI model to generate medication suggestions tailored to the user's emotional state and adjusts the information accordingly.

[0185] 3. Emotional Engine:

[0186] The emotion engine uses facial recognition and voice analysis technologies to determine the user's emotional state. This allows it to provide appropriate feedback to the user and create a personalized healthcare plan.

[0187] 4. Database management:

[0188] The database stores drug information, emotional data, and interaction information, and is regularly updated to ensure it remains up-to-date. This makes it possible to provide users with accurate information.

[0189] Specific example:

[0190] For example, if a user is taking both heart medication and vitamin C, the device analyzes the user's facial expressions and detects anxiety. The emotion engine sends this data to a server, which uses a generative AI model to generate medication suggestions, including relaxation exercises to reduce anxiety. These suggestions are then communicated to the user through the device.

[0191] Example of a prompt:

[0192] "Please explain how this system analyzes emotions and provides feedback when users enter medication information."

[0193] This enables the present invention to provide more effective healthcare support that takes into account the user's emotional state.

[0194] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0195] Step 1:

[0196] The user inputs medication information. The user uses a smartphone or tablet to input the medication name, dosage, frequency of administration, etc. Simultaneously, the device activates its built-in camera and microphone to record the user's facial expressions and voice. Medication information and audio-visual data are acquired as input data.

[0197] Step 2:

[0198] The device uses the acquired audio-visual data to send an analysis request to the emotion engine. The emotion engine applies facial recognition and voice analysis algorithms. It uses the user's image and voice data as input and generates data to measure the user's emotional state (e.g., reassurance, anxiety, stress) as output.

[0199] Step 3:

[0200] The terminal formats drug information and generated emotion data and sends it to the server in JSON format. It uses a combined format of drug information and emotion data as input and generates encrypted data packets as output.

[0201] Step 4:

[0202] The server analyzes the received data and stores it in a database. It takes drug information and emotional state data as input and registers them as user profiles using a database management system. This allows the data to be saved as history and used by other algorithms.

[0203] Step 5:

[0204] The server compares incoming data with existing information in the database to analyze drug interactions. It takes new drug information and existing database information as input, applies an analysis algorithm to identify the presence or absence of interactions, and generates the interaction analysis results as output.

[0205] Step 6:

[0206] The server utilizes a generative AI model to generate personalized medication recommendations based on emotional data. It uses interaction analysis results and emotional state data as input and generates medication recommendations that include customized feedback tailored to the user's emotional state as output.

[0207] Step 7:

[0208] The server generates suggestions and sends them to the terminal, notifying the user. Using the generated suggestion data as input, it produces visual and audio notification formats as output. The user can then take appropriate action.

[0209] (Application Example 2)

[0210] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0211] In recent years, with the increasing use of medications, there has been a growing demand for personalized healthcare for individual users. However, the current system is based solely on general medication information and suggestions, and suffers from a lack of emotional support tailored to the user's emotional state and location. Furthermore, when users are emotionally unstable, extra care is needed, and there is a need to establish a system that provides appropriate support accordingly.

[0212] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0213] In this invention, the server includes means for analyzing the user's emotional state and personalizing information based on that analysis; means for acquiring location information and providing emotional support based on the user's emotional state and location information; and means for providing voice and visual reassurance based on the emotional analysis data. This enables personalized healthcare support that is tailored to the user's emotional state and location information.

[0214] "Users" refer to the individual people who use the system and are the entities that provide drug information and emotional data.

[0215] "Drug information" refers to data concerning a specific drug, including its name, intended use, efficacy, side effects, or method of use.

[0216] "Emotional state" refers to data that indicates the user's psychological state, and is analyzed from facial expressions and voice tone collected through cameras and microphones.

[0217] "Location information" refers to data indicating the user's geographical location, and is obtained using technologies such as GPS.

[0218] "Personalization" refers to providing information that is customized according to the user's individual emotional state and location.

[0219] "Mental support" refers to advice, audio, and visual information that takes into account the user's emotional state and promotes a sense of security and relaxation.

[0220] "Means of providing auditory and visual reassurance" refers to technologies that play music or messages based on the user's emotional state and provide a sense of security through visual effects.

[0221] The system for realizing this invention consists of a terminal, a server, an emotion engine, a database, and the like. The terminal is equipped with a camera and a microphone, and when the user inputs drug information, it analyzes their facial expressions and tone of voice to recognize their emotional state. Specifically, this analysis is performed using emotion recognition software such as EmotionSDK.

[0222] The server acquires drug information and emotional data transmitted from the terminal and analyzes drug interactions based on this information. A dedicated algorithm is used for the analysis, and new drug and health information is constantly updated in the database. Utilizing the information in this database, the server generates personalized suggestions, taking into account the user's emotions and location, and notifies the terminal.

[0223] For example, if a user consumes a caffeinated beverage while feeling anxious, the server recognizes the user's anxious emotions through facial expression analysis. Based on the analysis results, it confirms that caffeine intake will not affect interactions with certain medications and provides mental support, such as recommending relaxing music. At the same time, if the user is in a quiet location, it displays a message on the device that provides a sense of calm and reassurance.

[0224] A concrete example of an input prompt for a generative AI model is the following sentence: "Suggest a relaxation message to provide to a user who is feeling anxious." In this way, a sense of security can be given to the user, promoting the safe use of medication.

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The device uses a camera and microphone to input medication information from the user and analyzes their facial expressions and voice tone during this process. It acquires camera video and audio data as input and uses the EmotionSDK to determine the user's emotional state. It generates emotional data and medication information as output.

[0228] Step 2:

[0229] The terminal formats the generated drug information and emotion data and sends it to the server. The input is the emotion data and drug information obtained in step 1, and the output is the transmission of data to the server. Specifically, the data is transmitted using a network communication protocol.

[0230] Step 3:

[0231] The server analyzes drug interactions based on drug information and emotion data received from the terminal. It retrieves relevant drug data from the database and performs the analysis while cross-referencing it. The input is data sent from the terminal and information from the database, and the output is the interaction analysis results.

[0232] Step 4:

[0233] The server generates personalized suggestions by taking into account interaction analysis results, sentiment data, and user location information. Location information is obtained from GPS data indicating the user's location. The inputs are interaction analysis results, sentiment data, and location information, and the output is a customized suggestion message.

[0234] Step 5:

[0235] The server sends the generated suggestion message to the terminal and notifies the user. The input is the suggestion message obtained in step 4, and the output is the notification to the user. Specifically, a mechanism is used to display the message on the terminal's display.

[0236] Step 6:

[0237] Based on the suggestions received, the user receives medication and psychological support. Input is a notification from the server, and specific actions may include taking medication or listening to relaxation music. Output is the user's sense of security and safe use of medication.

[0238] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0239] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0240] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0244] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0245] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0246] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0247] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0248] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0249] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0250] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0251] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0252] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0253] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0254] To implement the present invention, a system is constructed for inputting drug data from users, analyzing the data, generating suggestions, notifying information, and updating data.

[0255] System configuration:

[0256] Terminal: Provides an interface for users to input drug information. Terminals consist of internet-connected devices such as smartphones, tablets, and PCs.

[0257] Server: This is the central component that stores information submitted by users in a database and executes analysis algorithms. The server holds information on multiple drugs and performs analysis based on the latest interaction data obtained from pharmaceutical manufacturers and public institutions.

[0258] Database: Manages drug information, interaction data, and health advice. It is regularly updated and has a structure that allows new information to be immediately reflected throughout the system.

[0259] Detailed program processing:

[0260] 1. The user enters information about medications and supplements they are currently taking via their device. This includes the name of the medication, dosage, frequency of administration, and time of administration.

[0261] 2. After the terminal formats the input information, it sends it to the server after going through an authentication process.

[0262] 3. The server stores the received information in the database as a unique user profile. It also starts referencing and analyzing the latest database based on the received data to check for interactions.

[0263] 4. Based on the analysis results, the server will suggest the optimal medication schedule and alternative drugs to the user. In addition, it will also provide advice on diet and exercise for overall health.

[0264] 5. The server sends the generated proposal to the user's terminal and notifies them.

[0265] 6. The user reviews the suggestions and adjusts their medication plan as needed to help improve their health.

[0266] Specific example:

[0267] For example, suppose a user is taking both a vitamin D supplement and heart medication. The user enters this product information into their device, and the server analyzes it based on an interaction database. As a result, it confirms that vitamin D does not interfere with the effectiveness of the heart medication and suggests continuing with the same schedule. However, it also provides new health advice, notifying the user that sun exposure and exercise can help improve cardiac function.

[0268] This system enables users to take their medication safely and effectively. In this way, the present invention improves the safety of drug use for users and makes it possible to maximize the benefits derived from pharmaceuticals.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] Users use their devices to enter detailed information about over-the-counter medications, prescription drugs, and supplements. This includes drug names, dosages, frequency of administration, and timing of administration.

[0272] Step 2:

[0273] The terminal converts the input information into a specified format and prepares to send it to the server via the communication line. During this process, the accuracy and completeness of the data are checked.

[0274] Step 3:

[0275] The server retrieves drug information received from the terminal and stores it in a secure database. Simultaneously, it creates a unique profile for each user to manage their information.

[0276] Step 4:

[0277] The server references the latest drug database and performs analysis to identify interactions and contraindications related to multiple entered drugs. AI algorithms are used to optimize this process.

[0278] Step 5:

[0279] The server generates an optimal dosing proposal based on the analysis results. This proposal includes adjustments to dosing times, recommended alternative medications, and risks of combination use. It also includes general health advice (e.g., exercise and diet recommendations).

[0280] Step 6:

[0281] The server transmits the dosing proposal and related information generated to the terminal and notifies the user. The notification depends on the user's settings, such as push notifications or emails.

[0282] Step 7:

[0283] The user checks the proposal through the terminal and reflects it in their dosing plan. Incorporate schedule adjustments and advice for daily life as needed.

[0284] Step 8:

[0285] The server periodically updates the pharmaceutical database and health information and immediately reflects the new data in the user's drug information. This ensures continuous provision of always up-to-date and safe information.

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In modern society, accurately understanding the interactions between multiple medications and supplements taken by individual users and developing safe and effective dosage plans is crucial. However, this is often left to the user's own judgment, leading to the risk of health problems based on misinformation. Furthermore, obtaining appropriate advice on exercise and diet for individual users is difficult. Therefore, there is a need for a system that allows users to easily access accurate information and obtain concrete guidance.

