system

Abstract

We provide the system. [Solution] Means for providing an interface for users to input drug information using other devices, A database means for storing data received from the aforementioned device and associating it with past information, An analytical means for analyzing accumulated data and extracting medical-related information, A generation means that generates personalized information based on the information analyzed by the aforementioned analysis means, A display means for presenting the generated information to the user, 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 as a 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 society, it is common for users to take and manage multiple medications, but it is difficult to eliminate concerns about the effects, administration methods, and drug combinations of medications. Also, it is difficult for pharmacists and healthcare providers to always keep track of information on new treatment methods and drugs and provide appropriate and prompt advice to users. This has become an obstacle for the elderly and patients with difficulty in moving to take medications safely and efficiently.

Means for Solving the Problems

[0005] This invention solves the problem by providing an interface means for users to input drug information, a database means for storing received data, an analysis means for performing data analysis, a generation means for generating personalized information, and a display means for presenting the analysis results. Furthermore, it enables support for users and healthcare providers through an evaluation means for receiving and evaluating feedback, and a suggestion means for proposing new treatment methods. With this system, users can take their medication with peace of mind, and healthcare providers can always provide advice to patients based on the latest information.

[0006] An "interface means" is a means for users to input drug information and a mechanism for accurately collecting data on the device.

[0007] A "database system" refers to a storage device and its management system for saving received information and managing it in conjunction with past records.

[0008] "Analysis means" refers to a means of performing a technical process to analyze relevant information based on stored data and extract necessary information.

[0009] "Generation means" refers to algorithms or programs for generating personalized medical information based on analysis results.

[0010] "Display means" refers to devices or interfaces used to visually present generated information to users.

[0011] An "evaluation method" is a system that has the function of receiving feedback data from users and deriving evaluation results by analyzing that data.

[0012] "Proposed means" refers to computational processes and algorithms that analyze new treatment methods and drug information to suggest treatment methods for users. [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] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0016] In the following embodiments, a labeled 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, a labeled 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, a labeled 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, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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] This invention provides a system that offers an interface allowing users to input medication information via devices such as smartphones, tablets, or personal computers. The system's core server receives user input information transmitted from the device and links it to a securely stored database, managing the data along with the user's past prescription history and related medical literature.

[0035] The server analyzes the received data and uses analytical tools to extract information about medications and symptoms. Next, it uses the generated information to create personalized feedback and advice, constructing information tailored to user needs.

[0036] Advice generated based on information submitted by the user is sent from the server to the terminal, which then displays it visually to the user. This display method allows the user to check the effects of the medication, how to take it, and precautions in real time.

[0037] Furthermore, this system includes an evaluation mechanism that allows users to send feedback to a server after taking medication, and continuously evaluates the drug's effectiveness based on that information. The evaluation results can be used to revise prescriptions and treatment methods as needed, through notification to healthcare professionals.

[0038] As a concrete example, when a user is prescribed a painkiller and enters the drug name into their terminal, that information is sent to a server. The server analyzes the latest medical literature on the drug and past user history to determine effective dosage and precautions, and presents the generated information to the user. When the user enters any changes in their physical condition after taking the medication, the system uses that information to evaluate the drug's effectiveness and notifies a medical professional if necessary. In this way, this invention efficiently connects users and healthcare providers, providing an environment where medication can be used with peace of mind.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users use a terminal to input information such as the names of prescribed medications, their current health condition, and their medication history. Users can also input this information directly into the terminal through a dedicated application.

[0042] Step 2:

[0043] The terminal sends user input information to the server. The data is securely transferred using a communication protocol and reaches the server via the internet.

[0044] Step 3:

[0045] The server verifies the data received from the terminal and saves it to the database. This information is managed in association with individual user accounts and used for subsequent processing.

[0046] Step 4:

[0047] The server begins analysis based on the information stored in the database. Using the analysis tools, it extracts relevant data from the input drug information, medical literature related to the user's condition, and past prescription history.

[0048] Step 5:

[0049] Based on the information obtained by the analysis tools, the server generates personalized advice using the generation tools. Drug effects, precise dosage instructions, and potential precautions are created at this stage.

[0050] Step 6:

[0051] The server sends the generated advice information to the terminal. This prepares the user to quickly obtain the information they need.

[0052] Step 7:

[0053] The terminal displays personalized advice received from the server to the user. This display method allows the user to review and understand important information regarding medication.

[0054] Step 8:

[0055] Users re-enter information about changes in their physical condition and perceived effects after taking medication via a device. User feedback is crucial to the evaluation process.

[0056] Step 9:

[0057] The server receives feedback from users and analyzes it using evaluation tools. This evaluation assesses the actual effects and side effects of the drug.

[0058] Step 10:

[0059] The server notifies healthcare providers of the evaluation results. If necessary, notifications may be sent, leading to prescription revisions or additional medical advice.

[0060] (Example 1)

[0061] 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."

[0062] Currently, many people have difficulty obtaining appropriate information and advice when using medications on their own. In particular, understanding the effects and side effects of each medication is challenging when multiple medications are prescribed. Furthermore, limited communication with doctors and pharmacists creates a need for a system that ensures patients fully understand how to take their medications and any precautions.

[0063] 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.

[0064] In this invention, the server includes means for providing an interface for a user to input drug information using a processing device, means for securely communicating the input information and transmitting it to a central control unit, and means for recording the data received by the central control unit and associating it with past prescription history and related information. This enables the user to obtain accurate and personalized information about medication in real time, thereby reducing anxiety about taking medication.

[0065] "User" refers to a user who uses the system to input and process drug information.

[0066] A "processing device" is an electronic device used by users to input drug information, and includes smartphones, tablets, and personal computers.

[0067] A "central control unit" refers to the core server of a system that receives, stores, and analyzes information transmitted from processing units.

[0068] A "recording medium" refers to a database used to securely store data received by a central control unit and to associate it with past history and related information.

[0069] "Analysis means" refers to technologies and algorithms for analyzing received and stored data and extracting medically relevant information.

[0070] "Generation means" refers to a process or apparatus for generating personalized feedback based on information extracted by analysis means.

[0071] "Display means" refers to interfaces or devices used to visually present generated feedback information to the user.

[0072] This invention provides a system that allows users to efficiently and safely manage their medication information. Users input information such as the name of the medication and how to use it into an interface using a terminal such as a smartphone, tablet, or personal computer. This data is transmitted from the terminal to a server, which is a central control unit.

[0073] The server securely transmits received information using encrypted communication technology and stores it in a database. This database is structured based on the user's past prescription history and relevant medical literature, and manages the received data by appropriately linking it. In addition, to analyze the received data, the server uses analytical methods such as natural language processing technology and generative AI models to extract detailed information about medications and symptoms.

[0074] The analyzed information is generated as personalized feedback by the server. This generation method provides personalized advice based on the user's past usage history and current status. The generated feedback is sent to the device and visually presented to the user via the device's display. In this way, the user can check important information such as effective medication use and precautions in real time.

[0075] For example, if a user enters information about a painkiller, the server analyzes the latest literature on that drug and the user's past history to determine the appropriate dosage and precautions. This information is generated as feedback and presented to the user using prompts such as, "Please tell me the effective way to take this painkiller and what precautions to take."

[0076] Furthermore, after users take their medication, they send feedback to the server about changes in their physical condition. The server analyzes this data and continuously evaluates the drug's effectiveness. The evaluation results are notified to healthcare professionals as needed to help optimize treatment and prescriptions.

[0077] In this way, the system connects users and healthcare providers, providing an environment that supports safe and effective medication management.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user enters medication information.

[0081] Users input information such as drug name, dosage, and administration schedule using an interface on a device such as a smartphone or computer. The device formats the entered information and prepares it for transmission to the server.

[0082] Step 2:

[0083] The terminal sends the input data to the server.

[0084] The terminal uses encrypted communication to send input data to the server. This data includes drug names and related information, and is treated as basic data for analysis by the server.

[0085] Step 3:

[0086] The server receives the data and saves it to the database.

[0087] The server first verifies the received drug information to confirm its effectiveness. The verified data is securely stored in a database, linked to the user's past prescription history and relevant medical literature.

[0088] Step 4:

[0089] The server analyzes the data and extracts the information.

[0090] The server analyzes data based on information stored in the database, using generative AI models and natural language processing techniques. This analysis extracts detailed information such as drug effects, side effects, and appropriate dosage. The input is information from the database, and the output is analyzed medical-related data.

[0091] Step 5:

[0092] The server generates personalized feedback.

[0093] Based on the extracted information, the server generates feedback tailored to the user's specific needs. This process generates personalized advice based on the user's past usage history and specific symptoms.

[0094] Step 6:

[0095] The server sends the generated feedback to the terminal.

[0096] The generated feedback is sent from the server to the device, making it easily viewable by the user. This allows the user to visually check the feedback content on their device.

[0097] Step 7:

[0098] The system receives and displays user feedback.

[0099] The device displays received feedback and provides the user with information on the effects of the medication, how to take it, and precautions. This allows the user to manage their medication appropriately.

[0100] Step 8:

[0101] Users provide feedback after using the product.

[0102] After using the medication, the user re-enters feedback on any changes in their physical condition or the effects of the drug into the device.

[0103] Step 9:

[0104] The device sends feedback data to the server.

[0105] The terminal sends the input feedback data to the server, enabling continuous drug evaluation.

[0106] Step 10:

[0107] The server analyzes the feedback and notifies medical professionals as needed.

[0108] The server analyzes the received feedback data to evaluate the drug's effectiveness and side effects. If necessary, it notifies healthcare professionals of the results to support the provision of appropriate prescriptions and treatments.

[0109] (Application Example 1)

[0110] 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."

[0111] Understanding information about drug usage and side effects, and taking medication appropriately, is difficult for the elderly and patients who need to take multiple medications. Furthermore, there is a lack of means to accurately record changes in physical condition after taking medication and to efficiently share this information with healthcare providers. Therefore, there is a need to create an environment where users can use medication with peace of mind.

[0112] 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.

[0113] In this invention, the server includes means for providing an interface for users to input drug information using another terminal; storage means for storing data received from the terminal and associating it with past data; analysis means for analyzing the accumulated data and extracting health-related information; display means for presenting the generated information on the user's mobile terminal; evaluation means for recording the user's physical condition after drug intake and performing an evaluation based on the recorded information; and communication means for notifying healthcare professionals of the evaluation results. This enables users to understand the effects and precautions of drugs in real time and to manage their medication appropriately. Furthermore, it facilitates information sharing with healthcare providers and enables support for safe and effective drug use.

[0114] A "terminal" is an external device used by users to input or receive information.

[0115] "Memory means" refers to a device or method that has the function of securely storing received data and associating it with past information.

[0116] "Analysis methods" refer to the process of extracting health-related information based on accumulated data and conducting detailed analysis.

[0117] "Display means" refers to a device or method for visually presenting generated information to a user.

[0118] "Evaluation methods" refer to a function that analyzes the physical condition recorded by the user after taking medication, evaluates the results, and provides improvement measures as needed.

[0119] "Communication methods" refer to methods of transmitting information to notify healthcare professionals of evaluation results.

[0120] This system is designed for the management and analysis of users' medication information. It primarily utilizes devices such as smartphones, cloud servers, and analysis software.

[0121] The server receives medication information from the terminal and securely stores that data. The received data is stored in cloud services such as AWS® and Google® Cloud and linked to past medication history. Data analysis is also performed using libraries such as Python's Pandas and Scikit-learn. In the analysis process, the latest health-related information regarding the drug's effects and side effects is extracted, and the generated personalized information is sent to the terminal.

[0122] The terminal plays the role of visually displaying the generated information to the user. A user interface is built using React Native and other technologies to provide the generated information in an easy-to-use format. Users input their physical condition after taking medication using the terminal, and this information is sent back to the server for evaluation.

[0123] For example, when a user takes "Medication A," notifications regarding the dosage schedule and warnings about side effects are sent via the application. The user records their physical condition after taking the medication in the app, and this data is sent to a server and shared with healthcare providers as reference information for their next medical consultation.

