system

Abstract

We provide the system. [Solution] A means of receiving drug information and health information from users, A means for generating drug efficacy, usage instructions, and precautions using artificial intelligence, based on received drug information, and by cross-referencing it with relevant databases. A means of displaying the generated information in an interactive format on the user's terminal, A means of receiving additional feedback from users and evaluating the effects of a drug using artificial intelligence, A means for pharmacists to generate suggestions based on changes in the user's physical condition, 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=第23]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When elderly people or patients with multiple diseases are prescribed many medications, it is not easy to understand the efficacy, usage method, and precautions of each drug and manage them appropriately. Also, it can be difficult to individually ask questions at a pharmacy or hospital to dispel anxiety and doubts about medications. Furthermore, it requires a great deal of time and effort for pharmacists to always keep abreast of the latest pharmaceutical research and reflect it in prescriptions. Considering such a background, there is a need for a support system for efficient and safe drug therapy.

Means for Solving the Problems

[0005] This invention provides a means for receiving drug information and health information from users and comparing it with relevant databases. It then uses artificial intelligence to generate drug efficacy, usage instructions, and precautions, and provides this information to users in an interactive format. Furthermore, it receives feedback from users and individually evaluates the effects of the drugs. For pharmacists, it provides a system that automatically generates appropriate suggestions based on changes in the user's health, thereby improving work efficiency and reliability, and thus solving these problems.

[0006] "User" refers to the person who operates the system and inputs information about medications and health conditions.

[0007] "Medication information" refers to detailed information such as the name, dosage, and intended use of the prescribed medication.

[0008] "Health information" refers to information about the user's current health status and symptoms.

[0009] "Related databases" refer to a collection of information including drug efficacy, usage instructions, precautions, and the latest pharmaceutical research.

[0010] "Generative artificial intelligence" refers to artificial intelligence technology that generates natural language based on input information and produces effective dialogue.

[0011] "Dialogue format" refers to an interactive information delivery method in which communication with users takes the form of questions and answers.

[0012] "Feedback" refers to information that users report about changes in their physical condition or side effects after using medication.

[0013] A "pharmacist" is a medical professional who dispenses medications and provides guidance to users.

[0014] "Suggestions" refer to information that should be provided to pharmacists, including points to note and alternative options. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is implemented as a system for managing and evaluating drug information and health information through the exchange of information between a user, a terminal, and a server. Its specific form and processing flow are described below in natural language.

[0037] The user first uses a terminal to enter information about prescribed medications and their current health condition. This terminal includes a voice input function and is designed for easy use by the elderly and visually impaired. The terminal then sends the user's input data to the server.

[0038] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest pharmaceutical research data, allowing for the retrieval of information from all angles. Generative artificial intelligence is used to automatically generate detailed drug information in a user-friendly format.

[0039] The generated information is provided to the user interactively through the terminal. For example, if the user asks for detailed information about using "aspirin," the server will send information such as, "Aspirin is a medication that relieves pain and inflammation. It can be hard on the stomach, so it is recommended to take it after meals," and then set a prompt such as, "Is there anything else you would like to know?"

[0040] After taking medication, users input feedback into their device regarding any changes in their physical condition or side effects. The server evaluates the drug's effectiveness based on this feedback and performs further detailed information analysis. The user's information is then cross-referenced with the database to generate an individualized evaluation.

[0041] For pharmacists, the server continuously monitors changes in the user's health and automatically delivers information on points to be aware of and suggested alternatives. This allows pharmacists to respond more quickly to the user's situation and adjust medications or suggest new treatments as needed.

[0042] This system can deepen users' understanding of medication use and improve the efficiency of pharmacists' work. For example, if an adverse reaction is reported, the server analyzes the report and suggests additional gastroprotective medications to the pharmacist, thereby improving the user's medication experience. This entire process improves the quality of healthcare and provides a user-friendly support environment for both users and pharmacists.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses the device to enter the names of prescribed medications and their health condition. Input can be either text or voice; in the case of voice input, the device converts it into text data.

[0046] Step 2:

[0047] The terminal transmits the entered medication information and health status information to the server. The transmission is in data format and is sent via a secure communication channel along with each user's identification information.

[0048] Step 3:

[0049] The server analyzes the received data to identify specific drug names and symptoms. This prepares it to search for relevant pharmacological data in the database.

[0050] Step 4:

[0051] The server accesses the database to retrieve information on the efficacy, usage, precautions, and other relevant details of a specified drug. The system is designed to take into account the latest research findings and guidelines.

[0052] Step 5:

[0053] The server uses artificial intelligence to generate information for the user in natural language based on the acquired data. The generated information is presented in a conversational format to make it easy for the user to understand.

[0054] Step 6:

[0055] The server sends the generated information back to the terminal and displays it to the user. The terminal provides the user with information about the drug's effects and usage, and also displays prompts for additional questions.

[0056] Step 7:

[0057] Users input feedback on changes in their physical condition or side effects after taking medication into their device. This feedback is also sent to the server.

[0058] Step 8:

[0059] The server receives feedback information and individually evaluates the effects of the drugs. Furthermore, it compares this information with a database and updates the evaluation of each drug.