[0289] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] In this invention, the server includes a device for receiving drug information from the user, a device for analyzing drug interactions based on the input drug information, and a device for generating optimal drug dosage suggestions based on the analysis results. This makes it possible for users to easily create a safe and optimized drug dosage plan and obtain specific advice that helps improve their health.

[0291] "User" refers to an individual who uses the system to input medication information and receives health-related suggestions and advice.

[0292] "Drug information" refers to detailed data that users enter into the system, such as the name of the drug or supplement, dosage, frequency of administration, and time of administration.

[0293] "Device" refers to a hardware or software component designed to perform a specific function.

[0294] "Interaction" refers to the phenomenon where the effects of multiple drugs or supplements change when they are used simultaneously.

[0295] A "generative AI model" refers to artificial intelligence technology that automatically makes specific suggestions or predictions based on data.

[0296] A "database" refers to a system for systematically storing information and making it accessible as needed.

[0297] A "profile" refers to a record that includes all medication and health information associated with a user.

[0298] "Health maintenance advice" refers to guidelines that include suggestions for improving exercise and diet, with the aim of improving the user's health.

[0299] This system is an advanced information processing system that analyzes drug interactions based on drug information entered by the user, generates optimal drug dosage suggestions, and notifies the user. The system is implemented using the following hardware and software components.

[0300] First, users enter information about the medications and supplements they are taking using a device such as a smartphone, tablet, or PC. This input process includes the name of the medication, dosage, frequency of administration, and time of administration. This information is entered via a dedicated application on the device or a web interface.

[0301] Next, the input data is processed by a server to ensure data consistency and security. The server has built-in advanced analysis programs that rapidly analyze drug interactions while referencing the latest interaction databases. Cloud-based databases and high-performance computing servers are used for the analysis.

[0302] Furthermore, the server uses a generative AI model to create a medication schedule and alternative drug suggestions tailored to the user's health condition based on the analysis results. These suggestions are customized based on the user profile and include advice on exercise and diet related to maintaining health.

[0303] The server sends these generated proposals to the user's terminal and notifies immediately. The user can adjust their medication plan based on the received information. Through this feedback loop, the system can maintain the refined accuracy of the proposals.

[0304] Specific example

[0305] For example, assume a user is taking vitamin D supplements and heart medicine. This user enters the information of these products into the terminal. The server performs an analysis based on the interaction database and confirms that vitamin D does not interfere with the efficacy of the heart medicine. As a result, it proposes to maintain the original schedule and notifies that sunbathing and exercise are helpful for improving heart function as new health advice.

[0306] Example of prompt sentence

[0307] Please enter the information of the medicine the user is taking in the following format:

[0308] Medicine name: Vitamin D

[0309] Dosage: 500 IU

[0310] Frequency of taking: Daily

[0311] Time of taking: Morning

[0312] Based on this information, please check the interaction between vitamin D and heart medicine and propose the optimal taking schedule if necessary.

[0313] This system enhances the user's awareness of the interaction between medicines and supports safe and effective medicine taking.

[0314] The flow of specific processing in Example 1 will be described using FIG. 11.

[0315] Step 1:

[0316] Users enter information about the medications and supplements they are taking using a dedicated application or web interface on their device. This information includes the name of the medication, dosage, frequency of administration, and time of administration. The entered information is stored on the device as data for subsequent processing.

[0317] Step 2:

[0318] The terminal converts the information entered by the user into a standard format. This format conversion ensures data consistency and facilitates analysis on the server. Next, before sending the converted data to the server, the terminal performs user authentication according to security protocols to confirm that the user has legitimate authority.

[0319] Step 3:

[0320] The server receives data sent from the terminal and stores it in the database as individual user profiles. This storage makes the data available for future reference and analysis. The server also uses the received data to reference the pre-created interaction database and begin analysis.

[0321] Step 4:

[0322] The server analyzes drug interactions based on the latest medical data. This analysis utilizes cloud-based databases and high-performance computing resources. Using generative AI models, the server generates medication suggestions and health advice based on the analysis results. The resulting suggestions are customized to each user's specific health condition.

[0323] Step 5:

[0324] The server sends the generated proposal to the terminal. This transmission uses the latest communication protocols to ensure data security and deliverability. The terminal receives a notification from the server and displays the information so the user can review the proposal.

[0325] Step 6:

[0326] Users review suggestions notified via their devices and revise their medication schedules. Based on these suggestions, users adjust their medication schedules or decide on specific health actions (e.g., exercise or dietary changes). This allows users to utilize health management information and achieve safe and effective medication use.

[0327] (Application Example 1)

[0328] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0329] To ensure the safety of medication use while comprehensively improving users' health, a system is needed that examines interactions between various medications and supplements and provides optimal dosage schedules and health advice. However, existing technologies have challenges in ensuring the timeliness of data and providing appropriate notifications to mobile devices.

[0330] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0331] In this invention, the server includes means for receiving drug information from the user, means for delivering generated suggestions to the user's mobile device, and means for analyzing drug interaction information using a generative model. This enables appropriate health management and safe drug use in response to the user's dynamic state.

[0332] A "user" is an individual or organization that provides drug information and receives suggestions through the system.

[0333] "Means for receiving drug information" refers to a function for collecting data on drugs and supplements entered by users.

[0334] "Means for analyzing interactions" refers to a function that evaluates and analyzes interactions between drugs based on the input drug information.

[0335] "Means for generating medication suggestions" refers to a function that creates the optimal medication schedule or alternatives based on the analysis results.

[0336] "Means of notifying users of proposals" refers to a function that sends generated proposals to the user's device to notify them.

[0337] A "mobile device" refers to a portable electronic device such as a smartphone, which is a device used by users to receive information.

[0338] "Methods for analyzing drug interaction information" refers to functions that utilize generative models to evaluate data on drug interactions and create suggestions regarding drug combinations.

[0339] A "generative model" is a technology of artificial intelligence and machine learning models designed to support the analysis of drug information and the generation of recommendations.

[0340] This invention provides a system for users to take medication safely and effectively. This system operates by having the user input medication information via a mobile device such as a smartphone, and a server analyzes that information to provide optimal dosage suggestions.

[0341] The server is built using programming languages ​​such as Python and uses AI models to analyze drug interaction data received from medical professionals. The server also stores the received drug data in a database management system (e.g., MySQL) and generates personalized medication suggestions and health advice based on the analysis results. This makes it possible to provide information tailored to the individual needs of each user.

[0342] Users' smartphones can connect to the internet and receive notifications from the server. Upon receiving a notification, users can review the suggested medications through the application and adjust their medication schedule as needed. Furthermore, the server also provides suggestions for improving lifestyle habits that can contribute to health, based on the analysis results.

[0343] As a concrete example, suppose a user registers information about heart medication and vitamin supplements on their smartphone. The server receives this information, uses a generative AI model to analyze whether there are any interactions, and proposes an optimal dosage schedule. The server then notifies the user of the results on their smartphone and also provides advice on exercise and diet in their daily life.

[0344] An example of a prompt message is, "Provide safe usage advice and lifestyle modification suggestions to users taking heart medication and vitamin supplements."

[0345] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0346] Step 1:

[0347] Users input information such as the name, dosage, and frequency of their current medications through a smartphone application. The entered medication information is temporarily stored within the application and formatted. The output is a list of the medication information entered by the user.

[0348] Step 2:

[0349] The terminal sends the formatted drug information to the server. The server receives this information, goes through an authentication process, and stores it in its database. To confirm that the entered drug information has been received correctly, the server outputs a message indicating that it has been received.

[0350] Step 3:

[0351] The server retrieves drug information stored in the database and analyzes drug interactions using a generative AI model. The input consists of drug information and existing interaction data. Based on this, it performs calculations to derive new drug interaction information. The output is the analysis result regarding the presence or absence of interactions.

[0352] Step 4:

[0353] The server generates an optimal dosage schedule and health advice based on the analysis results. An example prompt used here is the input: "Provide safe dosage advice and lifestyle improvement suggestions for a user taking heart medication and vitamin supplements." The output is suggested data detailing a specific dosage schedule and advice.

[0354] Step 5:

[0355] The server sends the generated suggestion data to the terminal. The terminal receives this data and notifies the user. The server outputs a confirmation message indicating that the notification was successful. The user receives the notification, reviews the information generated within the system, and takes action.

[0356] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0357] This invention provides a system that offers more effective and personalized healthcare support by considering the user's emotional state in addition to managing and suggesting drug information. This system incorporates an emotion engine that recognizes the user's emotions when inputting drug information and considers this information along with the analysis of the drug data. The system configuration and details of each component are described below.

[0358] System configuration:

[0359] Terminal: The user inputs medication information and provides emotional data. The terminal is equipped with a camera and microphone, and analyzes facial expressions and voice tone to determine the user's emotions.

[0360] Server: Retrieves entered drug information and emotion data and stores it in the database. Emotion data is used to customize the suggested content.

[0361] Emotion Engine: Determines the user's emotions through facial recognition and voice analysis technologies. Measures the user's stress and sense of security and generates appropriate feedback.

[0362] Database: Manages drug information, sentiment data, and interaction information. New drug information is updated to maintain its current state at all times.

[0363] Detailed program processing:

[0364] 1. As the user inputs medication information, the emotion engine analyzes facial expressions and voice tone through the device's camera and microphone to generate emotion data.

[0365] 2. The device formats medication information and emotional data and sends it to the server. The data is stored as an individual user profile.

[0366] 3. Based on the information received by the server, drug interaction analysis is performed. The analysis results are linked to drug dosage recommendations that take into account the user's emotional state.

[0367] 4. The server adjusts the generated suggestions according to the user's emotional state. If the user is feeling stressed, it will include advice that promotes relaxation and reassuring elements.

[0368] 5. The server sends the adjusted proposal to the terminal and notifies the user.

[0369] Specific example:

[0370] For example, suppose a user is taking a new heart medication and vitamin C while feeling anxious. In this case, the device detects the user's anxiety from their facial expressions, and the emotion engine collects that data. The server performs a standard interaction analysis to confirm that vitamin C does not affect the heart medication. However, to alleviate anxiety, the suggestions include simple relaxation exercises and reassuring support messages. Based on these suggestions, the user can take their medication with peace of mind.

[0371] Thus, by taking into account the emotional state of the user, the present invention provides more personalized support and creates an environment in which medications can be used with peace of mind.

[0372] The following describes the processing flow.