[0124] When a generative AI model is used to generate information, the following prompts can be used:

[0125] "Based on the medication information entered by the user, generate feedback that combines past medication history with the latest research findings."

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] Users input medication information using a smartphone or other device. This input includes information such as the name of the drug, dosage, and time of administration. The entered data is formatted on the device and then converted into a format that can be sent to the server.

[0129] Step 2:

[0130] The server receives medication information sent from the terminal. The received data is stored in a database on AWS or Google Cloud. Here, data processing is performed to link past medication history with current medication information.

[0131] Step 3:

[0132] The server analyzes the stored data. This analysis uses Python's Pandas and Scikit-learn to extract information about drug effects and side effects based on the latest medical literature and data. The analysis results are generated as personalized feedback for each user.

[0133] Step 4:

[0134] A generative AI model is used to generate more detailed feedback from the analysis results. The prompt message "Generate feedback combining past medication history and the latest research findings based on the medication information entered by the user" is used. The generated feedback is stored on the server.

[0135] Step 5:

[0136] The server sends the generated feedback to the device. The device receives this information and displays it visually on the user interface. Based on this display, the user can check their medication schedule and precautions.

[0137] Step 6:

[0138] After taking the medication, the user enters any changes in their physical condition or any side effects into the device. This feedback information is then sent back to the server.

[0139] Step 7:

[0140] The server receives post-medication feedback from users and analyzes its effectiveness using evaluation tools. If necessary, these evaluation results are communicated to healthcare professionals. Healthcare providers can use this information to inform future consultations and prescription changes.

[0141] 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.

[0142] This invention begins with a user inputting drug information via a terminal, which is then transmitted to a server and stored in a database. The terminal also acquires emotional data based on the user's voice, facial expressions, and text input, and transmits this data to the server.

[0143] The server analyzes this data using analytical tools, extracting the user's prescription history and medical information, and generating personalized advice based on emotional data obtained by the emotion engine. The emotion engine identifies the user's emotional state from their input and responses, and adjusts the information provided by the generation tools accordingly.

[0144] The generated information is flexibly adapted to the user's emotional state, and the adjusted content is sent to the device and presented to the user. The displayed information includes not only the effects and precautions for use of medications, but also health advice tailored to the user's emotions.

[0145] For example, if a user asks a question related to taking painkillers and the device indicates that the user's emotions are unstable, the system will suggest additional information or relaxation methods to alleviate the user's anxiety. Furthermore, the emotion engine continuously receives feedback data, and by continuously evaluating the drug's effects and the user's emotional response using evaluation tools, it is possible to provide healthcare providers with further information.

[0146] Thus, the present invention is an advanced medical support system that dynamically responds to the user's emotions and enables more holistic health management.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] The user uses a device to input the name of the prescribed medication, symptoms, and medication history. In addition, the user provides data representing their emotional state by using voice or text input on the device.

[0150] Step 2:

[0151] The terminal transmits medication information and emotional data entered by the user to the server. The data is encrypted and securely transferred to the server.

[0152] Step 3:

[0153] The server saves the received information to the database. This allows for centralized management of both past user information and newly entered data.

[0154] Step 4:

[0155] The server processes the information stored in the database using analytical tools and extracts medical information suitable for the user. Simultaneously, the emotion engine analyzes the user's emotional data and identifies their emotional state.

[0156] Step 5:

[0157] Based on the emotional state identified by the emotion engine, the server generates personalized information using generation methods. This includes detailed information about medication use and emotionally sensitive advice.

[0158] Step 6:

[0159] The server sends the generated information to the terminal. The information is specifically tailored to take into account the user's emotional state, with additional consideration to alleviate the user's anxiety.

[0160] Step 7:

[0161] The terminal displays information received from the server to the user. The user can then use this information to take their medication safely and with peace of mind.

[0162] Step 8:

[0163] The user then inputs feedback on any changes in their physical condition or emotions they experienced after taking the medication, again via the device. Changes in emotions and side effects are also recorded.

[0164] Step 9:

[0165] The server receives feedback and uses evaluation tools to analyze and evaluate the drug's effects and the user's emotional response. Necessary actions are then considered based on the results.

[0166] Step 10:

[0167] Based on the analysis results, the server sends notifications to healthcare providers as needed. This allows healthcare providers to understand the user's condition and adjust prescriptions and treatment plans as necessary.

[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 will be referred to as the "terminal."

[0170] In today's healthcare environment, there is an increasing need to understand patients' conditions in real time and provide personalized health management. However, existing systems do not adequately provide information that takes into account the emotional state of the user, and new technologies are needed to improve the quality and effectiveness of medical support.

[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 providing an interface for users to obtain information using other devices; information recording means for storing data received from the devices and associating it with past information; information analysis means for analyzing the accumulated data and extracting medical-related information; emotion analysis means for identifying the user's emotional state and adjusting the content of the information provided; information generation means for generating personalized information based on the analyzed information; and means for presenting the generated information to the user and displaying a response. This enables more personalized and emotionally sensitive medical support.

[0173] "Means of providing an interface" refers to a system component that provides an intermediate function for users to obtain information using other devices.

[0174] An "information recording means" is a component of a system that has the function of storing received data and associating it with past information.

[0175] An "information analysis tool" is a component of a system that has the function of analyzing accumulated data and extracting medical-related information.

[0176] "Emotional analysis means" refers to a system component that has an analytical function to identify the emotional state of a user and adjust the content of the information provided accordingly.

[0177] An "information generation means" is a component of a system that has the function of generating personalized information based on analyzed information.

[0178] "Means of display" refers to the components of a system that presents generated information to the user and provides a visible response.

[0179] This invention realizes a system that includes a terminal that provides an interface for users to input drug information, and a server that analyzes the data and generates personalized advice.

[0180] The user inputs information such as the name of the medication, dosage, and timing of administration through the terminal's interface. Simultaneously, the terminal captures the user's voice and facial expressions, collecting emotional data. This data is securely transmitted to a server.

[0181] The server analyzes the received data using specialized analytical tools. Drug information is cross-referenced with a database, and the user's emotional state is analyzed using emotion analysis tools. Emotion analysis utilizes a generative AI model to generate situation-appropriate prompts while considering the user's personality and emotions.

[0182] The generated personalized advice is sent from the server to the terminal and presented to the user. This includes information on how to use medication, its effects, precautions regarding side effects, and health advice tailored to the user's emotional state. For example, if a user expresses anxiety about using painkillers, the server will suggest relaxation techniques and provide additional information to alleviate anxiety.

[0183] Examples of prompt messages include: "Based on user input, collect text, facial expression, and voice data, perform sentiment analysis, adjust the information provided based on the results, and respond to the user."

[0184] Thus, this system aims to support users in using medications with greater peace of mind by providing useful medical information, thereby improving overall health management.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The user enters medication information through the terminal's interface. This information includes the name of the medication, dosage, and time of administration. The terminal acquires this input data and simultaneously collects emotional data using voice and facial expression sensors. The collected data is then compiled into a single data package and prepared for transmission to the server.

[0188] Step 2:

[0189] The terminal transmits the user's medication information and emotional data to the server. This transmission uses encrypted communication over the internet. The server records the received data package in a database and prepares it for analysis.

[0190] Step 3:

[0191] The server uses the received drug information to cross-reference it with a database and extract relevant information. Furthermore, it utilizes a generative AI model to perform emotion analysis and analyze the user's emotional state. This analysis uses emotion analysis tools to identify emotions from the user's voice tone and facial expression changes. The results of the analysis include drug-related information and an evaluation of the emotional state.

[0192] Step 4:

[0193] The server generates personalized advice based on extracted drug information and sentiment analysis results. Information generation is used, with a generating AI model producing prompts appropriate to the user's state. Advice is then formulated based on these prompts. The generated advice includes drug usage, effects, precautions, and health recommendations tailored to the user's emotions.

[0194] Step 5:

[0195] The server sends the generated advice to the terminal. The terminal displays the advice to the user and provides voice guidance as needed. This display operation is designed to allow the user to intuitively understand the information.

[0196] Step 6:

[0197] The user inputs feedback on the advice received into a terminal. This feedback is sent to the server via the terminal. The server analyzes the feedback data and improves the system's information provision process as needed. This evaluation mechanism enables continuous system optimization.

[0198] (Application Example 2)

[0199] 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 device 14 will be referred to as the "terminal."

[0200] Conventional health management systems provide uniform medical information and health advice without considering the emotional state of users, making it difficult to provide effective support tailored to the individual needs of each user. Furthermore, individualized support that takes emotional aspects into account is particularly required for the elderly and users who require special support, and conventional technologies have faced challenges in this regard as well.

[0201] 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.

[0202] In this invention, the server includes input management means for users to input drug information using another terminal, information recording means for storing information received from the terminal and linking it with past information and related information, information analysis means for analyzing the accumulated information and extracting medical-related data, and emotion analysis means for receiving user emotion data, analyzing it and reflecting it in personalized advice. This makes it possible to provide personalized health advice that takes into account the user's emotional state.

[0203] "Input management means" refers to a device or software that provides a function for users to input drug information using another terminal.

[0204] "Information recording means" refers to a data storage or management system for saving information received from a terminal and associating it with past information and related information.

[0205] "Information analysis tools" refer to algorithms and programs used to analyze accumulated information, extract medical-related data, and process it.

[0206] "Information generation means" refers to a process or system used to generate personalized advice based on analyzed data.

[0207] "Information display means" refers to a display screen or user interface used to present generated advice to the user.

[0208] "Emotional analysis tools" refer to systems and algorithms that receive users' emotional data, analyze it, and incorporate it into personalized advice.

[0209] In this invention, the user's device plays a crucial role. Users input drug information and health-related information using portable devices such as smartphones and tablets. Input is performed via text, voice, or facial recognition using the smartphone's camera.

[0210] First, the terminal provides an interface for receiving medication information. This information is sent to a cloud server and stored via an information recording device. The server associates the received data with past medical information and analyzes the data using an information analysis device.

[0211] Based on the analyzed data, the information generation system generates personalized health advice. This generated advice is presented to the user through the information display system. At the same time, the server uses an emotion analysis system to analyze the emotional data received from the user and incorporates it into the advice. This enables flexible responses tailored to the user's emotional state.

[0212] Specifically, services such as the Google Cloud Speech-to-Text API and AWS Rekognition are used to analyze voice and facial expression data. The analyzed emotion data is then used to optimize personalized advice.

[0213] For example, if an elderly person enters a comment such as "I'm worried about side effects," the system will automatically generate advice such as "Don't worry, we'll introduce safe ways to combine medications." An example of a prompt might be, "Based on the emotional data obtained from this user's voice and facial expression analysis, generate customized advice on medication information."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The user enters medication information using a terminal. Input is done via text or voice through input forms or a voice recognition interface. The entered information is temporarily stored by the terminal and sent to a server for data processing.

[0217] Step 2:

[0218] The device captures the user's facial expressions with its camera and acquires emotional data. This facial data is analyzed using a facial recognition service such as AWS Rekognition to determine the user's emotional state (e.g., anxiety, reassurance). The analysis results are sent to the server.

[0219] Step 3:

[0220] The server records drug information and emotional data received from the terminal into a database. In the data recording step, the information is associated with past medical information and prepared as input data for the next analysis step.

[0221] Step 4:

[0222] The server analyzes the received information using information analysis tools. Specifically, it compares drug data with past medical information and extracts medical-related data. Natural language processing technology is used for the analysis to extract necessary information from the text data.

[0223] Step 5:

[0224] The server generates personalized health advice based on the analyzed data using an information generation mechanism. Using a generation AI model, it creates advice that reflects the user's emotional state and medication information using prompts. For example, a prompt might say, "Generate customized medication advice based on the emotional data obtained from this user's voice and facial expression analysis."