[0060] Step 9:

[0061] The server generates necessary warnings and suggestions for pharmacists and notifies them via their terminals. This allows pharmacists to review prescriptions and provide additional care as needed.

[0062] This series of steps leads to a system that supports both users and pharmacists.

[0063] (Example 1)

[0064] 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."

[0065] There is a need for a system that allows users, including the elderly and visually impaired, to easily obtain detailed information on the proper use and side effects of medications, and to receive personalized advice based on feedback on their health status. In particular, a system is needed that enables rapid and accurate health management even for users in remote locations.

[0066] 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.

[0067] In this invention, the server includes means for receiving information about medications and health status from the user; means for comparing the received medication information with a relevant database and generating information about the effects, usage, and precautions of the medication using a generating AI; and means for displaying the generated information in an interactive format on the user's communication device and responding to additional questions from the user. This enables users, including the elderly and visually impaired, to understand how to use medications appropriately according to their health condition and to effectively manage their health.

[0068] A "user" refers to a person who uses this system to input medication information and health status information, and receives guidance and advice based on that information.

[0069] "Drug information" refers to detailed information about a prescribed medication, including its name, effects, usage, and precautions.

[0070] "Health status information" refers to information about the user's current physical condition and symptoms.

[0071] A "database" refers to a collection of information that includes drug effects, usage instructions, and the latest research data.

[0072] "Generative AI" refers to artificial intelligence technology that automatically generates explanatory text in natural language in a format easily understood by humans, based on input information.

[0073] A "communication device" refers to a device used by users to input medication information and health status information, or to receive generated information.

[0074] A "health manager" is a person who monitors the health status of users and provides appropriate suggestions and guidance when necessary.

[0075] "Feedback information" refers to information provided by users regarding changes in their physical condition or side effects after using medication.

[0076] This invention is a system that enables drug management and health management through the mutual cooperation of users, terminals, and servers. Its specific form is described below.

[0077] Users first use a terminal to input medication information and their own health status. This terminal is equipped with voice recognition software that can convert voice input into text format, making it easy for the elderly and visually impaired to use.

[0078] The entered information is transmitted to the server via a communication terminal. The server uses this information to compare it with its internal database. This database contains information on the effects, usage, and precautions of various drugs. It also stores the latest drug research data, which is updated regularly. The server uses a generative AI model to automatically generate detailed drug information required by the user. Specifically, the software used is a generative AI model, which creates information based on natural language processing technology.

[0079] The generated information is displayed on the user's terminal in an interactive format. For example, if a user inputs "What precautions should I take when using aspirin?", the server generates information such as "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals," and then prompts, "Is there anything else you would like to know?"

[0080] After taking medication, users input feedback into a terminal regarding changes in their health condition and any side effects. This feedback is then sent back to the server and cross-referenced with relevant databases. In this process, the server evaluates the effectiveness of the medication based on the feedback and generates personalized advice. This information is automatically notified to healthcare managers, who can then appropriately monitor the user's health and take appropriate action as needed.

[0081] For example, if a user wants to know about the side effects of medication taken at night, the system can provide information quickly and appropriately by prompting them with a message such as, "Please tell me what precautions I should take when taking medication at night."

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user inputs medication information and health status via voice through a terminal. This input is converted into text data by speech recognition software. The input includes the names of prescribed medications and information about the user's current health condition. The output is medication information and health status information in text format.

[0085] Step 2:

[0086] The terminal sends the converted text data to the server. In this process, the text data is formatted into the appropriate format and transferred to the server via the network. The input is the text data from the terminal, and the output is the drug information and health status information that reaches the server.

[0087] Step 3:

[0088] The server compares the received information with its internal database. The database contains information on the effects, usage, and precautions of the medication. Here, the server extracts relevant information using the medication name as a keyword. The input is the user's medication information, and the output is detailed information about the medication extracted as a result of the comparison.

[0089] Step 4:

[0090] The server uses a generative AI model to generate drug information in a user-friendly format. For example, it generates detailed information such as, "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals." The input is the result of a matching process, and the output is drug information in natural language format.

[0091] Step 5:

[0092] The terminal provides the user with generated information. The information is displayed interactively, and prompts are set to accommodate additional questions from the user. Input is generated information from the server, and output is the presentation of information to the user.

[0093] Step 6:

[0094] The user provides feedback on changes in their physical condition and side effects after taking medication. This feedback is again in voice format, and the device converts it into text data. The input is the user's feedback on their physical condition, and the output is text data that is sent back to the server.

[0095] Step 7:

[0096] The server evaluates the effectiveness of the medication based on the feedback information and generates advice to be provided to healthcare managers. Here, a generative AI model is used to create individual advice, which is then sent to the healthcare manager. The input is the user's feedback information, and the output is individual advice.

[0097] Step 8:

[0098] Health managers monitor the user's health status based on advice received from the server and make necessary adjustments to prescriptions or suggest new options. The input is advice from the server, and the output is the response and guidance from the health manager.

[0099] (Application Example 1)

[0100] 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."

[0101] In the use of pharmaceuticals, there is a problem in that effective medical therapy cannot be achieved because users do not properly understand the efficacy, usage, and safety of the drugs. Furthermore, the hurdles to obtaining information are high for the elderly and visually impaired, which increases the risk of inappropriate drug use. In addition, there is the challenge that it takes time and effort for pharmacists to manually analyze user information and provide appropriate advice.