[0373] Step 1:

[0374] When users input information about over-the-counter medications, prescription drugs, and supplements using their devices, their facial expressions and voice tone are simultaneously recorded through the device's camera and microphone. An emotion engine uses this data to analyze the user's emotional state and determine their stress and sense of security levels.

[0375] Step 2:

[0376] The terminal converts the entered drug information and analyzed emotional data into a predetermined format and sends it to the server via a secure communication channel.

[0377] Step 3:

[0378] The server stores the received drug information in a database, and also stores emotional data in association with the user profile.

[0379] Step 4:

[0380] The server uses drug information to refer to an interaction database and analyzes drug interactions and contraindications. An AI algorithm is used to identify potential risks.

[0381] Step 5:

[0382] The server uses the drug interaction analysis results to generate standard dosage suggestions. These suggestions are then customized as needed based on the received emotional data. For example, if the user is feeling anxious, additional messages or simple relaxation advice may be added to alleviate this anxiety.

[0383] Step 6:

[0384] The server sends the final proposal to the device and notifies the user. The proposal is communicated to the user via push notification or in-app message.

[0385] Step 7:

[0386] Users can review the suggested content on their devices and adjust their medication schedule based on that information. They can also use the feedback function to provide feedback to the system regarding the usefulness of the suggestions.

[0387] Through these steps, the system can provide users with safe and personalized medication recommendations, while also addressing their emotional needs.

[0388] (Example 2)

[0389] Next, we will describe Example 2. 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".

[0390] There is a need to provide a more individualized approach to the health problems faced by each user. Conventional systems do not consider the user's emotional state when suggesting medications, potentially leading to users taking medication while experiencing anxiety and stress. Furthermore, the lack of adequate provision of appropriate improvement plans based on health conditions is also a challenge.

[0391] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0392] In this invention, the server includes means for receiving drug information from the user, means for recognizing the user's emotional state and generating emotional data, and means for analyzing interactions based on the input drug information. This makes it possible to provide the user with personalized drug dosage suggestions that take their emotional state into account, thereby reducing the user's anxiety and stress and providing a sense of security regarding their health.

[0393] "Users" refer to individuals who provide drug information and emotional data to the system.

[0394] "Drug information" refers to data related to a drug, such as its name, frequency of administration, and dosage.

[0395] "Interaction" refers to the chemical and physiological effects that can occur when multiple drugs act simultaneously in the body.

[0396] "Emotional state" refers to the user's psychological state, including stress, anxiety, and sense of security.

[0397] "Emotional data" refers to information that quantifies and qualitatively represents the emotional state of users.

[0398] A "database" refers to a collection of information that stores and manages drug information, emotional data, and health information.

[0399] A "specialist" refers to a professional in the medical or chemical field who possesses knowledge of drug interactions and can provide data to the system.

[0400] This invention is a system that provides personalized health management to users. This system mainly consists of a terminal, a server, an emotion engine, and a database.

[0401] 1. Device functions:

[0402] The device is used for inputting user medication information and collecting emotional data. The device has a built-in camera and microphone, which are used to analyze the user's facial expressions and voice. This generates emotional data in real time.

[0403] 2. Server role:

[0404] The server receives medication information and emotional data transmitted from the terminal and stores them in a database. The server also analyzes the emotional data using an emotion engine. Specifically, it uses a generative AI model to generate medication suggestions tailored to the user's emotional state and adjusts the information accordingly.

[0405] 3. Emotional Engine:

[0406] The emotion engine uses facial recognition and voice analysis technologies to determine the user's emotional state. This allows it to provide appropriate feedback to the user and create a personalized healthcare plan.

[0407] 4. Database management:

[0408] The database stores drug information, emotional data, and interaction information, and is regularly updated to ensure it remains up-to-date. This makes it possible to provide users with accurate information.

[0409] Specific example:

[0410] For example, if a user is taking both heart medication and vitamin C, the device analyzes the user's facial expressions and detects anxiety. The emotion engine sends this data to a server, which uses a generative AI model to generate medication suggestions, including relaxation exercises to reduce anxiety. These suggestions are then communicated to the user through the device.

[0411] Example of a prompt:

[0412] "Please explain how this system analyzes emotions and provides feedback when users enter medication information."

[0413] This enables the present invention to provide more effective healthcare support that takes into account the user's emotional state.

[0414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0415] Step 1:

[0416] The user inputs medication information. The user uses a smartphone or tablet to input the medication name, dosage, frequency of administration, etc. Simultaneously, the device activates its built-in camera and microphone to record the user's facial expressions and voice. Medication information and audio-visual data are acquired as input data.

[0417] Step 2:

[0418] The device uses the acquired audio-visual data to send an analysis request to the emotion engine. The emotion engine applies facial recognition and voice analysis algorithms. It uses the user's image and voice data as input and generates data to measure the user's emotional state (e.g., reassurance, anxiety, stress) as output.

[0419] Step 3:

[0420] The terminal formats drug information and generated emotion data and sends it to the server in JSON format. It uses a combined format of drug information and emotion data as input and generates encrypted data packets as output.

[0421] Step 4:

[0422] The server analyzes the received data and stores it in a database. It takes drug information and emotional state data as input and registers them as user profiles using a database management system. This allows the data to be saved as history and used by other algorithms.

[0423] Step 5:

[0424] The server compares incoming data with existing information in the database to analyze drug interactions. It takes new drug information and existing database information as input, applies an analysis algorithm to identify the presence or absence of interactions, and generates the interaction analysis results as output.

[0425] Step 6:

[0426] The server utilizes a generative AI model to generate personalized medication recommendations based on emotional data. It uses interaction analysis results and emotional state data as input and generates medication recommendations that include customized feedback tailored to the user's emotional state as output.

[0427] Step 7:

[0428] The server generates suggestions and sends them to the terminal, notifying the user. Using the generated suggestion data as input, it produces visual and audio notification formats as output. The user can then take appropriate action.

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0431] In recent years, with the increasing use of medications, there has been a growing demand for personalized healthcare for individual users. However, the current system is based solely on general medication information and suggestions, and suffers from a lack of emotional support tailored to the user's emotional state and location. Furthermore, when users are emotionally unstable, extra care is needed, and there is a need to establish a system that provides appropriate support accordingly.

[0432] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0433] In this invention, the server includes means for analyzing the user's emotional state and personalizing information based on that analysis; means for acquiring location information and providing emotional support based on the user's emotional state and location information; and means for providing voice and visual reassurance based on the emotional analysis data. This enables personalized healthcare support that is tailored to the user's emotional state and location information.

[0434] "Users" refer to the individual people who use the system and are the entities that provide drug information and emotional data.

[0435] "Drug information" refers to data concerning a specific drug, including its name, intended use, efficacy, side effects, or method of use.

[0436] "Emotional state" refers to data that indicates the user's psychological state, and is analyzed from facial expressions and voice tone collected through cameras and microphones.

[0437] "Location information" refers to data indicating the user's geographical location, and is obtained using technologies such as GPS.

[0438] "Personalization" refers to providing information that is customized according to the user's individual emotional state and location.

[0439] "Mental support" refers to advice, audio, and visual information that takes into account the user's emotional state and promotes a sense of security and relaxation.

[0440] "Means of providing auditory and visual reassurance" refers to technologies that play music or messages based on the user's emotional state and provide a sense of security through visual effects.

[0441] The system for realizing this invention consists of a terminal, a server, an emotion engine, a database, and the like. The terminal is equipped with a camera and a microphone, and when the user inputs drug information, it analyzes their facial expressions and tone of voice to recognize their emotional state. Specifically, this analysis is performed using emotion recognition software such as EmotionSDK.

[0442] The server acquires drug information and emotional data transmitted from the terminal and analyzes drug interactions based on this information. A dedicated algorithm is used for the analysis, and new drug and health information is constantly updated in the database. Utilizing the information in this database, the server generates personalized suggestions, taking into account the user's emotions and location, and notifies the terminal.

[0443] For example, if a user consumes a caffeinated beverage while feeling anxious, the server recognizes the user's anxious emotions through facial expression analysis. Based on the analysis results, it confirms that caffeine intake will not affect interactions with certain medications and provides mental support, such as recommending relaxing music. At the same time, if the user is in a quiet location, it displays a message on the device that provides a sense of calm and reassurance.

[0444] A concrete example of an input prompt for a generative AI model is the following sentence: "Suggest a relaxation message to provide to a user who is feeling anxious." In this way, a sense of security can be given to the user, promoting the safe use of medication.

[0445] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0446] Step 1:

[0447] The device uses a camera and microphone to input medication information from the user and analyzes their facial expressions and voice tone during this process. It acquires camera video and audio data as input and uses the EmotionSDK to determine the user's emotional state. It generates emotional data and medication information as output.

[0448] Step 2:

[0449] The terminal formats the generated drug information and emotion data and sends it to the server. The input is the emotion data and drug information obtained in step 1, and the output is the transmission of data to the server. Specifically, the data is transmitted using a network communication protocol.

[0450] Step 3:

[0451] The server analyzes drug interactions based on drug information and emotion data received from the terminal. It retrieves relevant drug data from the database and performs the analysis while cross-referencing it. The input is data sent from the terminal and information from the database, and the output is the interaction analysis results.

[0452] Step 4:

[0453] The server generates personalized suggestions by taking into account interaction analysis results, sentiment data, and user location information. Location information is obtained from GPS data indicating the user's location. The inputs are interaction analysis results, sentiment data, and location information, and the output is a customized suggestion message.

[0454] Step 5:

[0455] The server sends the generated suggestion message to the terminal and notifies the user. The input is the suggestion message obtained in step 4, and the output is the notification to the user. Specifically, a mechanism is used to display the message on the terminal's display.

[0456] Step 6:

[0457] Based on the suggestions received, the user receives medication and psychological support. Input is a notification from the server, and specific actions may include taking medication or listening to relaxation music. Output is the user's sense of security and safe use of medication.

[0458] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0459] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0460] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0461] [Third Embodiment]

[0462] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0463] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0464] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0465] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0466] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0467] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0468] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0469] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0470] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0471] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0472] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0473] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0474] To implement the present invention, a system is constructed for inputting drug data from users, analyzing the data, generating suggestions, notifying information, and updating data.

[0475] System configuration:

[0476] Terminal: Provides an interface for users to input drug information. Terminals consist of internet-connected devices such as smartphones, tablets, and PCs.