[0225] Step 6:

[0226] The server sends the generated health advice to the device. The device displays this information to the user and notifies them through the screen and audio output. The user receives personalized advice and uses it as a guide to decide on their next course of action.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] This invention provides a system that offers an interface allowing users to input medication information via devices such as smartphones, tablets, or personal computers. The system's core server receives user input information transmitted from the device and links it to a securely stored database, managing the data along with the user's past prescription history and related medical literature.

[0244] The server analyzes the received data and uses analytical tools to extract information about medications and symptoms. Next, it uses the generated information to create personalized feedback and advice, constructing information tailored to user needs.

[0245] Advice generated based on information submitted by the user is sent from the server to the terminal, which then displays it visually to the user. This display method allows the user to check the effects of the medication, how to take it, and precautions in real time.

[0246] Furthermore, this system includes an evaluation mechanism that allows users to send feedback to a server after taking medication, and continuously evaluates the drug's effectiveness based on that information. The evaluation results can be used to revise prescriptions and treatment methods as needed, through notification to healthcare professionals.

[0247] As a concrete example, when a user is prescribed a painkiller and enters the drug name into their terminal, that information is sent to a server. The server analyzes the latest medical literature on the drug and past user history to determine effective dosage and precautions, and presents the generated information to the user. When the user enters any changes in their physical condition after taking the medication, the system uses that information to evaluate the drug's effectiveness and notifies a medical professional if necessary. In this way, this invention efficiently connects users and healthcare providers, providing an environment where medication can be used with peace of mind.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] Users use a terminal to input information such as the names of prescribed medications, their current health condition, and their medication history. Users can also input this information directly into the terminal through a dedicated application.

[0251] Step 2:

[0252] The terminal sends user input information to the server. The data is securely transferred using a communication protocol and reaches the server via the internet.

[0253] Step 3:

[0254] The server verifies the data received from the terminal and saves it to the database. This information is managed in association with individual user accounts and used for subsequent processing.

[0255] Step 4:

[0256] The server begins analysis based on the information stored in the database. Using the analysis tools, it extracts relevant data from the input drug information, medical literature related to the user's condition, and past prescription history.

[0257] Step 5:

[0258] Based on the information obtained by the analysis tools, the server generates personalized advice using the generation tools. Drug effects, precise dosage instructions, and potential precautions are created at this stage.

[0259] Step 6:

[0260] The server sends the generated advice information to the terminal. This prepares the user to quickly obtain the information they need.

[0261] Step 7:

[0262] The terminal displays personalized advice received from the server to the user. This display method allows the user to review and understand important information regarding medication.

[0263] Step 8:

[0264] Users re-enter information about changes in their physical condition and perceived effects after taking medication via a device. User feedback is crucial to the evaluation process.

[0265] Step 9:

[0266] The server receives feedback from users and analyzes it using evaluation tools. This evaluation assesses the actual effects and side effects of the drug.

[0267] Step 10:

[0268] The server notifies healthcare providers of the evaluation results. If necessary, notifications may be sent, leading to prescription revisions or additional medical advice.

[0269] (Example 1)

[0270] 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 glasses 214 will be referred to as the "terminal."

[0271] Currently, many people have difficulty obtaining appropriate information and advice when using medications on their own. In particular, understanding the effects and side effects of each medication is challenging when multiple medications are prescribed. Furthermore, limited communication with doctors and pharmacists creates a need for a system that ensures patients fully understand how to take their medications and any precautions.

[0272] 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.

[0273] In this invention, the server includes means for providing an interface for a user to input drug information using a processing device, means for securely communicating the input information and transmitting it to a central control unit, and means for recording the data received by the central control unit and associating it with past prescription history and related information. This enables the user to obtain accurate and personalized information about medication in real time, thereby reducing anxiety about taking medication.

[0274] "User" refers to a user who uses the system to input and process drug information.

[0275] A "processing device" is an electronic device used by users to input drug information, and includes smartphones, tablets, and personal computers.

[0276] A "central control unit" refers to the core server of a system that receives, stores, and analyzes information transmitted from processing units.

[0277] A "recording medium" refers to a database used to securely store data received by a central control unit and to associate it with past history and related information.

[0278] "Analysis means" refers to technologies and algorithms for analyzing received and stored data and extracting medically relevant information.

[0279] "Generation means" refers to a process or apparatus for generating personalized feedback based on information extracted by analysis means.

[0280] "Display means" refers to interfaces or devices used to visually present generated feedback information to the user.

[0281] This invention provides a system that allows users to efficiently and safely manage their medication information. Users input information such as the name of the medication and how to use it into an interface using a terminal such as a smartphone, tablet, or personal computer. This data is transmitted from the terminal to a server, which is a central control unit.

[0282] The server securely transmits received information using encrypted communication technology and stores it in a database. This database is structured based on the user's past prescription history and relevant medical literature, and manages the received data by appropriately linking it. In addition, to analyze the received data, the server uses analytical methods such as natural language processing technology and generative AI models to extract detailed information about medications and symptoms.

[0283] The analyzed information is generated as individualized feedback by the server. By means of this generation, personalized advice based on the user's past usage history and current status is provided. The generated feedback is transmitted to the terminal and visually presented to the user via the display device of the terminal. In this way, the user can check important information such as effective ways of taking medicine and precautions in real time.

[0284] For example, when the user inputs information about painkillers, the server analyzes the appropriate ways of taking the medicine and points to note based on the latest literature on the medicine and the user's past history. This information is generated as feedback and presented to the user using a prompt sentence in the form of "Please tell me the effective ways of taking painkillers and precautions".

[0285] Furthermore, after the user takes the medicine and sends feedback on the physical condition change to the server, the server analyzes the data and continuously evaluates the effect of the medicine. The evaluation results are notified to medical experts as necessary and used for optimizing treatment and prescriptions.

[0286] In this way, the system connects the user and medical providers and provides an environment to support safe and effective medicine management.

[0287] The flow of specific processing in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The user inputs medicine information.

[0290] The user uses an interface on a terminal such as a smartphone or a personal computer to input information such as the medicine name, dosage, and taking schedule. The terminal formats the input information and prepares to transmit it to the server.

[0291] Step 2:

[0292] The terminal sends the input data to the server.

[0293] The terminal uses encrypted communication to send input data to the server. This data includes drug names and related information, and is treated as basic data for analysis by the server.

[0294] Step 3:

[0295] The server receives the data and saves it to the database.

[0296] The server first verifies the received drug information to confirm its effectiveness. The verified data is securely stored in a database, linked to the user's past prescription history and relevant medical literature.

[0297] Step 4:

[0298] The server analyzes the data and extracts the information.

[0299] The server analyzes data based on information stored in the database, using generative AI models and natural language processing techniques. This analysis extracts detailed information such as drug effects, side effects, and appropriate dosage. The input is information from the database, and the output is analyzed medical-related data.

[0300] Step 5:

[0301] The server generates personalized feedback.

[0302] Based on the extracted information, the server generates feedback tailored to the user's specific needs. This process generates personalized advice based on the user's past usage history and specific symptoms.

[0303] Step 6:

[0304] The server sends the generated feedback to the terminal.

[0305] The generated feedback is sent from the server to the terminal, so that it can be easily viewed by the user. As a result, the user can visually confirm the content of the feedback on the terminal.

[0306] Step 7:

[0307] The user receives and displays the feedback.

[0308] The terminal displays the received feedback and provides the user with information on the effects of the drug, dosage method, and precautions. As a result, the user can perform appropriate drug management.

[0309] Step 8:

[0310] The user inputs feedback after use.

[0311] After using the drug, the user inputs feedback on changes in physical condition and the effects of the drug into the terminal again.

[0312] Step 9:

[0313] The terminal sends the feedback data to the server.

[0314] The terminal sends the input feedback data to the server to continuously enable the evaluation of the drug.

[0315] Step 10:

[0316] The server analyzes the feedback and notifies medical experts if necessary.

[0317] The server analyzes the received feedback data, evaluates the effects and side effects of the drug. If necessary, it notifies the medical experts of the results and supports the provision of appropriate prescriptions and treatments.

[0318] [ (Application Example 1)

[0319] 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."

[0320] Understanding information about drug usage and side effects, and taking medication appropriately, is difficult for the elderly and patients who need to take multiple medications. Furthermore, there is a lack of means to accurately record changes in physical condition after taking medication and to efficiently share this information with healthcare providers. Therefore, there is a need to create an environment where users can use medication with peace of mind.

[0321] 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.

[0322] In this invention, the server includes means for providing an interface for users to input drug information using another terminal; storage means for storing data received from the terminal and associating it with past data; analysis means for analyzing the accumulated data and extracting health-related information; display means for presenting the generated information on the user's mobile terminal; evaluation means for recording the user's physical condition after drug intake and performing an evaluation based on the recorded information; and communication means for notifying healthcare professionals of the evaluation results. This enables users to understand the effects and precautions of drugs in real time and to manage their medication appropriately. Furthermore, it facilitates information sharing with healthcare providers and enables support for safe and effective drug use.

[0323] A "terminal" is an external device used by users to input or receive information.

[0324] "Memory means" refers to a device or method that has the function of securely storing received data and associating it with past information.

[0325] "Analysis methods" refer to the process of extracting health-related information based on accumulated data and conducting detailed analysis.

[0326] "Display means" refers to a device or method for visually presenting generated information to a user.

[0327] "Evaluation methods" refer to a function that analyzes the physical condition recorded by the user after taking medication, evaluates the results, and provides improvement measures as needed.

[0328] "Communication methods" refer to methods of transmitting information to notify healthcare professionals of evaluation results.

[0329] This system is designed for the management and analysis of users' medication information. It primarily utilizes devices such as smartphones, cloud servers, and analysis software.

[0330] The server receives medication information from the terminal and securely stores that data. The received data is stored in cloud services such as AWS and Google Cloud and linked to past medication history. Data analysis is also performed using libraries such as Python's Pandas and Scikit-learn. In the analysis process, the latest health-related information regarding the drug's effects and side effects is extracted, and the generated personalized information is sent to the terminal.

[0331] The terminal plays the role of visually displaying the generated information to the user. A user interface is built using React Native and other technologies to provide the generated information in an easy-to-use format. Users input their physical condition after taking medication using the terminal, and this information is sent back to the server for evaluation.

[0332] For example, when a user takes "Medication A," notifications regarding the dosage schedule and warnings about side effects are sent via the application. The user records their physical condition after taking the medication in the app, and this data is sent to a server and shared with healthcare providers as reference information for their next medical consultation.

[0333] When a generative AI model is used to generate information, the following prompts can be used:

[0334] "Based on the medication information entered by the user, generate feedback that combines past medication history with the latest research findings."

[0335] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0336] Step 1:

[0337] Users input medication information using a smartphone or other device. This input includes information such as the name of the drug, dosage, and time of administration. The entered data is formatted on the device and then converted into a format that can be sent to the server.

[0338] Step 2:

[0339] The server receives medication information sent from the terminal. The received data is stored in a database on AWS or Google Cloud. Here, data processing is performed to link past medication history with current medication information.

[0340] Step 3:

[0341] The server analyzes the stored data. This analysis uses Python's Pandas and Scikit-learn to extract information about drug effects and side effects based on the latest medical literature and data. The analysis results are generated as personalized feedback for each user.

[0342] Step 4:

[0343] A generative AI model is used to generate more detailed feedback from the analysis results. The prompt message "Generate feedback combining past medication history and the latest research findings based on the medication information entered by the user" is used. The generated feedback is stored on the server.

[0344] Step 5:

[0345] The server sends the generated feedback to the device. The device receives this information and displays it visually on the user interface. Based on this display, the user can check their medication schedule and precautions.

[0346] Step 6:

[0347] After taking the medication, the user enters any changes in their physical condition or any side effects into the device. This feedback information is then sent back to the server.

[0348] Step 7:

[0349] The server receives post-medication feedback from users and analyzes its effectiveness using evaluation tools. If necessary, these evaluation results are communicated to healthcare professionals. Healthcare providers can use this information to inform future consultations and prescription changes.