[0102] 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.

[0103] In this invention, the server includes means for receiving drug information and health information from the user, means for matching the received drug information with a related database and generating drug efficacy, usage instructions, and precautions using artificial intelligence, and means for reading product identification information in a physical store such as a pharmacy and providing the user with drug information via voice. As a result, users can easily obtain and understand detailed drug information, and it is also possible to contribute to improving the work of pharmacists.

[0104] A "user" is an individual who provides information about medications and health conditions through the system and receives detailed information and suggestions about medications.

[0105] "Drug information" refers to information about the type of drug, its effects, how to use it, precautions, etc.

[0106] "Health information" refers to data related to the user's health status and physical condition, and is used to evaluate the effectiveness and applicability of medications.

[0107] A "related database" is a source of information that stores datasets containing information such as the efficacy and usage of drugs, as well as the latest research data.

[0108] "Generative artificial intelligence" refers to AI technology that analyzes received data and provides the generated information to users in an easily understandable way.

[0109] "Product identification information" refers to data used to individually identify products handled by pharmacies and retail stores, and is usually implemented as a barcode or QR code (registered trademark).

[0110] A "physical store" refers to a physical location where customers can actually visit and purchase products.

[0111] A "pharmacist" is a professional who possesses qualifications specializing in medicine and provides medications and advice to users.

[0112] The embodiment for carrying out the invention is configured as follows: In this system, three parties are involved: a server, a terminal, and a user, each playing a specific role.

[0113] First, the user enters medication information and health information using a device. The device can be a smartphone or tablet, and in some cases, smart glasses may also be used. Because it includes a voice input function, users can provide the necessary information via voice. The device then sends the entered information to the server.

[0114] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest research data. The server utilizes a generative AI model to generate detailed drug information in a user-friendly format based on the received information.

[0115] The generated information is presented to the device in an interactive format. When using a smartphone or smart glasses, information can also be provided via voice. For example, if a user wants to know more about a pain reliever, they might receive instructions such as, "This medication relieves headaches. Please note that it is recommended to take it after meals."

[0116] In physical stores like pharmacies, drug information is instantly obtained by scanning product identification information (barcodes or QR codes). This information is also obtained via a server and provided to the user through a terminal.

[0117] Furthermore, when users report the effects and side effects of medications as feedback, the server evaluates this information and analyzes it using a generative AI model. This evaluation is also provided to pharmacists, enabling them to suggest improvements to users.

[0118] For example, if a user needs medication for stomach pain, scanning the product identification information will cause the server to provide details about the medication, and voice guidance will be provided on the terminal. An example of a prompt message might be, "Please explain this medication in detail. Please include its effects, usage instructions, and precautions."

[0119] Through these activities, the acquisition and understanding of pharmaceutical information will be facilitated, and the work of pharmacists will be supported, leading to the proposal of safer and more effective pharmaceutical therapies for users.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The user uses a terminal to input medication and health information via voice input or touch operation. This input data is collected by the terminal and sent to the server. The input consists of information dictated or selected by the user, and this is sent to the server in digital format.

[0123] Step 2:

[0124] The server compares the received drug information with the relevant database. The database stores detailed information about the drugs, and by matching this data with the user's input data, the server retrieves the relevant information. In this process, the input is drug information from the user, and the output is detailed information about the matched drugs.

[0125] Step 3:

[0126] The server uses a generative AI model to generate drug efficacy, usage instructions, and precautions that are easy for users to understand, based on the cross-referenced information. The data processing performed here involves converting the data into natural language and preparing it for interactive presentation. The input is drug details from the database, and the output is drug information presented in a user-friendly format.

[0127] Step 4:

[0128] The terminal presents the user with generated drug information received from the server. The collected information is provided to the user through voice and screen displays, guiding them to ask additional questions in an interactive format. The input is formatted drug information from the server, and the output is voice guidance and screen displays for the user.

[0129] Step 5:

[0130] After taking medication, users input feedback about changes in their physical condition into a terminal. This feedback data is sent from the terminal to the server. The input is user feedback information, and the transmitted and stored output is detailed data used for further evaluation.

[0131] Step 6:

[0132] The server uses a generated AI model based on feedback information to evaluate the effectiveness of medications and, if necessary, generates improvement suggestions for pharmacists. The input is feedback data, and the output is support information that enables pharmacists to evaluate and make suggestions.

[0133] Step 7:

[0134] The generated suggestions are sent to the pharmacist's terminal. The pharmacist then uses this information to approve or modify the suggestions as needed. The input is suggestion data from the server, and the modified output is the safe drug information and advice ultimately provided to the user.

[0135] 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.

[0136] This invention provides a system that integrates the management of drug information and the handling of user emotions through a combination of user, terminal, server, and emotion engine. This system aims to support users in using medications with confidence and to enable pharmacists to perform their duties efficiently.

[0137] The user uses the device to input information about prescribed medications, their physical condition, and their emotions. Voice input is also available, allowing for smooth information provision even in situations where touch input is difficult. The device sends this information to the server, which centrally manages and processes the data.