[0477] Server: This is the central component that stores information submitted by users in a database and executes analysis algorithms. The server holds information on multiple drugs and performs analysis based on the latest interaction data obtained from pharmaceutical manufacturers and public institutions.

[0478] Database: Manages drug information, interaction data, and health advice. It is regularly updated and has a structure that allows new information to be immediately reflected throughout the system.

[0479] Detailed program processing:

[0480] 1. The user enters information about medications and supplements they are currently taking via their device. This includes the name of the medication, dosage, frequency of administration, and time of administration.

[0481] 2. After the terminal formats the input information, it sends it to the server after going through an authentication process.

[0482] 3. The server stores the received information in the database as a unique user profile. It also starts referencing and analyzing the latest database based on the received data to check for interactions.

[0483] 4. Based on the analysis results, the server will suggest the optimal medication schedule and alternative drugs to the user. In addition, it will also provide advice on diet and exercise for overall health.

[0484] 5. The server sends the generated proposal to the user's terminal and notifies them.

[0485] 6. The user reviews the suggestions and adjusts their medication plan as needed to help improve their health.

[0486] Specific example:

[0487] For example, suppose a user is taking both a vitamin D supplement and heart medication. The user enters this product information into their device, and the server analyzes it based on an interaction database. As a result, it confirms that vitamin D does not interfere with the effectiveness of the heart medication and suggests continuing with the same schedule. However, it also provides new health advice, notifying the user that sun exposure and exercise can help improve cardiac function.

[0488] This system enables users to take their medication safely and effectively. In this way, the present invention improves the safety of drug use for users and makes it possible to maximize the benefits derived from pharmaceuticals.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] Users use their devices to enter detailed information about over-the-counter medications, prescription drugs, and supplements. This includes drug names, dosages, frequency of administration, and timing of administration.

[0492] Step 2:

[0493] The terminal converts the input information into a specified format and prepares to send it to the server via the communication line. During this process, the accuracy and completeness of the data are checked.

[0494] Step 3:

[0495] The server retrieves drug information received from the terminal and stores it in a secure database. Simultaneously, it creates a unique profile for each user to manage their information.

[0496] Step 4:

[0497] The server references the latest drug database and performs analysis to identify interactions and contraindications related to multiple entered drugs. AI algorithms are used to optimize this process.

[0498] Step 5:

[0499] The server generates optimal dosage suggestions based on the analysis results. These suggestions include adjustments to the timing of administration, recommended alternative medications, and risks of drug interactions. They also include general health advice (e.g., recommendations for exercise and diet).

[0500] Step 6:

[0501] The server generates medication suggestions and related information, sends them to the user's device, and notifies the user. Notifications are sent via push notifications, email, or other methods depending on the user's settings.

[0502] Step 7:

[0503] Users can review suggestions via their devices and incorporate them into their own medication plans. If necessary, they can adjust their schedules and receive advice on daily life.

[0504] Step 8:

[0505] The server regularly updates the drug database and health information, instantly reflecting new data in the user's medication information. This ensures that the information provided is always up-to-date and reliable.

[0506] (Example 1)

[0507] Next, we will describe Example 1. 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."

[0508] In modern society, accurately understanding the interactions between multiple medications and supplements taken by individual users and developing safe and effective dosage plans is crucial. However, this is often left to the user's own judgment, leading to the risk of health problems based on misinformation. Furthermore, obtaining appropriate advice on exercise and diet for individual users is difficult. Therefore, there is a need for a system that allows users to easily access accurate information and obtain concrete guidance.

[0509] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0510] In this invention, the server includes a device for receiving drug information from the user, a device for analyzing drug interactions based on the input drug information, and a device for generating optimal drug dosage suggestions based on the analysis results. This makes it possible for users to easily create a safe and optimized drug dosage plan and obtain specific advice that helps improve their health.

[0511] "User" refers to an individual who uses the system to input medication information and receives health-related suggestions and advice.

[0512] "Drug information" refers to detailed data that users enter into the system, such as the name of the drug or supplement, dosage, frequency of administration, and time of administration.

[0513] "Device" refers to a hardware or software component designed to perform a specific function.

[0514] "Interaction" refers to the phenomenon where the effects of multiple drugs or supplements change when they are used simultaneously.

[0515] A "generative AI model" refers to artificial intelligence technology that automatically makes specific suggestions or predictions based on data.

[0516] A "database" refers to a system for systematically storing information and making it accessible as needed.

[0517] A "profile" refers to a record that includes all medication and health information associated with a user.

[0518] "Health maintenance advice" refers to guidelines that include suggestions for improving exercise and diet, with the aim of improving the user's health.

[0519] This system is an advanced information processing system that analyzes drug interactions based on drug information entered by the user, generates optimal drug dosage suggestions, and notifies the user. The system is implemented using the following hardware and software components.

[0520] First, users enter information about the medications and supplements they are taking using a device such as a smartphone, tablet, or PC. This input process includes the name of the medication, dosage, frequency of administration, and time of administration. This information is entered via a dedicated application on the device or a web interface.

[0521] Next, the input data is processed by a server to ensure data consistency and security. The server incorporates advanced analysis programs that rapidly analyze drug interactions while referencing the latest interaction databases. Cloud-based databases and high-performance computing servers are used for the analysis.

[0522] Furthermore, the server uses a generative AI model to create a medication schedule and alternative drug suggestions tailored to the user's health condition based on the analysis results. These suggestions are customized based on the user profile and include advice on exercise and diet related to maintaining health.

[0523] The server sends these generated suggestions to the user's terminal, providing immediate notification. The user can then adjust their medication plan based on the information received. This feedback loop allows the system to maintain the refined accuracy of its suggestions.

[0524] Specific example

[0525] For example, suppose a user is taking vitamin D supplements and heart medication. This user enters information about these products into their device. The server analyzes the interaction database and confirms that vitamin D does not interfere with the effectiveness of the heart medication. As a result, it suggests maintaining the original schedule and provides new health advice, notifying the user that sun exposure and exercise can help improve heart function.

[0526] Example of a prompt

[0527] The user enters information about medications they are currently taking in the following format:

[0528] Drug name: Vitamin D

[0529] Dose: 500 IU

[0530] Frequency of use: Daily

[0531] Dosage time: Morning

[0532] Based on this information, please check for interactions between vitamin D and heart medications and, if necessary, suggest an optimal dosage schedule.

[0533] This system aims to raise users' awareness of drug interactions and support safe and effective medication use.

[0534] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0535] Step 1:

[0536] Users enter information about the medications and supplements they are taking using a dedicated application or web interface on their device. This information includes the name of the medication, dosage, frequency of administration, and time of administration. The entered information is stored on the device as data for subsequent processing.

[0537] Step 2:

[0538] The terminal converts the information entered by the user into a standard format. This format conversion ensures data consistency and facilitates analysis on the server. Next, before sending the converted data to the server, the terminal performs user authentication according to security protocols to confirm that the user has legitimate authority.

[0539] Step 3:

[0540] The server receives data sent from the terminal and stores it in the database as individual user profiles. This storage makes the data available for future reference and analysis. The server also uses the received data to reference the pre-created interaction database and begin analysis.

[0541] Step 4:

[0542] The server analyzes drug interactions based on the latest medical data. This analysis utilizes cloud-based databases and high-performance computing resources. Using generative AI models, the server generates medication suggestions and health advice based on the analysis results. The resulting suggestions are customized to each user's specific health condition.

[0543] Step 5:

[0544] The server sends the generated proposal to the terminal. This transmission uses the latest communication protocols to ensure data security and deliverability. The terminal receives a notification from the server and displays the information so the user can review the proposal.

[0545] Step 6:

[0546] Users review suggestions notified via their devices and revise their medication schedules. Based on these suggestions, users adjust their medication schedules or decide on specific health actions (e.g., exercise or dietary changes). This allows users to utilize health management information and achieve safe and effective medication use.

[0547] (Application Example 1)

[0548] Next, we will explain Application Example 1. In the following explanation, 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."

[0549] To ensure the safety of medication use while comprehensively improving users' health, a system is needed that examines interactions between various medications and supplements and provides optimal dosage schedules and health advice. However, existing technologies have challenges in ensuring the timeliness of data and providing appropriate notifications to mobile devices.

[0550] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0551] In this invention, the server includes means for receiving drug information from the user, means for delivering generated suggestions to the user's mobile device, and means for analyzing drug interaction information using a generative model. This enables appropriate health management and safe drug use in response to the user's dynamic state.

[0552] A "user" is an individual or organization that provides drug information and receives suggestions through the system.

[0553] "Means for receiving drug information" refers to a function for collecting data on drugs and supplements entered by users.

[0554] "Means for analyzing interactions" refers to a function that evaluates and analyzes interactions between drugs based on the input drug information.

[0555] "Means for generating medication suggestions" refers to a function that creates the optimal medication schedule or alternatives based on the analysis results.

[0556] "Means of notifying users of proposals" refers to a function that sends generated proposals to the user's device to notify them.

[0557] A "mobile device" refers to a portable electronic device such as a smartphone, which is a device used by users to receive information.

[0558] "Methods for analyzing drug interaction information" refers to functions that utilize generative models to evaluate data on drug interactions and create suggestions regarding drug combinations.

[0559] A "generative model" is a technology of artificial intelligence and machine learning models designed to support the analysis of drug information and the generation of recommendations.

[0560] This invention provides a system for users to take medication safely and effectively. This system operates by having the user input medication information via a mobile device such as a smartphone, and a server analyzes that information to provide optimal dosage suggestions.

[0561] The server is built using programming languages ​​such as Python and uses AI models to analyze drug interaction data received from medical professionals. The server also stores the received drug data in a database management system (e.g., MySQL) and generates personalized medication suggestions and health advice based on the analysis results. This makes it possible to provide information tailored to the individual needs of each user.

[0562] Users' smartphones can connect to the internet and receive notifications from the server. Upon receiving a notification, users can review the suggested medications through the application and adjust their medication schedule as needed. Furthermore, the server also provides suggestions for improving lifestyle habits that can contribute to health, based on the analysis results.

[0563] As a concrete example, suppose a user registers information about heart medication and vitamin supplements on their smartphone. The server receives this information, uses a generative AI model to analyze whether there are any interactions, and proposes an optimal dosage schedule. The server then notifies the user of the results on their smartphone and also provides advice on exercise and diet in their daily life.

[0564] An example of a prompt message is, "Provide safe usage advice and lifestyle modification suggestions to users taking heart medication and vitamin supplements."