[0350] 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.

[0351] This invention begins with a user inputting drug information via a terminal, which is then transmitted to a server and stored in a database. The terminal also acquires emotional data based on the user's voice, facial expressions, and text input, and transmits this data to the server.

[0352] The server analyzes this data using analytical tools, extracting the user's prescription history and medical information, and generating personalized advice based on emotional data obtained by the emotion engine. The emotion engine identifies the user's emotional state from their input and responses, and adjusts the information provided by the generation tools accordingly.

[0353] The generated information is flexibly adapted to the user's emotional state, and the adjusted content is sent to the device and presented to the user. The displayed information includes not only the effects and precautions for use of medications, but also health advice tailored to the user's emotions.

[0354] For example, if a user asks a question related to taking painkillers and the device indicates that the user's emotions are unstable, the system will suggest additional information or relaxation methods to alleviate the user's anxiety. Furthermore, the emotion engine continuously receives feedback data, and by continuously evaluating the drug's effects and the user's emotional response using evaluation tools, it is possible to provide healthcare providers with further information.

[0355] Thus, the present invention is an advanced medical support system that dynamically responds to the user's emotions and enables more holistic health management.

[0356] The following describes the processing flow.

[0357] Step 1:

[0358] The user uses a device to input the name of the prescribed medication, symptoms, and medication history. In addition, the user provides data representing their emotional state by using voice or text input on the device.

[0359] Step 2:

[0360] The terminal transmits medication information and emotional data entered by the user to the server. The data is encrypted and securely transferred to the server.

[0361] Step 3:

[0362] The server saves the received information to the database. This allows for centralized management of both past user information and newly entered data.

[0363] Step 4:

[0364] The server processes the information stored in the database using analytical tools and extracts medical information suitable for the user. Simultaneously, the emotion engine analyzes the user's emotional data and identifies their emotional state.

[0365] Step 5:

[0366] Based on the emotional state identified by the emotion engine, the server generates personalized information using generation methods. This includes detailed information about medication use and emotionally sensitive advice.

[0367] Step 6:

[0368] The server sends the generated information to the terminal. The information is specifically tailored to take into account the user's emotional state, with additional consideration to alleviate the user's anxiety.

[0369] Step 7:

[0370] The terminal displays information received from the server to the user. The user can then use this information to take their medication safely and with peace of mind.

[0371] Step 8:

[0372] The user then inputs feedback on any changes in their physical condition or emotions they experienced after taking the medication, again via the device. Changes in emotions and side effects are also recorded.

[0373] Step 9:

[0374] The server receives feedback and uses evaluation tools to analyze and evaluate the drug's effects and the user's emotional response. Necessary actions are then considered based on the results.

[0375] Step 10:

[0376] Based on the analysis results, the server sends notifications to healthcare providers as needed. This allows healthcare providers to understand the user's condition and adjust prescriptions and treatment plans as necessary.

[0377] (Example 2)

[0378] 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".

[0379] In today's healthcare environment, there is an increasing need to understand patients' conditions in real time and provide personalized health management. However, existing systems do not adequately provide information that takes into account the emotional state of the user, and new technologies are needed to improve the quality and effectiveness of medical support.

[0380] 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.

[0381] In this invention, the server includes means for providing an interface for users to obtain information using other devices; information recording means for storing data received from the devices and associating it with past information; information analysis means for analyzing the accumulated data and extracting medical-related information; emotion analysis means for identifying the user's emotional state and adjusting the content of the information provided; information generation means for generating personalized information based on the analyzed information; and means for presenting the generated information to the user and displaying a response. This enables more personalized and emotionally sensitive medical support.

[0382] "Means of providing an interface" refers to a system component that provides an intermediate function for users to obtain information using other devices.

[0383] An "information recording means" is a component of a system that has the function of storing received data and associating it with past information.

[0384] An "information analysis tool" is a component of a system that has the function of analyzing accumulated data and extracting medical-related information.

[0385] "Emotional analysis means" refers to a system component that has an analytical function to identify the emotional state of a user and adjust the content of the information provided accordingly.

[0386] An "information generation means" is a component of a system that has the function of generating personalized information based on analyzed information.

[0387] "Means of display" refers to the components of a system that presents generated information to the user and provides a visible response.

[0388] This invention realizes a system that includes a terminal that provides an interface for users to input drug information, and a server that analyzes the data and generates personalized advice.

[0389] The user inputs information such as the name of the medication, dosage, and timing of administration through the terminal's interface. Simultaneously, the terminal captures the user's voice and facial expressions, collecting emotional data. This data is securely transmitted to a server.

[0390] The server analyzes the received data using specialized analytical tools. Drug information is cross-referenced with a database, and the user's emotional state is analyzed using emotion analysis tools. Emotion analysis utilizes a generative AI model to generate situation-appropriate prompts while considering the user's personality and emotions.

[0391] The generated personalized advice is sent from the server to the terminal and presented to the user. This includes information on how to use medication, its effects, precautions regarding side effects, and health advice tailored to the user's emotional state. For example, if a user expresses anxiety about using painkillers, the server will suggest relaxation techniques and provide additional information to alleviate anxiety.

[0392] Examples of prompt messages include: "Based on user input, collect text, facial expression, and voice data, perform sentiment analysis, adjust the information provided based on the results, and respond to the user."

[0393] Thus, this system aims to support users in using medications with greater peace of mind by providing useful medical information, thereby improving overall health management.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] The user enters medication information through the terminal's interface. This information includes the name of the medication, dosage, and time of administration. The terminal acquires this input data and simultaneously collects emotional data using voice and facial expression sensors. The collected data is then compiled into a single data package and prepared for transmission to the server.

[0397] Step 2:

[0398] The terminal transmits the user's medication information and emotional data to the server. This transmission uses encrypted communication over the internet. The server records the received data package in a database and prepares it for analysis.

[0399] Step 3:

[0400] The server uses the received drug information to cross-reference it with a database and extract relevant information. Furthermore, it utilizes a generative AI model to perform emotion analysis and analyze the user's emotional state. This analysis uses emotion analysis tools to identify emotions from the user's voice tone and facial expression changes. The results of the analysis include drug-related information and an evaluation of the emotional state.

[0401] Step 4:

[0402] The server generates personalized advice based on extracted drug information and sentiment analysis results. Information generation is used, with a generating AI model producing prompts appropriate to the user's state. Advice is then formulated based on these prompts. The generated advice includes drug usage, effects, precautions, and health recommendations tailored to the user's emotions.

[0403] Step 5:

[0404] The server sends the generated advice to the terminal. The terminal displays the advice to the user and provides voice guidance as needed. This display operation is designed to allow the user to intuitively understand the information.

[0405] Step 6:

[0406] The user inputs feedback on the advice received into a terminal. This feedback is sent to the server via the terminal. The server analyzes the feedback data and improves the system's information provision process as needed. This evaluation mechanism enables continuous system optimization.

[0407] (Application Example 2)

[0408] 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."

[0409] Conventional health management systems provide uniform medical information and health advice without considering the emotional state of users, making it difficult to provide effective support tailored to the individual needs of each user. Furthermore, individualized support that takes emotional aspects into account is particularly required for the elderly and users who require special support, and conventional technologies have faced challenges in this regard as well.

[0410] 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.

[0411] In this invention, the server includes input management means for users to input drug information using another terminal, information recording means for storing information received from the terminal and linking it with past information and related information, information analysis means for analyzing the accumulated information and extracting medical-related data, and emotion analysis means for receiving user emotion data, analyzing it and reflecting it in personalized advice. This makes it possible to provide personalized health advice that takes into account the user's emotional state.

[0412] "Input management means" refers to a device or software that provides a function for users to input drug information using another terminal.

[0413] "Information recording means" refers to a data storage or management system for saving information received from a terminal and associating it with past information and related information.

[0414] "Information analysis tools" refer to algorithms and programs used to analyze accumulated information, extract medical-related data, and process it.

[0415] "Information generation means" refers to a process or system used to generate personalized advice based on analyzed data.

[0416] "Information display means" refers to a display screen or user interface used to present generated advice to the user.

[0417] "Emotional analysis tools" refer to systems and algorithms that receive users' emotional data, analyze it, and incorporate it into personalized advice.

[0418] In this invention, the user's device plays a crucial role. Users input drug information and health-related information using portable devices such as smartphones and tablets. Input is performed via text, voice, or facial recognition using the smartphone's camera.

[0419] First, the terminal provides an interface for receiving medication information. This information is sent to a cloud server and stored via an information recording device. The server associates the received data with past medical information and analyzes the data using an information analysis device.

[0420] Based on the analyzed data, the information generation system generates personalized health advice. This generated advice is presented to the user through the information display system. At the same time, the server uses an emotion analysis system to analyze the emotional data received from the user and incorporates it into the advice. This enables flexible responses tailored to the user's emotional state.

[0421] Specifically, services such as the Google Cloud Speech-to-Text API and AWS Rekognition are used to analyze voice and facial expression data. The analyzed emotion data is then used to optimize personalized advice.

[0422] For example, if an elderly person enters a comment such as "I'm worried about side effects," the system will automatically generate advice such as "Don't worry, we'll introduce safe ways to combine medications." An example of a prompt might be, "Based on the emotional data obtained from this user's voice and facial expression analysis, generate customized advice on medication information."

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The user enters medication information using a terminal. Input is done via text or voice through input forms or a voice recognition interface. The entered information is temporarily stored by the terminal and sent to a server for data processing.

[0426] Step 2:

[0427] The device captures the user's facial expressions with its camera and acquires emotional data. This facial data is analyzed using a facial recognition service such as AWS Rekognition to determine the user's emotional state (e.g., anxiety, reassurance). The analysis results are sent to the server.

[0428] Step 3:

[0429] The server records drug information and emotional data received from the terminal into a database. In the data recording step, the information is associated with past medical information and prepared as input data for the next analysis step.

[0430] Step 4:

[0431] The server analyzes the received information using information analysis tools. Specifically, it compares drug data with past medical information and extracts medical-related data. Natural language processing technology is used for the analysis to extract necessary information from the text data.

[0432] Step 5:

[0433] The server generates personalized health advice based on the analyzed data using an information generation mechanism. Using a generation AI model, it creates advice that reflects the user's emotional state and medication information using prompts. For example, a prompt might say, "Generate customized medication advice based on the emotional data obtained from this user's voice and facial expression analysis."

[0434] Step 6:

[0435] The server sends the generated health advice to the device. The device displays this information to the user and notifies them through the screen and audio output. The user receives personalized advice and uses it as a guide to decide on their next course of action.

[0436] 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.

[0437] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0438] 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.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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".

[0452] This invention provides a system that offers an interface allowing users to input medication information via devices such as smartphones, tablets, or personal computers. The system's core server receives user input information transmitted from the device and links it to a securely stored database, managing the data along with the user's past prescription history and related medical literature.

[0453] The server analyzes the received data and uses analytical tools to extract information about medications and symptoms. Next, it uses the generated information to create personalized feedback and advice, constructing information tailored to user needs.

[0454] Advice generated based on information submitted by the user is sent from the server to the terminal, which then displays it visually to the user. This display method allows the user to check the effects of the medication, how to take it, and precautions in real time.

[0455] Furthermore, this system includes an evaluation mechanism that allows users to send feedback to a server after taking medication, and continuously evaluates the drug's effectiveness based on that information. The evaluation results can be used to revise prescriptions and treatment methods as needed, through notification to healthcare professionals.

[0456] As a concrete example, when a user is prescribed a painkiller and enters the drug name into their terminal, that information is sent to a server. The server analyzes the latest medical literature on the drug and past user history to determine effective dosage and precautions, and presents the generated information to the user. When the user enters any changes in their physical condition after taking the medication, the system uses that information to evaluate the drug's effectiveness and notifies a medical professional if necessary. In this way, this invention efficiently connects users and healthcare providers, providing an environment where medication can be used with peace of mind.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] Users use a terminal to input information such as the names of prescribed medications, their current health condition, and their medication history. Users can also input this information directly into the terminal through a dedicated application.