[0138] The server first cross-references drug information with relevant databases to extract drug efficacy, usage instructions, precautions, and other relevant information. Next, it uses an emotion engine to analyze the user's emotions. Emotion recognition technology can identify the user's mental state, such as whether they are tense, anxious, or hesitant to ask a question.

[0139] Based on insights provided by the emotion engine, the generated drug information is adaptively adjusted. For example, if a user is particularly anxious, the server will adjust the way information is presented, such as providing more detailed explanations and additional support information. Flexible dialogue tailored to the user's emotions is also provided to encourage users to ask questions with confidence.

[0140] The server also provides pharmacists with information that takes emotional data into account when making suggestions based on changes in the user's physical condition. This allows pharmacists to communicate with users while considering their psychological state, enabling them to provide more effective care. For example, if the data indicates that the user is anxious, the pharmacist can receive suggestions to provide careful explanations accordingly.

[0141] This system integrates drug information management with emotion-based responses, enhancing user confidence and improving the quality of pharmacist services. For example, it could be used to provide more detailed drug information than usual to anxious users, and to continuously offer reassuring suggestions to pharmacists.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user uses the device to input information about medications, health conditions, and emotions. Input can be in text or voice format; if voice is used, the device converts the voice to text.

[0145] Step 2:

[0146] The terminal sends all the entered information to the server at once. This information includes the name of the medication, the user's symptoms, and their emotional response to them.

[0147] Step 3:

[0148] The server compares the received drug information with a database and extracts the efficacy, dosage, and precautions for the relevant drug. Simultaneously, it uses an emotion engine to analyze the user's emotions.

[0149] Step 4:

[0150] The server determines how the information should be presented based on the sentiment analysis results. For example, if anxiety is detected, the server prepares a detailed explanation to address it.

[0151] Step 5:

[0152] The server sends the generated information back to the terminal and displays it to the user in an interactive format. The information is presented in a relaxed tone, taking the user's emotions into consideration.

[0153] Step 6:

[0154] After taking medication, users input feedback via their device regarding changes in their physical condition and their emotions at the time. This information is also sent to the server.

[0155] Step 7:

[0156] The server evaluates the effectiveness of drug use based on feedback data. It also considers emotional data to provide additional support that reassures the user.

[0157] Step 8:

[0158] The server generates suggestions for pharmacists based on the user's physical condition and emotions, and notifies them via the terminal. This allows pharmacists to comprehensively understand the user's situation and adjust their support accordingly.

[0159] Step 9:

[0160] The pharmacist reviews the suggestion and provides prescription revisions and additional support as needed. The pharmacist's terminal displays response options that also take the user's feelings into consideration.

[0161] This process will lead to the creation of a system in which both users and pharmacists receive appropriate and effective support.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] In recent years, the amount of information available on chemicals has increased, making it difficult for users to quickly and accurately obtain information that is relevant to their needs. Furthermore, anxiety and doubts about the use of chemicals often become a psychological burden for users. Therefore, there is a need for experts to provide more effective support to ensure that users can use chemicals with confidence.

[0165] 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.

[0166] In this invention, the server includes means for receiving chemical information and health status information from the user, means for matching relevant information sources based on the received information and generating information using source technology, and means for dynamically adjusting the provision of information according to the user's emotions using emotion analysis technology. As a result, users can use chemicals with peace of mind, and experts can provide effective support that takes into account the user's psychological state.

[0167] "Chemical product information" refers to a series of pieces of information related to a chemical product, such as its name, ingredients, effects, usage instructions, and precautions.

[0168] "Health status information" refers to information such as the user's physical condition, symptoms, recent changes in their physical condition, and health concerns.

[0169] "Emotional analysis technology" is a technology that analyzes user input data to identify and evaluate the user's current emotional state.

[0170] "Source technology" refers to technologies that use artificial intelligence and machine learning models to generate new information and insights based on input data.

[0171] "Information sources" refer to databases and documents containing detailed information about chemical products.

[0172] A "specialist" refers to a professional who possesses knowledge about the use and effects of chemicals, typically such as a pharmacist or a doctor.

[0173] "Dynamic adjustment" means changing the content of information or services in real time in response to changes in circumstances or input data.

[0174] This system is initiated when the user inputs chemical information and health status information via an information terminal. This input can also be done using voice input technology, allowing for flexible information provision tailored to the user's situation. The terminal formats this data and immediately sends it to the server.

[0175] The server uses a sophisticated database system to compare received chemical information with existing information sources. Software components used include a database management system and a generative AI model. During this process, the server generates information on the efficacy, usage, and precautions of the chemicals, organizes it, and prepares it for optimal delivery to users.

[0176] Furthermore, the server uses emotion analysis technology to determine the user's emotional state from their input and dynamically adjusts the content and presentation of the information it provides. This adjustment process includes utilizing generative AI models to generate information that reduces the user's psychological burden. For example, if a user inputs "I'm worried about side effects," the server will provide detailed information about side effects and how to deal with them.

[0177] Furthermore, the system is designed so that the server notifies experts of the generated information and suggestions on their terminals, allowing experts to review, approve, or revise that information. This enables experts to communicate more accurately with users.