[0565] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0566] Step 1:

[0567] Users input information such as the name, dosage, and frequency of their current medications through a smartphone application. The entered medication information is temporarily stored within the application and formatted. The output is a list of the medication information entered by the user.

[0568] Step 2:

[0569] The terminal sends the formatted drug information to the server. The server receives this information, goes through an authentication process, and stores it in its database. To confirm that the entered drug information has been received correctly, the server outputs a message indicating that it has been received.

[0570] Step 3:

[0571] The server retrieves drug information stored in the database and analyzes drug interactions using a generative AI model. The input consists of drug information and existing interaction data. Based on this, it performs calculations to derive new drug interaction information. The output is the analysis result regarding the presence or absence of interactions.

[0572] Step 4:

[0573] The server generates an optimal dosage schedule and health advice based on the analysis results. An example prompt used here is the input: "Provide safe dosage advice and lifestyle improvement suggestions for a user taking heart medication and vitamin supplements." The output is suggested data detailing a specific dosage schedule and advice.

[0574] Step 5:

[0575] The server sends the generated suggestion data to the terminal. The terminal receives this data and notifies the user. The server outputs a confirmation message indicating that the notification was successful. The user receives the notification, reviews the information generated within the system, and takes action.

[0576] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0577] This invention provides a system that offers more effective and personalized healthcare support by considering the user's emotional state in addition to managing and suggesting drug information. This system incorporates an emotion engine that recognizes the user's emotions when inputting drug information and considers this information along with the analysis of the drug data. The system configuration and details of each component are described below.

[0578] System configuration:

[0579] Terminal: The user inputs medication information and provides emotional data. The terminal is equipped with a camera and microphone, and analyzes facial expressions and voice tone to determine the user's emotions.

[0580] Server: Retrieves entered drug information and emotion data and stores it in the database. Emotion data is used to customize the suggested content.

[0581] Emotion Engine: Determines the user's emotions through facial recognition and voice analysis technologies. Measures the user's stress and sense of security and generates appropriate feedback.

[0582] Database: Manages drug information, sentiment data, and interaction information. New drug information is updated to maintain its current state at all times.

[0583] Detailed program processing:

[0584] 1. As the user inputs medication information, the emotion engine analyzes facial expressions and voice tone through the device's camera and microphone to generate emotion data.

[0585] 2. The device formats medication information and emotional data and sends it to the server. The data is stored as an individual user profile.

[0586] 3. Based on the information received by the server, drug interaction analysis is performed. The analysis results are linked to drug dosage recommendations that take into account the user's emotional state.

[0587] 4. The server adjusts the generated suggestions according to the user's emotional state. If the user is feeling stressed, it will include advice that promotes relaxation and reassuring elements.

[0588] 5. The server sends the adjusted proposal to the terminal and notifies the user.

[0589] Specific example:

[0590] For example, suppose a user is taking a new heart medication and vitamin C while feeling anxious. In this case, the device detects the user's anxiety from their facial expressions, and the emotion engine collects that data. The server performs a standard interaction analysis to confirm that vitamin C does not affect the heart medication. However, to alleviate anxiety, the suggestions include simple relaxation exercises and reassuring support messages. Based on these suggestions, the user can take their medication with peace of mind.

[0591] Thus, by taking into account the emotional state of the user, the present invention provides more personalized support and creates an environment in which medications can be used with peace of mind.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] When users input information about over-the-counter medications, prescription drugs, and supplements using their devices, their facial expressions and voice tone are simultaneously recorded through the device's camera and microphone. An emotion engine uses this data to analyze the user's emotional state and determine their stress and sense of security levels.

[0595] Step 2:

[0596] The terminal converts the entered drug information and analyzed emotional data into a predetermined format and sends it to the server via a secure communication channel.

[0597] Step 3:

[0598] The server stores the received drug information in a database, and also stores emotional data in association with the user profile.

[0599] Step 4:

[0600] The server uses drug information to refer to an interaction database and analyzes drug interactions and contraindications. An AI algorithm is used to identify potential risks.

[0601] Step 5:

[0602] The server uses the drug interaction analysis results to generate standard dosage suggestions. These suggestions are then customized as needed based on the received emotional data. For example, if the user is feeling anxious, additional messages or simple relaxation advice may be added to alleviate this anxiety.

[0603] Step 6:

[0604] The server sends the final proposal to the device and notifies the user. The proposal is communicated to the user via push notification or in-app message.

[0605] Step 7:

[0606] Users can review the suggested content on their devices and adjust their medication schedule based on that information. They can also use the feedback function to provide feedback to the system regarding the usefulness of the suggestions.

[0607] Through these steps, the system can provide users with safe and personalized medication recommendations, while also addressing their emotional needs.

[0608] (Example 2)

[0609] Next, we will describe Example 2. 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."

[0610] There is a need to provide a more individualized approach to the health problems faced by each user. Conventional systems do not consider the user's emotional state when suggesting medications, potentially leading to users taking medication while experiencing anxiety and stress. Furthermore, the lack of adequate provision of appropriate improvement plans based on health conditions is also a challenge.

[0611] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0612] In this invention, the server includes means for receiving drug information from the user, means for recognizing the user's emotional state and generating emotional data, and means for analyzing interactions based on the input drug information. This makes it possible to provide the user with personalized drug dosage suggestions that take their emotional state into account, thereby reducing the user's anxiety and stress and providing a sense of security regarding their health.

[0613] "Users" refer to individuals who provide drug information and emotional data to the system.

[0614] "Drug information" refers to data related to a drug, such as its name, frequency of administration, and dosage.

[0615] "Interaction" refers to the chemical and physiological effects that can occur when multiple drugs act simultaneously in the body.

[0616] "Emotional state" refers to the user's psychological state, including stress, anxiety, and sense of security.

[0617] "Emotional data" refers to information that quantifies and qualitatively represents the emotional state of users.

[0618] A "database" refers to a collection of information that stores and manages drug information, emotional data, and health information.

[0619] A "specialist" refers to a professional in the medical or chemical field who possesses knowledge of drug interactions and can provide data to the system.

[0620] This invention is a system that provides personalized health management to users. This system mainly consists of a terminal, a server, an emotion engine, and a database.

[0621] 1. Device functions:

[0622] The device is used for inputting user medication information and collecting emotional data. The device has a built-in camera and microphone, which are used to analyze the user's facial expressions and voice. This generates emotional data in real time.

[0623] 2. Server role:

[0624] The server receives medication information and emotional data transmitted from the terminal and stores them in a database. The server also analyzes the emotional data using an emotion engine. Specifically, it uses a generative AI model to generate medication suggestions tailored to the user's emotional state and adjusts the information accordingly.

[0625] 3. Emotional Engine:

[0626] The emotion engine uses facial recognition and voice analysis technologies to determine the user's emotional state. This allows it to provide appropriate feedback to the user and create a personalized healthcare plan.

[0627] 4. Database management:

[0628] The database stores drug information, emotional data, and interaction information, and is regularly updated to ensure it remains up-to-date. This makes it possible to provide users with accurate information.

[0629] Specific example:

[0630] For example, if a user is taking both heart medication and vitamin C, the device analyzes the user's facial expressions and detects anxiety. The emotion engine sends this data to a server, which uses a generative AI model to generate medication suggestions, including relaxation exercises to reduce anxiety. These suggestions are then communicated to the user through the device.

[0631] Example of a prompt:

[0632] "Please explain how this system analyzes emotions and provides feedback when users enter medication information."

[0633] This enables the present invention to provide more effective healthcare support that takes into account the user's emotional state.

[0634] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0635] Step 1:

[0636] The user inputs medication information. The user uses a smartphone or tablet to input the medication name, dosage, frequency of administration, etc. Simultaneously, the device activates its built-in camera and microphone to record the user's facial expressions and voice. Medication information and audio-visual data are acquired as input data.

[0637] Step 2:

[0638] The device uses the acquired audio-visual data to send an analysis request to the emotion engine. The emotion engine applies facial recognition and voice analysis algorithms. It uses the user's image and voice data as input and generates data to measure the user's emotional state (e.g., reassurance, anxiety, stress) as output.

[0639] Step 3:

[0640] The terminal formats drug information and generated emotion data and sends it to the server in JSON format. It uses a combined format of drug information and emotion data as input and generates encrypted data packets as output.

[0641] Step 4:

[0642] The server analyzes the received data and stores it in a database. It takes drug information and emotional state data as input and registers them as user profiles using a database management system. This allows the data to be saved as history and used by other algorithms.

[0643] Step 5:

[0644] The server compares incoming data with existing information in the database to analyze drug interactions. It takes new drug information and existing database information as input, applies an analysis algorithm to identify the presence or absence of interactions, and generates the interaction analysis results as output.

[0645] Step 6:

[0646] The server utilizes a generative AI model to generate personalized medication recommendations based on emotional data. It uses interaction analysis results and emotional state data as input and generates medication recommendations that include customized feedback tailored to the user's emotional state as output.

[0647] Step 7:

[0648] The server generates suggestions and sends them to the terminal, notifying the user. Using the generated suggestion data as input, it produces visual and audio notification formats as output. The user can then take appropriate action.

[0649] (Application Example 2)

[0650] Next, we will explain application example 2. In the following explanation, 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."

[0651] In recent years, with the increasing use of medications, there has been a growing demand for personalized healthcare for individual users. However, the current system is based solely on general medication information and suggestions, and suffers from a lack of emotional support tailored to the user's emotional state and location. Furthermore, when users are emotionally unstable, extra care is needed, and there is a need to establish a system that provides appropriate support accordingly.

[0652] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0653] In this invention, the server includes means for analyzing the user's emotional state and personalizing information based on that analysis; means for acquiring location information and providing emotional support based on the user's emotional state and location information; and means for providing voice and visual reassurance based on the emotional analysis data. This enables personalized healthcare support that is tailored to the user's emotional state and location information.

[0654] "Users" refer to the individual people who use the system and are the entities that provide drug information and emotional data.

[0655] "Drug information" refers to data concerning a specific drug, including its name, intended use, efficacy, side effects, or method of use.

[0656] "Emotional state" refers to data that indicates the user's psychological state, and is analyzed from facial expressions and voice tone collected through cameras and microphones.

[0657] "Location information" refers to data indicating the user's geographical location, and is obtained using technologies such as GPS.

[0658] "Personalization" refers to providing information that is customized according to the user's individual emotional state and location.