[0460] Step 2:

[0461] The terminal sends user input information to the server. The data is securely transferred using a communication protocol and reaches the server via the internet.

[0462] Step 3:

[0463] The server verifies the data received from the terminal and saves it to the database. This information is managed in association with individual user accounts and used for subsequent processing.

[0464] Step 4:

[0465] The server begins analysis based on the information stored in the database. Using the analysis tools, it extracts relevant data from the input drug information, medical literature related to the user's condition, and past prescription history.

[0466] Step 5:

[0467] Based on the information obtained by the analysis tools, the server generates personalized advice using the generation tools. Drug effects, precise dosage instructions, and potential precautions are created at this stage.

[0468] Step 6:

[0469] The server sends the generated advice information to the terminal. This prepares the user to quickly obtain the information they need.

[0470] Step 7:

[0471] The terminal displays personalized advice received from the server to the user. This display method allows the user to review and understand important information regarding medication.

[0472] Step 8:

[0473] Users re-enter information about changes in their physical condition and perceived effects after taking medication via a device. User feedback is crucial to the evaluation process.

[0474] Step 9:

[0475] The server receives feedback from users and analyzes it using evaluation tools. This evaluation assesses the actual effects and side effects of the drug.

[0476] Step 10:

[0477] The server notifies healthcare providers of the evaluation results. If necessary, notifications may be sent, leading to prescription revisions or additional medical advice.

[0478] (Example 1)

[0479] 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."

[0480] Currently, many people have difficulty obtaining appropriate information and advice when using medications on their own. In particular, understanding the effects and side effects of each medication is challenging when multiple medications are prescribed. Furthermore, limited communication with doctors and pharmacists creates a need for a system that ensures patients fully understand how to take their medications and any precautions.

[0481] 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.

[0482] In this invention, the server includes means for providing an interface for a user to input drug information using a processing device, means for securely communicating the input information and transmitting it to a central control unit, and means for recording the data received by the central control unit and associating it with past prescription history and related information. This enables the user to obtain accurate and personalized information about medication in real time, thereby reducing anxiety about taking medication.

[0483] "User" refers to a user who uses the system to input and process drug information.

[0484] A "processing device" is an electronic device used by users to input drug information, and includes smartphones, tablets, and personal computers.

[0485] A "central control unit" refers to the core server of a system that receives, stores, and analyzes information transmitted from processing units.

[0486] A "recording medium" refers to a database used to securely store data received by a central control unit and to associate it with past history and related information.

[0487] "Analysis means" refers to technologies and algorithms for analyzing received and stored data and extracting medically relevant information.

[0488] "Generation means" refers to a process or apparatus for generating personalized feedback based on information extracted by analysis means.

[0489] "Display means" refers to interfaces or devices used to visually present generated feedback information to the user.

[0490] This invention provides a system that allows users to efficiently and safely manage their medication information. Users input information such as the name of the medication and how to use it into an interface using a terminal such as a smartphone, tablet, or personal computer. This data is transmitted from the terminal to a server, which is a central control unit.

[0491] The server securely transmits received information using encrypted communication technology and stores it in a database. This database is structured based on the user's past prescription history and relevant medical literature, and manages the received data by appropriately linking it. In addition, to analyze the received data, the server uses analytical methods such as natural language processing technology and generative AI models to extract detailed information about medications and symptoms.

[0492] The analyzed information is generated as personalized feedback by the server. This generation method provides personalized advice based on the user's past usage history and current status. The generated feedback is sent to the device and visually presented to the user via the device's display. In this way, the user can check important information such as effective medication use and precautions in real time.

[0493] For example, if a user enters information about a painkiller, the server analyzes the latest literature on that drug and the user's past history to determine the appropriate dosage and precautions. This information is generated as feedback and presented to the user using prompts such as, "Please tell me the effective way to take this painkiller and what precautions to take."

[0494] Furthermore, after users take their medication, they send feedback to the server about changes in their physical condition. The server analyzes this data and continuously evaluates the drug's effectiveness. The evaluation results are notified to healthcare professionals as needed to help optimize treatment and prescriptions.

[0495] In this way, the system connects users and healthcare providers, providing an environment that supports safe and effective medication management.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The user enters medication information.

[0499] Users input information such as drug name, dosage, and administration schedule using an interface on a device such as a smartphone or computer. The device formats the entered information and prepares it for transmission to the server.

[0500] Step 2:

[0501] The terminal sends the input data to the server.

[0502] The terminal uses encrypted communication to send input data to the server. This data includes drug names and related information, and is treated as basic data for analysis by the server.

[0503] Step 3:

[0504] The server receives the data and saves it to the database.

[0505] The server first verifies the received drug information to confirm its effectiveness. The verified data is securely stored in a database, linked to the user's past prescription history and relevant medical literature.

[0506] Step 4:

[0507] The server analyzes the data and extracts the information.

[0508] The server analyzes data based on information stored in the database, using generative AI models and natural language processing techniques. This analysis extracts detailed information such as drug effects, side effects, and appropriate dosage. The input is information from the database, and the output is analyzed medical-related data.

[0509] Step 5:

[0510] The server generates personalized feedback.

[0511] Based on the extracted information, the server generates feedback tailored to the user's specific needs. This process generates personalized advice based on the user's past usage history and specific symptoms.

[0512] Step 6:

[0513] The server sends the generated feedback to the terminal.

[0514] The generated feedback is sent from the server to the device, making it easily viewable by the user. This allows the user to visually check the feedback content on their device.

[0515] Step 7:

[0516] The system receives and displays user feedback.

[0517] The device displays received feedback and provides the user with information on the effects of the medication, how to take it, and precautions. This allows the user to manage their medication appropriately.

[0518] Step 8:

[0519] Users provide feedback after using the product.

[0520] After using the medication, the user re-enters feedback on any changes in their physical condition or the effects of the drug into the device.

[0521] Step 9:

[0522] The device sends feedback data to the server.

[0523] The terminal sends the input feedback data to the server, enabling continuous drug evaluation.

[0524] Step 10:

[0525] The server analyzes the feedback and notifies medical professionals as needed.

[0526] The server analyzes the received feedback data to evaluate the drug's effectiveness and side effects. If necessary, it notifies healthcare professionals of the results to support the provision of appropriate prescriptions and treatments.

[0527] (Application Example 1)

[0528] 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."

[0529] Understanding information about drug usage and side effects, and taking medication appropriately, is difficult for the elderly and patients who need to take multiple medications. Furthermore, there is a lack of means to accurately record changes in physical condition after taking medication and to efficiently share this information with healthcare providers. Therefore, there is a need to create an environment where users can use medication with peace of mind.

[0530] 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.

[0531] In this invention, the server includes means for providing an interface for users to input drug information using another terminal; storage means for storing data received from the terminal and associating it with past data; analysis means for analyzing the accumulated data and extracting health-related information; display means for presenting the generated information on the user's mobile terminal; evaluation means for recording the user's physical condition after drug intake and performing an evaluation based on the recorded information; and communication means for notifying healthcare professionals of the evaluation results. This enables users to understand the effects and precautions of drugs in real time and to manage their medication appropriately. Furthermore, it facilitates information sharing with healthcare providers and enables support for safe and effective drug use.

[0532] A "terminal" is an external device used by users to input or receive information.

[0533] "Memory means" refers to a device or method that has the function of securely storing received data and associating it with past information.

[0534] "Analysis methods" refer to the process of extracting health-related information based on accumulated data and conducting detailed analysis.

[0535] "Display means" refers to a device or method for visually presenting generated information to a user.

[0536] "Evaluation methods" refer to a function that analyzes the physical condition recorded by the user after taking medication, evaluates the results, and provides improvement measures as needed.

[0537] "Communication methods" refer to methods of transmitting information to notify healthcare professionals of evaluation results.

[0538] This system is designed for the management and analysis of users' medication information. It primarily utilizes devices such as smartphones, cloud servers, and analysis software.

[0539] The server receives medication information from the terminal and securely stores that data. The received data is stored in cloud services such as AWS and Google Cloud and linked to past medication history. Data analysis is also performed using libraries such as Python's Pandas and Scikit-learn. In the analysis process, the latest health-related information regarding the drug's effects and side effects is extracted, and the generated personalized information is sent to the terminal.

[0540] The terminal plays the role of visually displaying the generated information to the user. A user interface is built using React Native and other technologies to provide the generated information in an easy-to-use format. Users input their physical condition after taking medication using the terminal, and this information is sent back to the server for evaluation.

[0541] For example, when a user takes "Medication A," notifications regarding the dosage schedule and warnings about side effects are sent via the application. The user records their physical condition after taking the medication in the app, and this data is sent to a server and shared with healthcare providers as reference information for their next medical consultation.

[0542] When a generative AI model is used to generate information, the following prompts can be used:

[0543] "Based on the medication information entered by the user, generate feedback that combines past medication history with the latest research findings."

[0544] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0545] Step 1:

[0546] Users input medication information using a smartphone or other device. This input includes information such as the name of the drug, dosage, and time of administration. The entered data is formatted on the device and then converted into a format that can be sent to the server.

[0547] Step 2:

[0548] The server receives medication information sent from the terminal. The received data is stored in a database on AWS or Google Cloud. Here, data processing is performed to link past medication history with current medication information.

[0549] Step 3:

[0550] The server analyzes the stored data. This analysis uses Python's Pandas and Scikit-learn to extract information about drug effects and side effects based on the latest medical literature and data. The analysis results are generated as personalized feedback for each user.

[0551] Step 4:

[0552] A generative AI model is used to generate more detailed feedback from the analysis results. The prompt message "Generate feedback combining past medication history and the latest research findings based on the medication information entered by the user" is used. The generated feedback is stored on the server.

[0553] Step 5:

[0554] The server sends the generated feedback to the device. The device receives this information and displays it visually on the user interface. Based on this display, the user can check their medication schedule and precautions.

[0555] Step 6:

[0556] After taking the medication, the user enters any changes in their physical condition or any side effects into the device. This feedback information is then sent back to the server.

[0557] Step 7:

[0558] The server receives post-medication feedback from users and analyzes its effectiveness using evaluation tools. If necessary, these evaluation results are communicated to healthcare professionals. Healthcare providers can use this information to inform future consultations and prescription changes.

[0559] 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.

[0560] This invention begins with a user inputting drug information via a terminal, which is then transmitted to a server and stored in a database. The terminal also acquires emotional data based on the user's voice, facial expressions, and text input, and transmits this data to the server.

[0561] The server analyzes this data using analytical tools, extracting the user's prescription history and medical information, and generating personalized advice based on emotional data obtained by the emotion engine. The emotion engine identifies the user's emotional state from their input and responses, and adjusts the information provided by the generation tools accordingly.

[0562] The generated information is flexibly adapted to the user's emotional state, and the adjusted content is sent to the device and presented to the user. The displayed information includes not only the effects and precautions for use of medications, but also health advice tailored to the user's emotions.

[0563] For example, if a user asks a question related to taking painkillers and the device indicates that the user's emotions are unstable, the system will suggest additional information or relaxation methods to alleviate the user's anxiety. Furthermore, the emotion engine continuously receives feedback data, and by continuously evaluating the drug's effects and the user's emotional response using evaluation tools, it is possible to provide healthcare providers with further information.

[0564] Thus, the present invention is an advanced medical support system that dynamically responds to the user's emotions and enables more holistic health management.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The user uses a device to input the name of the prescribed medication, symptoms, and medication history. In addition, the user provides data representing their emotional state by using voice or text input on the device.

[0568] Step 2:

[0569] The terminal transmits medication information and emotional data entered by the user to the server. The data is encrypted and securely transferred to the server.

[0570] Step 3:

[0571] The server saves the received information to the database. This allows for centralized management of both past user information and newly entered data.