[0178] As a concrete example, a prompt might read, "Generate information to present detailed side effects of a medication and how to manage them, in case the user is feeling anxious." Based on this prompt, the server performs the process of generating and presenting the information.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] Users input chemical information and health status information using an information terminal. Voice input functionality can be used to provide information more easily. The entered data is formatted into items such as chemical name, symptoms, and emotions.

[0182] Step 2:

[0183] The terminal sends user input data to the server. The input data is securely transferred over the network and received by the server. The received data is temporarily stored in a database, preparing it for subsequent processing.

[0184] Step 3:

[0185] The server compares the received chemical information with the information sources in the database. Using the generative AI model employed, it extracts relevant information such as efficacy, usage instructions, and precautions. In this process, the server identifies the information that best matches the entered chemical name, and the data is output in an organized format.

[0186] Step 4:

[0187] The server utilizes sentiment analysis technology to evaluate the user's emotional state. It analyzes keywords included in the input data to identify the user's anxieties and tensions. The analysis results are used to customize the information provided, generating emotion-based insights.

[0188] Step 5:

[0189] Based on the results of sentiment analysis, the server dynamically adjusts the content and presentation method of the information provided to the user. Using a generative AI model, the information is processed to appropriately meet the requested content, and detailed guides to alleviate anxiety and specific countermeasures are output.

[0190] Step 6:

[0191] The server sends the generated information to the user's terminal in an interactive format, allowing the user to review it. The information is displayed in a user-friendly format on the terminal's interface. This information provision allows users to deepen their understanding and gain a sense of security.

[0192] Step 7:

[0193] The server notifies the expert's terminal of the generated information and suggestions. The expert reviews the received information and makes corrections or approvals as needed. Through this process, the expert can provide more appropriate support to the user.

[0194] (Application Example 2)

[0195] 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."

[0196] In physical stores such as pharmacies and drugstores, there is a need to effectively alleviate the anxiety and worries that customers feel about medications and to enable them to use medications with peace of mind. In particular, there is a lack of systems that can provide appropriate medication information while taking into account the emotional state of the customer. Pharmacists need to improve the quality of service by communicating more appropriately according to the physical condition and emotional state of the customer.

[0197] 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.

[0198] In this invention, the server includes means for receiving drug information and health information from the user; means for matching the received drug information with a relevant database and generating drug efficacy, usage instructions, and precautions using a generative machine learning model; and means for analyzing the user's emotional data and adaptively adjusting the information presentation using a generative machine learning model. This enables flexible information provision according to the user's emotional state, thereby increasing their sense of security and supporting the pharmacist's work.

[0199] A "user" is an individual who uses this system to obtain medication information and provides information about their own health condition and emotions.

[0200] "Drug information" refers to information such as the name of the drug, its effects, how to use it, and precautions.

[0201] "Health information" refers to information about the user's health status, specifically including symptoms, body temperature, and other physical conditions.

[0202] A "generative machine learning model" is a sophisticated algorithm used to analyze received data and generate necessary information.

[0203] "Emotional data" refers to information about the user's emotional state, specifically data that uses indicators such as anxiety, tension, and a sense of security.

[0204] "Adaptively adjusting information presentation" refers to dynamically changing the content and format of the information provided according to the user's emotional state.

[0205] A "pharmacist" is a professional who provides medications and instructs users, supporting them in using medications safely through communication.

[0206] To implement this invention, the system requires a series of hardware and software components. This system includes a user terminal, a processing server, and a display device such as smart glasses.

[0207] The user's device is equipped with an interface for voice input and sentiment data collection. This interface includes a microphone for receiving voice input and speech recognition software for converting speech into text data. Specifically, the Google® Cloud Speech-to-Text API is used to convert speech data into text. This data is then processed by IBM Watson® sentiment analysis API, which analyzes the user's sentiment data.

[0208] The server uses a machine learning model generated based on drug information, health information, and emotional data received from the terminal to determine the efficacy, usage, and precautions of the medications to be provided to the user. The server also communicates with a database in a cloud environment (such as AWS® Lambda) to retrieve relevant drug information.

[0209] When adaptively adjusting information presentation to take into account the user's emotional state, the level of detail and explanation of the information are flexibly changed based on the results of emotion analysis. The information generated by the server is visually displayed on smart glasses, enhancing the user's sense of security.

[0210] As a concrete example, a pharmacist wearing smart glasses could detect a customer's anxiety and then check detailed medication information and usage instructions, or provide guidance to offer additional reassurance. An example of a prompt message for the generating AI model might be, "A customer with cold symptoms is anxious about using a spray-type medication. Please suggest ways to provide detailed explanations and guidance to reassure them." This would create a system where pharmacists appropriately provide necessary information according to the user's emotional state, enabling effective communication.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The user's device receives voice input. When the user speaks information about medications into the device, the voice input module captures the voice and saves it as audio data. The input is the user's voice data, and the output is the data saved as an audio file.

[0214] Step 2:

[0215] The device's speech recognition software converts the received audio file into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the audio data and convert it into a string. The output is the user's spoken content in text format.

[0216] Step 3:

[0217] Text data, along with the user's health and emotional data, is sent to the server. The server then uses this data to prepare to retrieve medication information from relevant databases. The input consists of user text, health information, and emotional data, while the output is the transmission of data to the server.