[0659] "Mental support" refers to advice, audio, and visual information that takes into account the user's emotional state and promotes a sense of security and relaxation.

[0660] "Means of providing auditory and visual reassurance" refers to technologies that play music or messages based on the user's emotional state and provide a sense of security through visual effects.

[0661] The system for realizing this invention consists of a terminal, a server, an emotion engine, a database, and the like. The terminal is equipped with a camera and a microphone, and when the user inputs drug information, it analyzes their facial expressions and tone of voice to recognize their emotional state. Specifically, this analysis is performed using emotion recognition software such as EmotionSDK.

[0662] The server acquires drug information and emotional data transmitted from the terminal and analyzes drug interactions based on this information. A dedicated algorithm is used for the analysis, and new drug and health information is constantly updated in the database. Utilizing the information in this database, the server generates personalized suggestions, taking into account the user's emotions and location, and notifies the terminal.

[0663] For example, if a user consumes a caffeinated beverage while feeling anxious, the server recognizes the user's anxious emotions through facial expression analysis. Based on the analysis results, it confirms that caffeine intake will not affect interactions with certain medications and provides mental support, such as recommending relaxing music. At the same time, if the user is in a quiet location, it displays a message on the device that provides a sense of calm and reassurance.

[0664] A concrete example of an input prompt for a generative AI model is the following sentence: "Suggest a relaxation message to provide to a user who is feeling anxious." In this way, a sense of security can be given to the user, promoting the safe use of medication.

[0665] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0666] Step 1:

[0667] The device uses a camera and microphone to input medication information from the user and analyzes their facial expressions and voice tone during this process. It acquires camera video and audio data as input and uses the EmotionSDK to determine the user's emotional state. It generates emotional data and medication information as output.

[0668] Step 2:

[0669] The terminal formats the generated drug information and emotion data and sends it to the server. The input is the emotion data and drug information obtained in step 1, and the output is the transmission of data to the server. Specifically, the data is transmitted using a network communication protocol.

[0670] Step 3:

[0671] The server analyzes drug interactions based on drug information and emotion data received from the terminal. It retrieves relevant drug data from the database and performs the analysis while cross-referencing it. The input is data sent from the terminal and information from the database, and the output is the interaction analysis results.

[0672] Step 4:

[0673] The server generates personalized suggestions by taking into account interaction analysis results, sentiment data, and user location information. Location information is obtained from GPS data indicating the user's location. The inputs are interaction analysis results, sentiment data, and location information, and the output is a customized suggestion message.

[0674] Step 5:

[0675] The server sends the generated suggestion message to the terminal and notifies the user. The input is the suggestion message obtained in step 4, and the output is the notification to the user. Specifically, a mechanism is used to display the message on the terminal's display.

[0676] Step 6:

[0677] Based on the suggestions received, the user receives medication and psychological support. Input is a notification from the server, and specific actions may include taking medication or listening to relaxation music. Output is the user's sense of security and safe use of medication.

[0678] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0679] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0680] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0681] [Fourth Embodiment]

[0682] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0683] As shown in Figure 7, the 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.

[0684] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0685] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0686] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0687] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0688] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0689] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0690] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0691] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0692] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0693] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0694] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] To implement the present invention, a system is constructed for inputting drug data from users, analyzing the data, generating suggestions, notifying information, and updating data.

[0696] System configuration:

[0697] Terminal: Provides an interface for users to input drug information. Terminals consist of internet-connected devices such as smartphones, tablets, and PCs.

[0698] Server: This is the central component that stores information submitted by users in a database and executes analysis algorithms. The server holds information on multiple drugs and performs analysis based on the latest interaction data obtained from pharmaceutical manufacturers and public institutions.

[0699] Database: Manages drug information, interaction data, and health advice. It is regularly updated and has a structure that allows new information to be immediately reflected throughout the system.

[0700] Detailed program processing:

[0701] 1. The user enters information about medications and supplements they are currently taking via their device. This includes the name of the medication, dosage, frequency of administration, and time of administration.

[0702] 2. After the terminal formats the input information, it sends it to the server after going through an authentication process.

[0703] 3. The server stores the received information in the database as a unique user profile. It also starts referencing and analyzing the latest database based on the received data to check for interactions.

[0704] 4. Based on the analysis results, the server will suggest the optimal medication schedule and alternative drugs to the user. In addition, it will also provide advice on diet and exercise for overall health.

[0705] 5. The server sends the generated proposal to the user's terminal and notifies them.

[0706] 6. The user reviews the suggestions and adjusts their medication plan as needed to help improve their health.

[0707] Specific example:

[0708] For example, suppose a user is taking both a vitamin D supplement and heart medication. The user enters this product information into their device, and the server analyzes it based on an interaction database. As a result, it confirms that vitamin D does not interfere with the effectiveness of the heart medication and suggests continuing with the same schedule. However, it also provides new health advice, notifying the user that sun exposure and exercise can help improve cardiac function.

[0709] This system enables users to take their medication safely and effectively. In this way, the present invention improves the safety of drug use for users and makes it possible to maximize the benefits derived from pharmaceuticals.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] Users use their devices to enter detailed information about over-the-counter medications, prescription drugs, and supplements. This includes drug names, dosages, frequency of administration, and timing of administration.

[0713] Step 2:

[0714] The terminal converts the input information into a specified format and prepares to send it to the server via the communication line. During this process, the accuracy and completeness of the data are checked.

[0715] Step 3:

[0716] The server retrieves drug information received from the terminal and stores it in a secure database. Simultaneously, it creates a unique profile for each user to manage their information.

[0717] Step 4:

[0718] The server references the latest drug database and performs analysis to identify interactions and contraindications related to multiple entered drugs. AI algorithms are used to optimize this process.

[0719] Step 5:

[0720] The server generates optimal dosage suggestions based on the analysis results. These suggestions include adjustments to the timing of administration, recommended alternative medications, and risks of drug interactions. They also include general health advice (e.g., recommendations for exercise and diet).

[0721] Step 6:

[0722] The server generates medication suggestions and related information, sends them to the user's device, and notifies the user. Notifications are sent via push notifications, email, or other methods depending on the user's settings.

[0723] Step 7:

[0724] Users can review suggestions via their devices and incorporate them into their own medication plans. If necessary, they can adjust their schedules and receive advice on daily life.

[0725] Step 8:

[0726] The server regularly updates the drug database and health information, instantly reflecting new data in the user's medication information. This ensures that the information provided is always up-to-date and reliable.

[0727] (Example 1)

[0728] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] In modern society, accurately understanding the interactions between multiple medications and supplements taken by individual users and developing safe and effective dosage plans is crucial. However, this is often left to the user's own judgment, leading to the risk of health problems based on misinformation. Furthermore, obtaining appropriate advice on exercise and diet for individual users is difficult. Therefore, there is a need for a system that allows users to easily access accurate information and obtain concrete guidance.

[0730] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0731] In this invention, the server includes a device for receiving drug information from the user, a device for analyzing drug interactions based on the input drug information, and a device for generating optimal drug dosage suggestions based on the analysis results. This makes it possible for users to easily create a safe and optimized drug dosage plan and obtain specific advice that helps improve their health.

[0732] "User" refers to an individual who uses the system to input medication information and receives health-related suggestions and advice.

[0733] "Drug information" refers to detailed data that users enter into the system, such as the name of the drug or supplement, dosage, frequency of administration, and time of administration.

[0734] "Device" refers to a hardware or software component designed to perform a specific function.

[0735] "Interaction" refers to the phenomenon where the effects of multiple drugs or supplements change when they are used simultaneously.

[0736] A "generative AI model" refers to artificial intelligence technology that automatically makes specific suggestions or predictions based on data.

[0737] A "database" refers to a system for systematically storing information and making it accessible as needed.

[0738] A "profile" refers to a record that includes all medication and health information associated with a user.

[0739] "Health maintenance advice" refers to guidelines that include suggestions for improving exercise and diet, with the aim of improving the user's health.

[0740] This system is an advanced information processing system that analyzes drug interactions based on drug information entered by the user, generates optimal drug dosage suggestions, and notifies the user. The system is implemented using the following hardware and software components.

[0741] First, users enter information about the medications and supplements they are taking using a device such as a smartphone, tablet, or PC. This input process includes the name of the medication, dosage, frequency of administration, and time of administration. This information is entered via a dedicated application on the device or a web interface.

[0742] Next, the input data is processed by a server to ensure data consistency and security. The server has built-in advanced analysis programs that rapidly analyze drug interactions while referencing the latest interaction databases. Cloud-based databases and high-performance computing servers are used for the analysis.

[0743] Furthermore, the server uses a generative AI model to create a medication schedule and alternative drug suggestions tailored to the user's health condition based on the analysis results. These suggestions are customized based on the user profile and include advice on exercise and diet related to maintaining health.

[0744] The server sends these generated suggestions to the user's terminal, providing immediate notification. The user can then adjust their medication plan based on the information received. This feedback loop allows the system to maintain the refined accuracy of its suggestions.

[0745] Specific example

[0746] For example, suppose a user is taking vitamin D supplements and heart medication. This user enters information about these products into their device. The server analyzes the interaction database and confirms that vitamin D does not interfere with the effectiveness of the heart medication. As a result, it suggests maintaining the original schedule and provides new health advice, notifying the user that sun exposure and exercise can help improve heart function.

[0747] Example of a prompt

[0748] The user enters information about the medications they are currently taking in the following format:

[0749] Drug name: Vitamin D

[0750] Dose: 500 IU

[0751] Frequency of use: Daily

[0752] Dosage time: Morning

[0753] Based on this information, please check for interactions between vitamin D and heart medications and, if necessary, suggest an optimal dosage schedule.

[0754] This system aims to raise users' awareness of drug interactions and support safe and effective medication use.

[0755] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0756] Step 1:

[0757] Users enter information about the medications and supplements they are taking using a dedicated application or web interface on their device. This information includes the name of the medication, dosage, frequency of administration, and time of administration. The entered information is stored on the device as data for subsequent processing.

[0758] Step 2:

[0759] The terminal converts the information entered by the user into a standard format. This format conversion ensures data consistency and facilitates analysis on the server. Next, before sending the converted data to the server, the terminal performs user authentication according to security protocols to confirm that the user has legitimate authority.

[0760] Step 3:

[0761] The server receives data sent from the terminal and stores it in the database as individual user profiles. This storage makes the data available for future reference and analysis. The server also uses the received data to reference the pre-created interaction database and begin analysis.