[0572] Step 4:

[0573] The server processes the information stored in the database using analytical tools and extracts medical information suitable for the user. Simultaneously, the emotion engine analyzes the user's emotional data and identifies their emotional state.

[0574] Step 5:

[0575] Based on the emotional state identified by the emotion engine, the server generates personalized information using generation methods. This includes detailed information about medication use and emotionally sensitive advice.

[0576] Step 6:

[0577] The server sends the generated information to the terminal. The information is specifically tailored to take into account the user's emotional state, with additional consideration to alleviate the user's anxiety.

[0578] Step 7:

[0579] The terminal displays information received from the server to the user. The user can then use this information to take their medication safely and with peace of mind.

[0580] Step 8:

[0581] The user then inputs feedback on any changes in their physical condition or emotions they experienced after taking the medication, again via the device. Changes in emotions and side effects are also recorded.

[0582] Step 9:

[0583] The server receives feedback and uses evaluation tools to analyze and evaluate the drug's effects and the user's emotional response. Necessary actions are then considered based on the results.

[0584] Step 10:

[0585] Based on the analysis results, the server sends notifications to healthcare providers as needed. This allows healthcare providers to understand the user's condition and adjust prescriptions and treatment plans as necessary.

[0586] (Example 2)

[0587] 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."

[0588] In today's healthcare environment, there is an increasing need to understand patients' conditions in real time and provide personalized health management. However, existing systems do not adequately provide information that takes into account the emotional state of the user, and new technologies are needed to improve the quality and effectiveness of medical support.

[0589] 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.

[0590] In this invention, the server includes means for providing an interface for users to obtain information using other devices; information recording means for storing data received from the devices and associating it with past information; information analysis means for analyzing the accumulated data and extracting medical-related information; emotion analysis means for identifying the user's emotional state and adjusting the content of the information provided; information generation means for generating personalized information based on the analyzed information; and means for presenting the generated information to the user and displaying a response. This enables more personalized and emotionally sensitive medical support.

[0591] "Means of providing an interface" refers to a system component that provides an intermediate function for users to obtain information using other devices.

[0592] An "information recording means" is a component of a system that has the function of storing received data and associating it with past information.

[0593] An "information analysis tool" is a component of a system that has the function of analyzing accumulated data and extracting medical-related information.

[0594] "Emotional analysis means" refers to a system component that has an analytical function to identify the emotional state of a user and adjust the content of the information provided accordingly.

[0595] An "information generation means" is a component of a system that has the function of generating personalized information based on analyzed information.

[0596] "Means of display" refers to the components of a system that presents generated information to the user and provides a visible response.

[0597] This invention realizes a system that includes a terminal that provides an interface for users to input drug information, and a server that analyzes the data and generates personalized advice.

[0598] The user inputs information such as the name of the medication, dosage, and timing of administration through the terminal's interface. Simultaneously, the terminal captures the user's voice and facial expressions, collecting emotional data. This data is securely transmitted to a server.

[0599] The server analyzes the received data using specialized analytical tools. Drug information is cross-referenced with a database, and the user's emotional state is analyzed using emotion analysis tools. Emotion analysis utilizes a generative AI model to generate situation-appropriate prompts while considering the user's personality and emotions.

[0600] The generated personalized advice is sent from the server to the terminal and presented to the user. This includes information on how to use medication, its effects, precautions regarding side effects, and health advice tailored to the user's emotional state. For example, if a user expresses anxiety about using painkillers, the server will suggest relaxation techniques and provide additional information to alleviate anxiety.

[0601] Examples of prompt messages include: "Based on user input, collect text, facial expression, and voice data, perform sentiment analysis, adjust the information provided based on the results, and respond to the user."

[0602] Thus, this system aims to support users in using medications with greater peace of mind by providing useful medical information, thereby improving overall health management.

[0603] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0604] Step 1:

[0605] The user enters medication information through the terminal's interface. This information includes the name of the medication, dosage, and time of administration. The terminal acquires this input data and simultaneously collects emotional data using voice and facial expression sensors. The collected data is then compiled into a single data package and prepared for transmission to the server.

[0606] Step 2:

[0607] The terminal transmits the user's medication information and emotional data to the server. This transmission uses encrypted communication over the internet. The server records the received data package in a database and prepares it for analysis.

[0608] Step 3:

[0609] The server uses the received drug information to cross-reference it with a database and extract relevant information. Furthermore, it utilizes a generative AI model to perform emotion analysis and analyze the user's emotional state. This analysis uses emotion analysis tools to identify emotions from the user's voice tone and facial expression changes. The results of the analysis include drug-related information and an evaluation of the emotional state.

[0610] Step 4:

[0611] The server generates personalized advice based on extracted drug information and sentiment analysis results. Information generation is used, with a generating AI model producing prompts appropriate to the user's state. Advice is then formulated based on these prompts. The generated advice includes drug usage, effects, precautions, and health recommendations tailored to the user's emotions.

[0612] Step 5:

[0613] The server sends the generated advice to the terminal. The terminal displays the advice to the user and provides voice guidance as needed. This display operation is designed to allow the user to intuitively understand the information.

[0614] Step 6:

[0615] The user inputs feedback on the advice received into a terminal. This feedback is sent to the server via the terminal. The server analyzes the feedback data and improves the system's information provision process as needed. This evaluation mechanism enables continuous system optimization.

[0616] (Application Example 2)

[0617] 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."

[0618] Conventional health management systems provide uniform medical information and health advice without considering the emotional state of users, making it difficult to provide effective support tailored to the individual needs of each user. Furthermore, individualized support that takes emotional aspects into account is particularly required for the elderly and users who require special support, and conventional technologies have faced challenges in this regard as well.

[0619] 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.

[0620] In this invention, the server includes input management means for users to input drug information using another terminal, information recording means for storing information received from the terminal and linking it with past information and related information, information analysis means for analyzing the accumulated information and extracting medical-related data, and emotion analysis means for receiving user emotion data, analyzing it and reflecting it in personalized advice. This makes it possible to provide personalized health advice that takes into account the user's emotional state.

[0621] "Input management means" refers to a device or software that provides a function for users to input drug information using another terminal.

[0622] "Information recording means" refers to a data storage or management system for saving information received from a terminal and associating it with past information and related information.

[0623] "Information analysis tools" refer to algorithms and programs used to analyze accumulated information, extract medical-related data, and process it.

[0624] "Information generation means" refers to a process or system used to generate personalized advice based on analyzed data.

[0625] "Information display means" refers to a display screen or user interface used to present generated advice to the user.

[0626] "Emotional analysis tools" refer to systems and algorithms that receive users' emotional data, analyze it, and incorporate it into personalized advice.

[0627] In this invention, the user's device plays a crucial role. Users input drug information and health-related information using portable devices such as smartphones and tablets. Input is performed via text, voice, or facial recognition using the smartphone's camera.

[0628] First, the terminal provides an interface for receiving medication information. This information is sent to a cloud server and stored via an information recording device. The server associates the received data with past medical information and analyzes the data using an information analysis device.

[0629] Based on the analyzed data, the information generation system generates personalized health advice. This generated advice is presented to the user through the information display system. At the same time, the server uses an emotion analysis system to analyze the emotional data received from the user and incorporates it into the advice. This enables flexible responses tailored to the user's emotional state.

[0630] Specifically, services such as the Google Cloud Speech-to-Text API and AWS Rekognition are used to analyze voice and facial expression data. The analyzed emotion data is then used to optimize personalized advice.

[0631] For example, if an elderly person enters a comment such as "I'm worried about side effects," the system will automatically generate advice such as "Don't worry, we'll introduce safe ways to combine medications." An example of a prompt might be, "Based on the emotional data obtained from this user's voice and facial expression analysis, generate customized advice on medication information."

[0632] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0633] Step 1:

[0634] The user enters medication information using a terminal. Input is done via text or voice through input forms or a voice recognition interface. The entered information is temporarily stored by the terminal and sent to a server for data processing.

[0635] Step 2:

[0636] The device captures the user's facial expressions with its camera and acquires emotional data. This facial data is analyzed using a facial recognition service such as AWS Rekognition to determine the user's emotional state (e.g., anxiety, reassurance). The analysis results are sent to the server.

[0637] Step 3:

[0638] The server records drug information and emotional data received from the terminal into a database. In the data recording step, the information is associated with past medical information and prepared as input data for the next analysis step.

[0639] Step 4:

[0640] The server analyzes the received information using information analysis tools. Specifically, it compares drug data with past medical information and extracts medical-related data. Natural language processing technology is used for the analysis to extract necessary information from the text data.

[0641] Step 5:

[0642] The server generates personalized health advice based on the analyzed data using an information generation mechanism. Using a generation AI model, it creates advice that reflects the user's emotional state and medication information using prompts. For example, a prompt might say, "Generate customized medication advice based on the emotional data obtained from this user's voice and facial expression analysis."

[0643] Step 6:

[0644] The server sends the generated health advice to the device. The device displays this information to the user and notifies them through the screen and audio output. The user receives personalized advice and uses it as a guide to decide on their next course of action.

[0645] 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.

[0646] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] This invention provides a system that offers an interface allowing users to input medication information via devices such as smartphones, tablets, or personal computers. The system's core server receives user input information transmitted from the device and links it to a securely stored database, managing the data along with the user's past prescription history and related medical literature.

[0663] The server analyzes the received data and uses analytical tools to extract information about medications and symptoms. Next, it uses the generated information to create personalized feedback and advice, constructing information tailored to user needs.

[0664] Advice generated based on information submitted by the user is sent from the server to the terminal, which then displays it visually to the user. This display method allows the user to check the effects of the medication, how to take it, and precautions in real time.

[0665] Furthermore, this system includes an evaluation mechanism that allows users to send feedback to a server after taking medication, and continuously evaluates the drug's effectiveness based on that information. The evaluation results can be used to revise prescriptions and treatment methods as needed, through notification to healthcare professionals.

[0666] As a concrete example, when a user is prescribed a painkiller and enters the drug name into their terminal, that information is sent to a server. The server analyzes the latest medical literature on the drug and past user history to determine effective dosage and precautions, and presents the generated information to the user. When the user enters any changes in their physical condition after taking the medication, the system uses that information to evaluate the drug's effectiveness and notifies a medical professional if necessary. In this way, this invention efficiently connects users and healthcare providers, providing an environment where medication can be used with peace of mind.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] Users use a terminal to input information such as the names of prescribed medications, their current health condition, and their medication history. Users can also input this information directly into the terminal through a dedicated application.

[0670] Step 2:

[0671] The terminal sends user input information to the server. The data is securely transferred using a communication protocol and reaches the server via the internet.

[0672] Step 3:

[0673] The server verifies the data received from the terminal and saves it to the database. This information is managed in association with individual user accounts and used for subsequent processing.

[0674] Step 4:

[0675] The server begins analysis based on the information stored in the database. Using the analysis tools, it extracts relevant data from the input drug information, medical literature related to the user's condition, and past prescription history.

[0676] Step 5:

[0677] Based on the information obtained by the analysis tools, the server generates personalized advice using the generation tools. Drug effects, precise dosage instructions, and potential precautions are created at this stage.

[0678] Step 6:

[0679] The server sends the generated advice information to the terminal. This prepares the user to quickly obtain the information they need.

[0680] Step 7:

[0681] The terminal displays personalized advice received from the server to the user. This display method allows the user to review and understand important information regarding medication.

[0682] Step 8:

[0683] Users re-enter information about changes in their physical condition and perceived effects after taking medication via a device. User feedback is crucial to the evaluation process.

[0684] Step 9:

[0685] The server receives feedback from users and analyzes it using evaluation tools. This evaluation assesses the actual effects and side effects of the drug.

[0686] Step 10:

[0687] The server notifies healthcare providers of the evaluation results. If necessary, notifications may be sent, leading to prescription revisions or additional medical advice.

[0688] (Example 1)

[0689] 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".