[0218] Step 4:

[0219] The server extracts relevant drug information from the database based on the text data. Here, it searches the database based on the received information such as the drug name, and retrieves the efficacy, usage instructions, and precautions for the corresponding drug. The output is the relevant drug information.

[0220] Step 5:

[0221] The server analyzes the user's emotional data using IBM Watson's Sentiment Analysis API. The server analyzes the user's emotional state from text data and generates data identifying states such as tension, relief, and anxiety. The output is the analysis result regarding the user's emotions.

[0222] Step 6:

[0223] The server integrates drug information and sentiment analysis results, and uses a generative AI model to adjust the content of the information presented. Based on the emotional state, it flexibly changes the content and details of the information presented as needed. Through this process, the generated information is optimized to enhance the user's sense of security.

[0224] Step 7:

[0225] The server transmits the adjusted medication information to the smart glasses. This information is visually displayed on the smart glasses worn by the pharmacist, allowing the pharmacist to obtain appropriate information and provide appropriate care to the user. The input is the adjusted medication information, and the output is the display of this information on the smart glasses.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is implemented as a system for managing and evaluating drug information and health information through the exchange of information between a user, a terminal, and a server. Its specific form and processing flow are described below in natural language.

[0243] The user first uses a terminal to enter information about prescribed medications and their current health condition. This terminal includes a voice input function and is designed for easy use by the elderly and visually impaired. The terminal then sends the user's input data to the server.

[0244] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest pharmaceutical research data, allowing for the retrieval of information from all angles. Generative artificial intelligence is used to automatically generate detailed drug information in a user-friendly format.

[0245] The generated information is provided to the user interactively through the terminal. For example, if the user asks for detailed information about using "aspirin," the server will send information such as, "Aspirin is a medication that relieves pain and inflammation. It can be hard on the stomach, so it is recommended to take it after meals," and then set a prompt such as, "Is there anything else you would like to know?"

[0246] After taking medication, users input feedback into their device regarding any changes in their physical condition or side effects. The server evaluates the drug's effectiveness based on this feedback and performs further detailed information analysis. The user's information is then cross-referenced with the database to generate an individualized evaluation.

[0247] For pharmacists, the server continuously monitors changes in the user's health and automatically delivers information on points to be aware of and suggested alternatives. This allows pharmacists to respond more quickly to the user's situation and adjust medications or suggest new treatments as needed.

[0248] This system can deepen users' understanding of medication use and improve the efficiency of pharmacists' work. For example, if an adverse reaction is reported, the server analyzes the report and suggests additional gastroprotective medications to the pharmacist, thereby improving the user's medication experience. This entire process improves the quality of healthcare and provides a user-friendly support environment for both users and pharmacists.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user uses the device to enter the names of prescribed medications and their health condition. Input can be either text or voice; in the case of voice input, the device converts it into text data.

[0252] Step 2:

[0253] The terminal transmits the entered medication information and health status information to the server. The transmission is in data format and is sent via a secure communication channel along with each user's identification information.

[0254] Step 3:

[0255] The server analyzes the received data to identify specific drug names and symptoms. This prepares it to search for relevant pharmacological data in the database.

[0256] Step 4:

[0257] The server accesses the database to retrieve information on the efficacy, usage, precautions, and other relevant details of a specified drug. The system is designed to take into account the latest research findings and guidelines.

[0258] Step 5:

[0259] The server uses artificial intelligence to generate information for the user in natural language based on the acquired data. The generated information is presented in a conversational format to make it easy for the user to understand.

[0260] Step 6:

[0261] The server sends the generated information back to the terminal and displays it to the user. The terminal provides the user with information about the drug's effects and usage, and also displays prompts for additional questions.

[0262] Step 7:

[0263] Users input feedback on changes in their physical condition or side effects after taking medication into their device. This feedback is also sent to the server.

[0264] Step 8:

[0265] The server receives feedback information and individually evaluates the effects of the drugs. Furthermore, it compares this information with a database and updates the evaluation of each drug.

[0266] Step 9:

[0267] The server generates necessary warnings and suggestions for pharmacists and notifies them via their terminals. This allows pharmacists to review prescriptions and provide additional care as needed.

[0268] This series of steps leads to a system that supports both users and pharmacists.

[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] There is a need for a system that allows users, including the elderly and visually impaired, to easily obtain detailed information on the proper use and side effects of medications, and to receive personalized advice based on feedback on their health status. In particular, a system is needed that enables rapid and accurate health management even for users in remote locations.

[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 receiving information about medications and health status from the user; means for comparing the received medication information with a relevant database and generating information about the effects, usage, and precautions of the medication using a generating AI; and means for displaying the generated information in an interactive format on the user's communication device and responding to additional questions from the user. This enables users, including the elderly and visually impaired, to understand how to use medications appropriately according to their health condition and to effectively manage their health.

[0274] A "user" refers to a person who uses this system to input medication information and health status information, and receives guidance and advice based on that information.

[0275] "Drug information" refers to detailed information about a prescribed medication, including its name, effects, usage, and precautions.

[0276] "Health status information" refers to information about the user's current physical condition and symptoms.

[0277] A "database" refers to a collection of information that includes drug effects, usage instructions, and the latest research data.