[0762] Step 4:

[0763] The server analyzes drug interactions based on the latest medical data. This analysis utilizes cloud-based databases and high-performance computing resources. Using generative AI models, the server generates medication suggestions and health advice based on the analysis results. The resulting suggestions are customized to each user's specific health condition.

[0764] Step 5:

[0765] The server sends the generated proposal to the terminal. This transmission uses the latest communication protocols to ensure data security and deliverability. The terminal receives a notification from the server and displays information so the user can review the proposal.

[0766] Step 6:

[0767] Users review suggestions notified via their devices and revise their medication schedules. Based on these suggestions, users adjust their medication schedules or decide on specific health actions (e.g., exercise or dietary changes). This allows users to utilize health management information and achieve safe and effective medication use.

[0768] (Application Example 1)

[0769] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] To ensure the safety of medication use while comprehensively improving users' health, a system is needed that examines interactions between various medications and supplements and provides optimal dosage schedules and health advice. However, existing technologies have challenges in ensuring the timeliness of data and providing appropriate notifications to mobile devices.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0772] In this invention, the server includes means for receiving drug information from the user, means for delivering generated suggestions to the user's mobile device, and means for analyzing drug interaction information using a generative model. This enables appropriate health management and safe drug use in response to the user's dynamic state.

[0773] A "user" is an individual or organization that provides drug information and receives suggestions through the system.

[0774] "Means for receiving drug information" refers to a function for collecting data on drugs and supplements entered by users.

[0775] "Means for analyzing interactions" refers to a function that evaluates and analyzes interactions between drugs based on the input drug information.

[0776] "Means for generating medication suggestions" refers to a function that creates the optimal medication schedule or alternatives based on the analysis results.

[0777] "Means of notifying users of proposals" refers to a function that sends generated proposals to the user's device to notify them.

[0778] A "mobile device" refers to a portable electronic device such as a smartphone, which is a device used by users to receive information.

[0779] "Methods for analyzing drug interaction information" refers to functions that utilize generative models to evaluate data on drug interactions and create suggestions regarding drug combinations.

[0780] A "generative model" is a technology of artificial intelligence and machine learning models designed to support the analysis of drug information and the generation of recommendations.

[0781] This invention provides a system for users to take medication safely and effectively. This system operates by having the user input medication information via a mobile device such as a smartphone, and a server analyzes that information to provide optimal dosage suggestions.

[0782] The server is built using programming languages ​​such as Python and uses AI models to analyze drug interaction data received from medical professionals. The server also stores the received drug data in a database management system (e.g., MySQL) and generates personalized medication suggestions and health advice based on the analysis results. This makes it possible to provide information tailored to the individual needs of each user.

[0783] Users' smartphones can connect to the internet and receive notifications from the server. Upon receiving a notification, users can review the suggested medications through the application and adjust their medication schedule as needed. Furthermore, the server also provides suggestions for improving lifestyle habits that can contribute to health, based on the analysis results.

[0784] As a concrete example, suppose a user registers information about heart medication and vitamin supplements on their smartphone. The server receives this information, uses a generative AI model to analyze whether there are any interactions, and proposes an optimal dosage schedule. The server then notifies the user of the results on their smartphone and also provides advice on exercise and diet in their daily life.

[0785] An example of a prompt message is, "Provide safe usage advice and lifestyle modification suggestions to users taking heart medication and vitamin supplements."

[0786] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0787] Step 1:

[0788] Users input information such as the name, dosage, and frequency of their current medications through a smartphone application. The entered medication information is temporarily stored within the application and formatted. The output is a list of the medication information entered by the user.

[0789] Step 2:

[0790] The terminal sends the formatted drug information to the server. The server receives this information, goes through an authentication process, and stores it in its database. To confirm that the entered drug information has been received correctly, the server outputs a message indicating that it has been received.

[0791] Step 3:

[0792] The server retrieves drug information stored in the database and analyzes drug interactions using a generative AI model. The input consists of drug information and existing interaction data. Based on this, it performs calculations to derive new drug interaction information. The output is the analysis result regarding the presence or absence of interactions.

[0793] Step 4:

[0794] The server generates an optimal dosage schedule and health advice based on the analysis results. An example prompt used here is the input: "Provide safe dosage advice and lifestyle improvement suggestions for a user taking heart medication and vitamin supplements." The output is suggested data detailing a specific dosage schedule and advice.

[0795] Step 5:

[0796] The server sends the generated suggestion data to the terminal. The terminal receives this data and notifies the user. The server outputs a confirmation message indicating that the notification was successful. The user receives the notification, reviews the information generated within the system, and takes action.

[0797] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0798] This invention provides a system that offers more effective and personalized healthcare support by considering the user's emotional state in addition to managing and suggesting drug information. This system incorporates an emotion engine that recognizes the user's emotions when inputting drug information and considers this information along with the analysis of the drug data. The system configuration and details of each component are described below.

[0799] System configuration:

[0800] Terminal: The user inputs medication information and provides emotional data. The terminal is equipped with a camera and microphone, and analyzes facial expressions and voice tone to determine the user's emotions.

[0801] Server: Retrieves entered drug information and emotion data and stores it in the database. Emotion data is used to customize the suggested content.

[0802] Emotion Engine: Determines the user's emotions through facial recognition and voice analysis technologies. Measures the user's stress and sense of security and generates appropriate feedback.

[0803] Database: Manages drug information, sentiment data, and interaction information. New drug information is updated to maintain its current state at all times.

[0804] Detailed program processing:

[0805] 1. As the user inputs medication information, the emotion engine analyzes facial expressions and voice tone through the device's camera and microphone to generate emotion data.

[0806] 2. The device formats medication information and emotional data and sends it to the server. The data is stored as an individual user profile.

[0807] 3. Based on the information received by the server, drug interaction analysis is performed. The analysis results are linked to drug dosage recommendations that take into account the user's emotional state.

[0808] 4. The server adjusts the generated suggestions according to the user's emotional state. If the user is feeling stressed, it will include advice that promotes relaxation and reassuring elements.

[0809] 5. The server sends the adjusted proposal to the terminal and notifies the user.

[0810] Specific example:

[0811] For example, suppose a user is taking a new heart medication and vitamin C while feeling anxious. In this case, the device detects the user's anxiety from their facial expressions, and the emotion engine collects that data. The server performs a standard interaction analysis to confirm that vitamin C does not affect the heart medication. However, to alleviate anxiety, the suggestions include simple relaxation exercises and reassuring support messages. Based on these suggestions, the user can take their medication with peace of mind.

[0812] Thus, by taking into account the emotional state of the user, the present invention provides more personalized support and creates an environment in which medications can be used with peace of mind.

[0813] The following describes the processing flow.

[0814] Step 1:

[0815] When users input information about over-the-counter medications, prescription drugs, and supplements using their devices, their facial expressions and voice tone are simultaneously recorded through the device's camera and microphone. An emotion engine uses this data to analyze the user's emotional state and determine their stress and sense of security levels.

[0816] Step 2:

[0817] The terminal converts the entered drug information and analyzed emotional data into a predetermined format and sends it to the server via a secure communication channel.

[0818] Step 3:

[0819] The server stores the received drug information in a database, and also stores emotional data in association with the user profile.

[0820] Step 4:

[0821] The server uses drug information to refer to an interaction database and analyzes drug interactions and contraindications. An AI algorithm is used to identify potential risks.

[0822] Step 5:

[0823] The server uses the drug interaction analysis results to generate standard dosage suggestions. These suggestions are then customized as needed based on the received emotional data. For example, if the user is feeling anxious, additional messages or simple relaxation advice may be added to alleviate this anxiety.

[0824] Step 6:

[0825] The server sends the final proposal to the device and notifies the user. The proposal is communicated to the user via push notification or in-app message.

[0826] Step 7:

[0827] Users can review the suggested content on their devices and adjust their medication schedule based on that information. They can also use the feedback function to provide feedback to the system regarding the usefulness of the suggestions.

[0828] Through these steps, the system can provide users with safe and personalized medication recommendations, while also addressing their emotional needs.

[0829] (Example 2)

[0830] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0831] There is a need to provide a more individualized approach to the health problems faced by each user. Conventional systems do not consider the user's emotional state when suggesting medications, potentially leading to users taking medication while experiencing anxiety and stress. Furthermore, the lack of adequate provision of appropriate improvement plans based on health conditions is also a challenge.

[0832] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0833] In this invention, the server includes means for receiving drug information from the user, means for recognizing the user's emotional state and generating emotional data, and means for analyzing interactions based on the input drug information. This makes it possible to provide the user with personalized drug dosage suggestions that take their emotional state into account, thereby reducing the user's anxiety and stress and providing a sense of security regarding their health.

[0834] "Users" refer to individuals who provide drug information and emotional data to the system.

[0835] "Drug information" refers to data related to a drug, such as its name, frequency of administration, and dosage.

[0836] "Interaction" refers to the chemical and physiological effects that can occur when multiple drugs act simultaneously in the body.

[0837] "Emotional state" refers to the user's psychological state, including stress, anxiety, and sense of security.

[0838] "Emotional data" refers to information that quantifies and qualitatively represents the emotional state of users.

[0839] A "database" refers to a collection of information that stores and manages drug information, emotional data, and health information.

[0840] A "specialist" refers to a professional in the medical or chemical field who possesses knowledge of drug interactions and can provide data to the system.

[0841] This invention is a system that provides personalized health management to users. This system mainly consists of a terminal, a server, an emotion engine, and a database.

[0842] 1. Device functions:

[0843] The device is used for inputting user medication information and collecting emotional data. The device has a built-in camera and microphone, which are used to analyze the user's facial expressions and voice. This generates emotional data in real time.

[0844] 2. Server role:

[0845] The server receives medication information and emotional data transmitted from the terminal and stores them in a database. The server also analyzes the emotional data using an emotion engine. Specifically, it uses a generative AI model to generate medication suggestions tailored to the user's emotional state and adjusts the information accordingly.

[0846] 3. Emotional Engine:

[0847] The emotion engine uses facial recognition and voice analysis technologies to determine the user's emotional state. This allows it to provide appropriate feedback to the user and create a personalized healthcare plan.

[0848] 4. Database management:

[0849] The database stores drug information, emotional data, and interaction information, and is regularly updated to ensure it remains up-to-date. This makes it possible to provide users with accurate information.