[0690] Currently, many people have difficulty obtaining appropriate information and advice when using medications on their own. In particular, understanding the effects and side effects of each medication is challenging when multiple medications are prescribed. Furthermore, limited communication with doctors and pharmacists creates a need for a system that ensures patients fully understand how to take their medications and any precautions.

[0691] 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.

[0692] In this invention, the server includes means for providing an interface for a user to input drug information using a processing device, means for securely communicating the input information and transmitting it to a central control unit, and means for recording the data received by the central control unit and associating it with past prescription history and related information. This enables the user to obtain accurate and personalized information about medication in real time, thereby reducing anxiety about taking medication.

[0693] "User" refers to a user who uses the system to input and process drug information.

[0694] A "processing device" is an electronic device used by users to input drug information, and includes smartphones, tablets, and personal computers.

[0695] A "central control unit" refers to the core server of a system that receives, stores, and analyzes information transmitted from processing units.

[0696] A "recording medium" refers to a database used to securely store data received by a central control unit and to associate it with past history and related information.

[0697] "Analysis means" refers to technologies and algorithms for analyzing received and stored data and extracting medically relevant information.

[0698] "Generation means" refers to a process or apparatus for generating personalized feedback based on information extracted by analysis means.

[0699] "Display means" refers to interfaces or devices used to visually present generated feedback information to the user.

[0700] This invention provides a system that allows users to efficiently and safely manage their medication information. Users input information such as the name of the medication and how to use it into an interface using a terminal such as a smartphone, tablet, or personal computer. This data is transmitted from the terminal to a server, which is a central control unit.

[0701] The server securely transmits received information using encrypted communication technology and stores it in a database. This database is structured based on the user's past prescription history and relevant medical literature, and manages the received data by appropriately linking it. In addition, to analyze the received data, the server uses analytical methods such as natural language processing technology and generative AI models to extract detailed information about medications and symptoms.

[0702] The analyzed information is generated as personalized feedback by the server. This generation method provides personalized advice based on the user's past usage history and current status. The generated feedback is sent to the device and visually presented to the user via the device's display. In this way, the user can check important information such as effective medication use and precautions in real time.

[0703] For example, if a user enters information about a painkiller, the server analyzes the latest literature on that drug and the user's past history to determine the appropriate dosage and precautions. This information is generated as feedback and presented to the user using prompts such as, "Please tell me the effective way to take this painkiller and what precautions to take."

[0704] Furthermore, after users take their medication, they send feedback to the server about changes in their physical condition. The server analyzes this data and continuously evaluates the drug's effectiveness. The evaluation results are notified to healthcare professionals as needed to help optimize treatment and prescriptions.

[0705] In this way, the system connects users and healthcare providers, providing an environment that supports safe and effective medication management.

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] The user enters medication information.

[0709] Users input information such as drug name, dosage, and administration schedule using an interface on a device such as a smartphone or computer. The device formats the entered information and prepares it for transmission to the server.

[0710] Step 2:

[0711] The terminal sends the input data to the server.

[0712] The terminal uses encrypted communication to send input data to the server. This data includes drug names and related information, and is treated as basic data for analysis by the server.

[0713] Step 3:

[0714] The server receives the data and saves it to the database.

[0715] The server first verifies the received drug information to confirm its effectiveness. The verified data is securely stored in a database, linked to the user's past prescription history and relevant medical literature.

[0716] Step 4:

[0717] The server analyzes the data and extracts the information.

[0718] The server analyzes data based on information stored in the database, using generative AI models and natural language processing techniques. This analysis extracts detailed information such as drug effects, side effects, and appropriate dosage. The input is information from the database, and the output is analyzed medical-related data.

[0719] Step 5:

[0720] The server generates personalized feedback.

[0721] Based on the extracted information, the server generates feedback tailored to the user's specific needs. This process generates personalized advice based on the user's past usage history and specific symptoms.

[0722] Step 6:

[0723] The server sends the generated feedback to the terminal.

[0724] The generated feedback is sent from the server to the device, making it easily viewable by the user. This allows the user to visually check the feedback content on their device.

[0725] Step 7:

[0726] The system receives and displays user feedback.

[0727] The device displays received feedback and provides the user with information on the effects of the medication, how to take it, and precautions. This allows the user to manage their medication appropriately.

[0728] Step 8:

[0729] Users provide feedback after using the product.

[0730] After using the medication, the user re-enters feedback on any changes in their physical condition or the effects of the drug into the device.

[0731] Step 9:

[0732] The device sends feedback data to the server.

[0733] The terminal sends the input feedback data to the server, enabling continuous drug evaluation.

[0734] Step 10:

[0735] The server analyzes the feedback and notifies medical professionals as needed.

[0736] The server analyzes the received feedback data to evaluate the drug's effectiveness and side effects. If necessary, it notifies healthcare professionals of the results to support the provision of appropriate prescriptions and treatments.

[0737] (Application Example 1)

[0738] 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".

[0739] Understanding information about drug usage and side effects, and taking medication appropriately, is difficult for the elderly and patients who need to take multiple medications. Furthermore, there is a lack of means to accurately record changes in physical condition after taking medication and to efficiently share this information with healthcare providers. Therefore, there is a need to create an environment where users can use medication with peace of mind.

[0740] 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.

[0741] In this invention, the server includes means for providing an interface for users to input drug information using another terminal; storage means for storing data received from the terminal and associating it with past data; analysis means for analyzing the accumulated data and extracting health-related information; display means for presenting the generated information on the user's mobile terminal; evaluation means for recording the user's physical condition after drug intake and performing an evaluation based on the recorded information; and communication means for notifying healthcare professionals of the evaluation results. This enables users to understand the effects and precautions of drugs in real time and to manage their medication appropriately. Furthermore, it facilitates information sharing with healthcare providers and enables support for safe and effective drug use.

[0742] A "terminal" is an external device used by users to input or receive information.

[0743] "Memory means" refers to a device or method that has the function of securely storing received data and associating it with past information.

[0744] "Analysis methods" refer to the process of extracting health-related information based on accumulated data and conducting detailed analysis.

[0745] "Display means" refers to a device or method for visually presenting generated information to a user.

[0746] "Evaluation methods" refer to a function that analyzes the physical condition recorded by the user after taking medication, evaluates the results, and provides improvement measures as needed.

[0747] "Communication methods" refer to methods of transmitting information to notify healthcare professionals of evaluation results.

[0748] This system is designed for the management and analysis of users' medication information. It primarily utilizes devices such as smartphones, cloud servers, and analysis software.

[0749] The server receives medication information from the terminal and securely stores that data. The received data is stored in cloud services such as AWS and Google Cloud and linked to past medication history. Data analysis is also performed using libraries such as Python's Pandas and Scikit-learn. In the analysis process, the latest health-related information regarding the drug's effects and side effects is extracted, and the generated personalized information is sent to the terminal.

[0750] The terminal plays the role of visually displaying the generated information to the user. A user interface is built using React Native and other technologies to provide the generated information in an easy-to-use format. Users input their physical condition after taking medication using the terminal, and this information is sent back to the server for evaluation.

[0751] For example, when a user takes "Medication A," notifications regarding the dosage schedule and warnings about side effects are sent via the application. The user records their physical condition after taking the medication in the app, and this data is sent to a server and shared with healthcare providers as reference information for their next medical consultation.

[0752] When a generative AI model is used to generate information, the following prompts can be used:

[0753] "Based on the medication information entered by the user, generate feedback that combines past medication history with the latest research findings."

[0754] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0755] Step 1:

[0756] Users input medication information using a smartphone or other device. This input includes information such as the name of the drug, dosage, and time of administration. The entered data is formatted on the device and then converted into a format that can be sent to the server.

[0757] Step 2:

[0758] The server receives medication information sent from the terminal. The received data is stored in a database on AWS or Google Cloud. Here, data processing is performed to link past medication history with current medication information.

[0759] Step 3:

[0760] The server analyzes the stored data. This analysis uses Python's Pandas and Scikit-learn to extract information about drug effects and side effects based on the latest medical literature and data. The analysis results are generated as personalized feedback for each user.

[0761] Step 4:

[0762] A generative AI model is used to generate more detailed feedback from the analysis results. The prompt message "Generate feedback combining past medication history and the latest research findings based on the medication information entered by the user" is used. The generated feedback is stored on the server.

[0763] Step 5:

[0764] The server sends the generated feedback to the device. The device receives this information and displays it visually on the user interface. Based on this display, the user can check their medication schedule and precautions.

[0765] Step 6:

[0766] After taking the medication, the user enters any changes in their physical condition or any side effects into the device. This feedback information is then sent back to the server.

[0767] Step 7:

[0768] The server receives post-medication feedback from users and analyzes its effectiveness using evaluation tools. If necessary, these evaluation results are communicated to healthcare professionals. Healthcare providers can use this information to inform future consultations and prescription changes.

[0769] 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.

[0770] This invention begins with a user inputting drug information via a terminal, which is then transmitted to a server and stored in a database. The terminal also acquires emotional data based on the user's voice, facial expressions, and text input, and transmits this data to the server.

[0771] The server analyzes this data using analytical tools, extracting the user's prescription history and medical information, and generating personalized advice based on emotional data obtained by the emotion engine. The emotion engine identifies the user's emotional state from their input and responses, and adjusts the information provided by the generation tools accordingly.

[0772] The generated information is flexibly adapted to the user's emotional state, and the adjusted content is sent to the device and presented to the user. The displayed information includes not only the effects and precautions for use of medications, but also health advice tailored to the user's emotions.

[0773] For example, if a user asks a question related to taking painkillers and the device indicates that the user's emotions are unstable, the system will suggest additional information or relaxation methods to alleviate the user's anxiety. Furthermore, the emotion engine continuously receives feedback data, and by continuously evaluating the drug's effects and the user's emotional response using evaluation tools, it is possible to provide healthcare providers with further information.

[0774] Thus, the present invention is an advanced medical support system that dynamically responds to the user's emotions and enables more holistic health management.

[0775] The following describes the processing flow.

[0776] Step 1:

[0777] The user uses a device to input the name of the prescribed medication, symptoms, and medication history. In addition, the user provides data representing their emotional state by using voice or text input on the device.

[0778] Step 2:

[0779] The terminal transmits medication information and emotional data entered by the user to the server. The data is encrypted and securely transferred to the server.

[0780] Step 3:

[0781] The server saves the received information to the database. This allows for centralized management of both past user information and newly entered data.

[0782] Step 4:

[0783] The server processes the information stored in the database using analytical tools and extracts medical information suitable for the user. Simultaneously, the emotion engine analyzes the user's emotional data and identifies their emotional state.

[0784] Step 5:

[0785] Based on the emotional state identified by the emotion engine, the server generates personalized information using generation methods. This includes detailed information about medication use and emotionally sensitive advice.

[0786] Step 6:

[0787] The server sends the generated information to the terminal. The information is specifically tailored to take into account the user's emotional state, with additional consideration to alleviate the user's anxiety.

[0788] Step 7:

[0789] The terminal displays information received from the server to the user. The user can then use this information to take their medication safely and with peace of mind.

[0790] Step 8:

[0791] The user then inputs feedback on any changes in their physical condition or emotions they experienced after taking the medication, again via the device. Changes in emotions and side effects are also recorded.

[0792] Step 9:

[0793] The server receives feedback and uses evaluation tools to analyze and evaluate the drug's effects and the user's emotional response. Necessary actions are then considered based on the results.

[0794] Step 10:

[0795] Based on the analysis results, the server sends notifications to healthcare providers as needed. This allows healthcare providers to understand the user's condition and adjust prescriptions and treatment plans as necessary.

[0796] (Example 2)

[0797] 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".

[0798] In today's healthcare environment, there is an increasing need to understand patients' conditions in real time and provide personalized health management. However, existing systems do not adequately provide information that takes into account the emotional state of the user, and new technologies are needed to improve the quality and effectiveness of medical support.

[0799] 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.