[0278] "Generative AI" refers to artificial intelligence technology that automatically generates explanatory text in natural language in a format easily understood by humans, based on input information.

[0279] A "communication device" refers to a device used by users to input medication information and health status information, or to receive generated information.

[0280] A "health manager" is a person who monitors the health status of users and provides appropriate suggestions and guidance when necessary.

[0281] "Feedback information" refers to information provided by users regarding changes in their physical condition or side effects after using medication.

[0282] The present invention is a system that realizes drug management and health management through the mutual cooperation of users, terminals, and servers. The specific forms thereof will be described below.

[0283] First, the user uses the terminal to input drug information and their own health status. This terminal is equipped with voice recognition software and can convert voice input into text format, so it can be easily operated even by the elderly and visually impaired.

[0284] The input information is sent to the server via the communication terminal. The server uses this information to collate with the internal database. In this database, the effects, usage methods, and precautions of various drugs are registered. Also, the latest pharmaceutical research data is updated and stored at any time. The server uses a generative AI model to automatically generate detailed information on the drugs required by the user. As a specific software, a generative AI model is used, which creates information based on natural language processing technology.

[0285] The generated information is displayed on the user's terminal in an interactive format. For example, when the user inputs "Please tell me the precautions when using aspirin", the server generates information such as "Aspirin is a drug that relieves pain and inflammation. It is recommended to take it after meals." and further presents a prompt such as "Is there anything else you want to know?"

[0286] After taking the medicine, the user inputs feedback on the changes in their health status and the presence or absence of side effects to the terminal. The feedback is sent to the server again and matched with the relevant database. In this process, the server evaluates the effect of the drug based on the feedback and generates individual advice. This information is automatically notified to the health managers, and they can appropriately grasp the user's health status and take corresponding actions as needed.

[0287] For example, if a user wants to know about the side effects of medication taken at night, the system can provide information quickly and appropriately by prompting them with a message such as, "Please tell me what precautions I should take when taking medication at night."

[0288] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0289] Step 1:

[0290] The user inputs medication information and health status via voice through a terminal. This input is converted into text data by speech recognition software. The input includes the names of prescribed medications and information about the user's current health condition. The output is medication information and health status information in text format.

[0291] Step 2:

[0292] The terminal sends the converted text data to the server. In this process, the text data is formatted into the appropriate format and transferred to the server via the network. The input is the text data from the terminal, and the output is the drug information and health status information that reaches the server.

[0293] Step 3:

[0294] The server compares the received information with its internal database. The database contains information on the effects, usage, and precautions of the medication. Here, the server extracts relevant information using the medication name as a keyword. The input is the user's medication information, and the output is detailed information about the medication extracted as a result of the comparison.

[0295] Step 4:

[0296] The server uses a generative AI model to generate drug information in a user-friendly format. For example, it generates detailed information such as, "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals." The input is the result of a matching process, and the output is drug information in natural language format.

[0297] Step 5:

[0298] The terminal provides the user with generated information. The information is displayed interactively, and prompts are set to accommodate additional questions from the user. Input is generated information from the server, and output is the presentation of information to the user.

[0299] Step 6:

[0300] The user provides feedback on changes in their physical condition and side effects after taking medication. This feedback is again in voice format, and the device converts it into text data. The input is the user's feedback on their physical condition, and the output is text data that is sent back to the server.

[0301] Step 7:

[0302] The server evaluates the effectiveness of the medication based on the feedback information and generates advice to be provided to healthcare managers. Here, a generative AI model is used to create individual advice, which is then sent to the healthcare manager. The input is the user's feedback information, and the output is individual advice.

[0303] Step 8:

[0304] Health managers monitor the user's health status based on advice received from the server and make necessary adjustments to prescriptions or suggest new options. The input is advice from the server, and the output is the response and guidance from the health manager.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0307] In the use of pharmaceuticals, there is a problem that an efficient pharmaceutical therapy cannot be achieved because users use them without properly understanding the efficacy, usage method, and safety. In addition, the hurdle for information acquisition is high for the elderly and visually impaired persons, resulting in a risk of inappropriate use of pharmaceuticals. In addition to this, there is also a problem that it takes time and labor for pharmacists to manually analyze users' information and give appropriate advice.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes means for receiving pharmaceutical information and physical condition information from a user, means for collating a related database based on the received pharmaceutical information and generating the efficacy, usage method, and precautions of pharmaceuticals using an artificial intelligence, and means for reading product identification information in a physical store such as a pharmacy and guiding the user with voice regarding the pharmaceutical information. As a result, the user can easily obtain and understand detailed pharmaceutical information, and it is also possible to contribute to the improvement of pharmacists' work.

[0310] A "user" is an individual who provides pharmaceutical information and physical condition information through the system and receives details and proposals regarding pharmaceuticals.

[0311] "Pharmaceutical information" refers to information regarding the type, efficacy, usage method, precautions, etc. of pharmaceuticals.

[0312] "Physical condition information" refers to data related to the health state and physical condition of the user, and is information used for evaluating the effects and applications of pharmaceuticals.

[0313] A "related database" is an information source that stores a data set including the efficacy, usage method, latest research data, etc. of pharmaceuticals.