[0850] Specific example:

[0851] For example, if a user is taking both heart medication and vitamin C, the device analyzes the user's facial expressions and detects anxiety. The emotion engine sends this data to a server, which uses a generative AI model to generate medication suggestions, including relaxation exercises to reduce anxiety. These suggestions are then communicated to the user through the device.

[0852] Example of a prompt:

[0853] "Please explain how this system analyzes emotions and provides feedback when users enter medication information."

[0854] This enables the present invention to provide more effective healthcare support that takes into account the user's emotional state.

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] The user inputs medication information. The user uses a smartphone or tablet to input the medication name, dosage, frequency of administration, etc. Simultaneously, the device activates its built-in camera and microphone to record the user's facial expressions and voice. Medication information and audio-visual data are acquired as input data.

[0858] Step 2:

[0859] The device uses the acquired audio-visual data to send an analysis request to the emotion engine. The emotion engine applies facial recognition and voice analysis algorithms. It uses the user's image and voice data as input and generates data to measure the user's emotional state (e.g., reassurance, anxiety, stress) as output.

[0860] Step 3:

[0861] The terminal formats drug information and generated emotion data and sends it to the server in JSON format. It uses a combined format of drug information and emotion data as input and generates encrypted data packets as output.

[0862] Step 4:

[0863] The server analyzes the received data and stores it in a database. It takes drug information and emotional state data as input and registers them as user profiles using a database management system. This allows the data to be saved as history and used by other algorithms.

[0864] Step 5:

[0865] The server compares incoming data with existing information in the database to analyze drug interactions. It takes new drug information and existing database information as input, applies an analysis algorithm to identify the presence or absence of interactions, and generates the interaction analysis results as output.

[0866] Step 6:

[0867] The server utilizes a generative AI model to generate personalized medication recommendations based on emotional data. It uses interaction analysis results and emotional state data as input and generates medication recommendations that include customized feedback tailored to the user's emotional state as output.

[0868] Step 7:

[0869] The server generates suggestions and sends them to the terminal, notifying the user. Using the generated suggestion data as input, it produces visual and audio notification formats as output. The user can then take appropriate action.

[0870] (Application Example 2)

[0871] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0872] In recent years, with the increasing use of medications, there has been a growing demand for personalized healthcare for individual users. However, the current system is based solely on general medication information and suggestions, and suffers from a lack of emotional support tailored to the user's emotional state and location. Furthermore, when users are emotionally unstable, extra care is needed, and there is a need to establish a system that provides appropriate support accordingly.

[0873] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0874] In this invention, the server includes means for analyzing the user's emotional state and personalizing information based on that analysis; means for acquiring location information and providing emotional support based on the user's emotional state and location information; and means for providing voice and visual reassurance based on the emotional analysis data. This enables personalized healthcare support that is tailored to the user's emotional state and location information.

[0875] "Users" refer to the individual people who use the system and are the entities that provide drug information and emotional data.

[0876] "Drug information" refers to data concerning a specific drug, including its name, intended use, efficacy, side effects, or method of use.

[0877] "Emotional state" refers to data that indicates the user's psychological state, and is analyzed from facial expressions and voice tone collected through cameras and microphones.

[0878] "Location information" refers to data indicating the user's geographical location, and is obtained using technologies such as GPS.

[0879] "Personalization" refers to providing information that is customized according to the user's individual emotional state and location.

[0880] "Mental support" refers to advice, audio, and visual information that takes into account the user's emotional state and promotes a sense of security and relaxation.

[0881] "Means of providing auditory and visual reassurance" refers to technologies that play music or messages based on the user's emotional state and provide a sense of security through visual effects.

[0882] The system for realizing this invention consists of a terminal, a server, an emotion engine, a database, and the like. The terminal is equipped with a camera and a microphone, and when the user inputs drug information, it analyzes their facial expressions and tone of voice to recognize their emotional state. Specifically, this analysis is performed using emotion recognition software such as EmotionSDK.

[0883] The server acquires drug information and emotional data transmitted from the terminal and analyzes drug interactions based on this information. A dedicated algorithm is used for the analysis, and new drug and health information is constantly updated in the database. Utilizing the information in this database, the server generates personalized suggestions, taking into account the user's emotions and location, and notifies the terminal.

[0884] For example, if a user consumes a caffeinated beverage while feeling anxious, the server recognizes the user's anxious emotions through facial expression analysis. Based on the analysis results, it confirms that caffeine intake will not affect interactions with certain medications and provides mental support, such as recommending relaxing music. At the same time, if the user is in a quiet location, it displays a message on the device that provides a sense of calm and reassurance.

[0885] A concrete example of an input prompt for a generative AI model is the following sentence: "Suggest a relaxation message to provide to a user who is feeling anxious." In this way, a sense of security can be given to the user, promoting the safe use of medication.

[0886] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0887] Step 1:

[0888] The device uses a camera and microphone to input medication information from the user and analyzes their facial expressions and voice tone during this process. It acquires camera video and audio data as input and uses the EmotionSDK to determine the user's emotional state. It generates emotional data and medication information as output.

[0889] Step 2:

[0890] The terminal formats the generated drug information and emotion data and sends it to the server. The input is the emotion data and drug information obtained in step 1, and the output is the transmission of data to the server. Specifically, the data is transmitted using a network communication protocol.

[0891] Step 3:

[0892] The server analyzes drug interactions based on drug information and emotion data received from the terminal. It retrieves relevant drug data from the database and performs the analysis while cross-referencing it. The input is data sent from the terminal and information from the database, and the output is the interaction analysis results.

[0893] Step 4:

[0894] The server generates personalized suggestions by taking into account interaction analysis results, sentiment data, and user location information. Location information is obtained from GPS data indicating the user's location. The inputs are interaction analysis results, sentiment data, and location information, and the output is a customized suggestion message.

[0895] Step 5:

[0896] The server sends the generated suggestion message to the terminal and notifies the user. The input is the suggestion message obtained in step 4, and the output is the notification to the user. Specifically, a mechanism is used to display the message on the terminal's display.

[0897] Step 6:

[0898] Based on the suggestions received, the user receives medication and psychological support. Input is a notification from the server, and specific actions may include taking medication or listening to relaxation music. Output is the user's sense of security and safe use of medication.

[0899] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0900] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0901] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0902] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0903] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0904] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0905] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0906] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0907] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0908] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0909] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0910] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0911] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0912] 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.

[0913] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0914] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0915] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0916] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0917] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0918] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0919] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0920] The following is further disclosed regarding the embodiments described above.

[0921] (Claim 1)

[0922] Means of receiving drug information from users,

[0923] A means of analyzing drug interactions based on input drug information,

[0924] A means for generating optimal medication suggestions based on analysis results,

[0925] A means of notifying users of the generated suggestions,

[0926] A means of updating the database with new drug information and health information,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, further comprising means for providing suggestions for improving exercise and diet based on the user's health condition.

[0930] (Claim 3)

[0931] The system according to claim 1, further comprising means for receiving data on drug interactions from medical professionals and utilizing it within the system.

[0932] "Example 1"

[0933] (Claim 1)

[0934] A device that receives drug information from users,

[0935] A device that analyzes drug interactions based on input drug information,

[0936] A device that generates optimal medication recommendations based on analysis results,

[0937] A device that notifies the user of the generated suggestions,

[0938] A device that stores drug information as individual profiles in a database,

[0939] A device that automatically generates health maintenance advice using a generative AI model,

[0940] A device that updates a database with new drug information and health information,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising a device that provides suggestions for improving exercise and diet based on the user's health condition.

[0944] (Claim 3)

[0945] The system according to claim 1, further comprising a device for receiving and utilizing data on drug interactions from medical professionals within the system.

[0946] "Application Example 1"

[0947] (Claim 1)

[0948] Means of receiving drug information from users,

[0949] A means of analyzing drug interactions based on input drug information,

[0950] A means for generating optimal medication suggestions based on analysis results,

[0951] A means of notifying users of the generated suggestions,

[0952] A means for delivering generated suggestions to the user's mobile device,

[0953] A means of updating the database with new drug information and health information,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, further comprising means for providing suggestions for improving exercise and diet based on the user's health condition.

[0957] (Claim 3)

[0958] The system according to claim 1, further comprising means for receiving data on drug interactions from medical professionals and analyzing drug interaction information using a generative model.

[0959] "Example 2 of combining an emotion engine"

[0960] (Claim 1)

[0961] Means of receiving drug information from users,

[0962] A means of analyzing drug interactions based on input drug information,

[0963] A means of recognizing the emotional state of a user and generating emotional data,

[0964] A means for generating optimal medication recommendations based on analysis results and emotional data,

[0965] A means of notifying users of the generated suggestions,

[0966] A means of updating the database with new drug information and health information,

[0967] A system that includes this.

[0968] (Claim 2)

[0969] The system according to claim 1, further comprising means for providing suggestions for improving exercise and diet based on the user's health and emotional state.

[0970] (Claim 3)

[0971] The system according to claim 1, further comprising means for receiving data on drug interactions from experts and utilizing it within the system.

[0972] "Application example 2 when combining with an emotional engine"

[0973] (Claim 1)

[0974] Means of receiving drug information from users,

[0975] A means of analyzing drug interactions based on input drug information,

[0976] A means for generating optimal medication suggestions based on analysis results,

[0977] A means of notifying users of the generated suggestions,

[0978] A means of updating the database with new drug information and health information,

[0979] A means of analyzing the emotional state of users and personalizing information based on that analysis,

[0980] A means of acquiring location information and providing mental support based on the user's emotional state and location information,

[0981] A system that includes this.

[0982] (Claim 2)

[0983] The system according to claim 1, further comprising means for providing suggestions for improving exercise and diet based on the user's health condition, and for sending relaxation messages corresponding to the user's emotional state.

[0984] (Claim 3)

[0985] The system according to claim 1, further comprising means for receiving data on drug interactions from medical professionals and utilizing it within the system, and means for providing voice and visual reassurance based on emotion analysis data. [Explanation of Symbols]

[0986] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of receiving drug information from users, A means of analyzing drug interactions based on input drug information, A means for generating optimal medication suggestions based on analysis results, A means of notifying users of the generated suggestions, A means of updating the database with new drug information and health information, A system that includes this.

2. The system according to claim 1, further comprising means for providing suggestions for improving exercise and diet based on the user's health condition.

3. The system according to claim 1, further comprising means for receiving data on drug interactions from medical professionals and utilizing it within the system.

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