[0800] In this invention, the server includes means for providing an interface for users to obtain information using other devices; information recording means for storing data received from the devices and associating it with past information; information analysis means for analyzing the accumulated data and extracting medical-related information; emotion analysis means for identifying the user's emotional state and adjusting the content of the information provided; information generation means for generating personalized information based on the analyzed information; and means for presenting the generated information to the user and displaying a response. This enables more personalized and emotionally sensitive medical support.

[0801] "Means of providing an interface" refers to a system component that provides an intermediate function for users to obtain information using other devices.

[0802] An "information recording means" is a component of a system that has the function of storing received data and associating it with past information.

[0803] An "information analysis tool" is a component of a system that has the function of analyzing accumulated data and extracting medical-related information.

[0804] "Emotional analysis means" refers to a system component that has an analytical function to identify the emotional state of a user and adjust the content of the information provided accordingly.

[0805] An "information generation means" is a component of a system that has the function of generating personalized information based on analyzed information.

[0806] "Means of display" refers to the components of a system that presents generated information to the user and provides a visible response.

[0807] This invention realizes a system that includes a terminal that provides an interface for users to input drug information, and a server that analyzes the data and generates personalized advice.

[0808] The user inputs information such as the name of the medication, dosage, and timing of administration through the terminal's interface. Simultaneously, the terminal captures the user's voice and facial expressions, collecting emotional data. This data is securely transmitted to a server.

[0809] The server analyzes the received data using specialized analytical tools. Drug information is cross-referenced with a database, and the user's emotional state is analyzed using emotion analysis tools. Emotion analysis utilizes a generative AI model to generate situation-appropriate prompts while considering the user's personality and emotions.

[0810] The generated personalized advice is sent from the server to the terminal and presented to the user. This includes information on how to use medication, its effects, precautions regarding side effects, and health advice tailored to the user's emotional state. For example, if a user expresses anxiety about using painkillers, the server will suggest relaxation techniques and provide additional information to alleviate anxiety.

[0811] Examples of prompt messages include: "Based on user input, collect text, facial expression, and voice data, perform sentiment analysis, adjust the information provided based on the results, and respond to the user."

[0812] Thus, this system aims to support users in using medications with greater peace of mind by providing useful medical information, thereby improving overall health management.

[0813] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0814] Step 1:

[0815] The user enters medication information through the terminal's interface. This information includes the name of the medication, dosage, and time of administration. The terminal acquires this input data and simultaneously collects emotional data using voice and facial expression sensors. The collected data is then compiled into a single data package and prepared for transmission to the server.

[0816] Step 2:

[0817] The terminal transmits the user's medication information and emotional data to the server. This transmission uses encrypted communication over the internet. The server records the received data package in a database and prepares it for analysis.

[0818] Step 3:

[0819] The server uses the received drug information to cross-reference it with a database and extract relevant information. Furthermore, it utilizes a generative AI model to perform emotion analysis and analyze the user's emotional state. This analysis uses emotion analysis tools to identify emotions from the user's voice tone and facial expression changes. The results of the analysis include drug-related information and an evaluation of the emotional state.

[0820] Step 4:

[0821] The server generates personalized advice based on extracted drug information and sentiment analysis results. Information generation is used, with a generating AI model producing prompts appropriate to the user's state. Advice is then formulated based on these prompts. The generated advice includes drug usage, effects, precautions, and health recommendations tailored to the user's emotions.

[0822] Step 5:

[0823] The server sends the generated advice to the terminal. The terminal displays the advice to the user and provides voice guidance as needed. This display operation is designed to allow the user to intuitively understand the information.

[0824] Step 6:

[0825] The user inputs feedback on the advice received into a terminal. This feedback is sent to the server via the terminal. The server analyzes the feedback data and improves the system's information provision process as needed. This evaluation mechanism enables continuous system optimization.

[0826] (Application Example 2)

[0827] 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".

[0828] Conventional health management systems provide uniform medical information and health advice without considering the emotional state of users, making it difficult to provide effective support tailored to the individual needs of each user. Furthermore, individualized support that takes emotional aspects into account is particularly required for the elderly and users who require special support, and conventional technologies have faced challenges in this regard as well.

[0829] 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.

[0830] In this invention, the server includes input management means for users to input drug information using another terminal, information recording means for storing information received from the terminal and linking it with past information and related information, information analysis means for analyzing the accumulated information and extracting medical-related data, and emotion analysis means for receiving user emotion data, analyzing it and reflecting it in personalized advice. This makes it possible to provide personalized health advice that takes into account the user's emotional state.

[0831] "Input management means" refers to a device or software that provides a function for users to input drug information using another terminal.

[0832] "Information recording means" refers to a data storage or management system for saving information received from a terminal and associating it with past information and related information.

[0833] "Information analysis tools" refer to algorithms and programs used to analyze accumulated information, extract medical-related data, and process it.

[0834] "Information generation means" refers to a process or system used to generate personalized advice based on analyzed data.

[0835] "Information display means" refers to a display screen or user interface used to present generated advice to the user.

[0836] "Emotional analysis tools" refer to systems and algorithms that receive users' emotional data, analyze it, and incorporate it into personalized advice.

[0837] In this invention, the user's device plays a crucial role. Users input drug information and health-related information using portable devices such as smartphones and tablets. Input is performed via text, voice, or facial recognition using the smartphone's camera.

[0838] First, the terminal provides an interface for receiving medication information. This information is sent to a cloud server and stored via an information recording device. The server associates the received data with past medical information and analyzes the data using an information analysis device.

[0839] Based on the analyzed data, the information generation system generates personalized health advice. This generated advice is presented to the user through the information display system. At the same time, the server uses an emotion analysis system to analyze the emotional data received from the user and incorporates it into the advice. This enables flexible responses tailored to the user's emotional state.

[0840] Specifically, services such as the Google Cloud Speech-to-Text API and AWS Rekognition are used to analyze voice and facial expression data. The analyzed emotion data is then used to optimize personalized advice.

[0841] For example, if an elderly person enters a comment such as "I'm worried about side effects," the system will automatically generate advice such as "Don't worry, we'll introduce safe ways to combine medications." An example of a prompt might be, "Based on the emotional data obtained from this user's voice and facial expression analysis, generate customized advice on medication information."

[0842] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0843] Step 1:

[0844] The user enters medication information using a terminal. Input is done via text or voice through input forms or a voice recognition interface. The entered information is temporarily stored by the terminal and sent to a server for data processing.

[0845] Step 2:

[0846] The device captures the user's facial expressions with its camera and acquires emotional data. This facial data is analyzed using a facial recognition service such as AWS Rekognition to determine the user's emotional state (e.g., anxiety, reassurance). The analysis results are sent to the server.

[0847] Step 3:

[0848] The server records drug information and emotional data received from the terminal into a database. In the data recording step, the information is associated with past medical information and prepared as input data for the next analysis step.

[0849] Step 4:

[0850] The server analyzes the received information using information analysis tools. Specifically, it compares drug data with past medical information and extracts medical-related data. Natural language processing technology is used for the analysis to extract necessary information from the text data.

[0851] Step 5:

[0852] The server generates personalized health advice based on the analyzed data using an information generation mechanism. Using a generation AI model, it creates advice that reflects the user's emotional state and medication information using prompts. For example, a prompt might say, "Generate customized medication advice based on the emotional data obtained from this user's voice and facial expression analysis."

[0853] Step 6:

[0854] The server sends the generated health advice to the device. The device displays this information to the user and notifies them through the screen and audio output. The user receives personalized advice and uses it as a guide to decide on their next course of action.

[0855] 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.

[0856] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] Means for providing an interface for users to input drug information using other devices,

[0879] A database means for storing data received from the aforementioned device and associating it with past information,

[0880] An analytical means for analyzing accumulated data and extracting medical-related information,

[0881] A generation means that generates personalized information based on the information analyzed by the aforementioned analysis means,

[0882] A display means for presenting the generated information to the user,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising an evaluation means for receiving feedback data from users, analyzing the data to generate evaluation results, and providing notifications as necessary.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising a suggestion means for suggesting treatment methods to users by analyzing new data and medical literature.

[0888] "Example 1"

[0889] (Claim 1)

[0890] A means for providing an interface for users to input drug information using a processing device,

[0891] Means for securely communicating the input information and transmitting it to a central control unit,

[0892] The central control unit provides a recording medium for storing the received data and associating it with past prescription history and related information,

[0893] An analytical means for analyzing the aforementioned accumulated data and extracting medical-related information,

[0894] A generation means that generates information optimized for individual users based on the information generated by the analysis means,

[0895] A display means for transmitting the generated information to the user's processing device and displaying it visually,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising an evaluation means for receiving post-use feedback data from users, analyzing the data to evaluate the effectiveness of the drug, and notifying experts in relevant fields as necessary.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising a suggestion means for suggesting prescription methods to users by analyzing new data and medical literature, and presenting information based on the suggestion to the user.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] A means of providing an interface for users to input medication information using other devices,

[0904] A storage means for storing data received from the aforementioned terminal and associating it with past data,

[0905] An analytical means for analyzing accumulated data and extracting health-related information,

[0906] A generation means that generates personalized information based on the information analyzed by the aforementioned analysis means,

[0907] A display means for presenting the generated information to the user's mobile device,

[0908] An evaluation means that allows the user to record their physical condition after taking medication and to perform an evaluation based on the recorded information,

[0909] A communication method for notifying healthcare professionals of information based on the evaluation results,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, comprising: inputting information on medications taken by the user; providing specialized dosage instructions and precautions based on that information; and further providing auxiliary means for sharing information with healthcare professionals as needed.

[0913] (Claim 3)

[0914] The system according to claim 1, comprising a suggestion means for suggesting treatment methods to users by analyzing new data and health-related literature, and providing users with individual advice based on those suggestions.

[0915] "Example 2 of combining an emotion engine"

[0916] (Claim 1)

[0917] Means for providing an interface for users to obtain information using other devices,

[0918] Information recording means for storing data received from the aforementioned device and associating it with past information,

[0919] An information analysis tool that analyzes accumulated data and extracts medical-related information,

[0920] A sentiment analysis tool that identifies the user's emotional state and adjusts the content of the information provided,

[0921] Information generation means for generating personalized information based on the analyzed information,

[0922] The generated information is presented to the user and a means is displayed to show a response.

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, comprising an evaluation means for receiving feedback data from users, analyzing the data to generate evaluation results, and providing notifications as necessary.

[0926] (Claim 3)

[0927] The system according to claim 1, comprising a means for providing support to users by analyzing new information and related literature.

[0928] "Application example 2 when combining with an emotional engine"

[0929] (Claim 1)

[0930] An input management means for users to input drug information using another device,

[0931] Information recording means that stores information received from the terminal and links it with past information and related information,

[0932] An information analysis tool that analyzes accumulated information and extracts medical-related data,

[0933] Information generation means for generating personalized advice based on the analyzed data,

[0934] Information display means for presenting the generated advice to the user,

[0935] A means of emotion analysis that receives user emotion data, analyzes it, and reflects it in personalized advice,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] The system according to claim 1, comprising an evaluation means for receiving feedback information from users, analyzing that information to generate evaluation results, and providing notifications as necessary.

[0939] (Claim 3)

[0940] The system according to claim 1, comprising, in addition to a suggestion means for proposing therapies to users by analyzing new information and medical literature, an emotion adjustment means for adjusting advice based on the user's emotional state. [Explanation of Symbols]

[0941] 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 for providing an interface for users to input drug information using other devices, A database means for storing data received from the aforementioned device and associating it with past information, An analytical means for analyzing accumulated data and extracting medical-related information, A generation means that generates personalized information based on the information analyzed by the aforementioned analysis means, A display means for presenting the generated information to the user, A system that includes this.

2. The system according to claim 1, comprising an evaluation means that receives feedback data from users, analyzes the data to generate evaluation results, and provides notifications as necessary.

3. The system according to claim 1, comprising a suggestion means for suggesting treatment methods to users by analyzing new data and medical literature.

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