[0314] "Generative artificial intelligence" refers to AI technology that analyzes received data and provides the generated information to users in an easily understandable way.

[0315] "Product identification information" refers to data used to individually identify products sold at pharmacies and retail stores, and is typically implemented as a barcode or QR code.

[0316] A "physical store" refers to a physical location where customers can actually visit and purchase products.

[0317] A "pharmacist" is a professional who possesses qualifications specializing in medicine and provides medications and advice to users.

[0318] The embodiment for carrying out the invention is configured as follows: In this system, three parties are involved: a server, a terminal, and a user, each playing a specific role.

[0319] First, the user enters medication information and health information using a device. The device can be a smartphone or tablet, and in some cases, smart glasses may also be used. Because it includes a voice input function, users can provide the necessary information via voice. The device then sends the entered information to the server.

[0320] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest research data. The server utilizes a generative AI model to generate detailed drug information in a user-friendly format based on the received information.

[0321] The generated information is presented to the device in an interactive format. When using a smartphone or smart glasses, information can also be provided via voice. For example, if a user wants to know more about a pain reliever, they might receive instructions such as, "This medication relieves headaches. Please note that it is recommended to take it after meals."

[0322] In physical stores like pharmacies, drug information is instantly obtained by scanning product identification information (barcodes or QR codes). This information is also obtained via a server and provided to the user through a terminal.

[0323] Furthermore, when users report the effects and side effects of medications as feedback, the server evaluates this information and analyzes it using a generative AI model. This evaluation is also provided to pharmacists, enabling them to suggest improvements to users.

[0324] For example, if a user needs medication for stomach pain, scanning the product identification information will cause the server to provide details about the medication, and voice guidance will be provided on the terminal. An example of a prompt message might be, "Please explain this medication in detail. Please include its effects, usage instructions, and precautions."

[0325] Through these activities, the acquisition and understanding of pharmaceutical information will be facilitated, and the work of pharmacists will be supported, leading to the proposal of safer and more effective pharmaceutical therapies for users.

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The user uses a terminal to input medication and health information via voice input or touch operation. This input data is collected by the terminal and sent to the server. The input consists of information dictated or selected by the user, and this is sent to the server in digital format.

[0329] Step 2:

[0330] The server compares the received drug information with the relevant database. The database stores detailed information about the drugs, and by matching this data with the user's input data, the server retrieves the relevant information. In this process, the input is drug information from the user, and the output is detailed information about the matched drugs.

[0331] Step 3:

[0332] The server uses a generative AI model to generate drug efficacy, usage instructions, and precautions that are easy for users to understand, based on the cross-referenced information. The data processing performed here involves converting the data into natural language and preparing it for interactive presentation. The input is drug details from the database, and the output is drug information presented in a user-friendly format.

[0333] Step 4:

[0334] The terminal presents the user with generated drug information received from the server. The collected information is provided to the user through voice and screen displays, guiding them to ask additional questions in an interactive format. The input is formatted drug information from the server, and the output is voice guidance and screen displays for the user.

[0335] Step 5:

[0336] After taking medication, users input feedback about changes in their physical condition into a terminal. This feedback data is sent from the terminal to the server. The input is user feedback information, and the transmitted and stored output is detailed data used for further evaluation.

[0337] Step 6:

[0338] The server uses a generated AI model based on feedback information to evaluate the effectiveness of medications and, if necessary, generates improvement suggestions for pharmacists. The input is feedback data, and the output is support information that enables pharmacists to evaluate and make suggestions.

[0339] Step 7:

[0340] The generated suggestions are sent to the pharmacist's terminal. The pharmacist then uses this information to approve or modify the suggestions as needed. The input is suggestion data from the server, and the modified output is the safe drug information and advice ultimately provided to the user.

[0341] 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.

[0342] This invention provides a system that integrates the management of drug information and the handling of user emotions through a combination of user, terminal, server, and emotion engine. This system aims to support users in using medications with confidence and to enable pharmacists to perform their duties efficiently.

[0343] The user uses the device to input information about prescribed medications, their physical condition, and their emotions. Voice input is also available, allowing for smooth information provision even in situations where touch input is difficult. The device sends this information to the server, which centrally manages and processes the data.

[0344] The server first cross-references drug information with relevant databases to extract drug efficacy, usage instructions, precautions, and other relevant information. Next, it uses an emotion engine to analyze the user's emotions. Emotion recognition technology can identify the user's mental state, such as whether they are tense, anxious, or hesitant to ask a question.

[0345] Based on insights provided by the emotion engine, the generated drug information is adaptively adjusted. For example, if a user is particularly anxious, the server will adjust the way information is presented, such as providing more detailed explanations and additional support information. Flexible dialogue tailored to the user's emotions is also provided to encourage users to ask questions with confidence.

[0346] The server also provides pharmacists with information that takes emotional data into account when making suggestions based on changes in the user's physical condition. This allows pharmacists to communicate with users while considering their psychological state, enabling them to provide more effective care. For example, if the data indicates that the user is anxious, the pharmacist can receive suggestions to provide careful explanations accordingly.

[0347] This system integrates drug information management with emotion-based responses, enhancing user confidence and improving the quality of pharmacist services. For example, it could be used to provide more detailed drug information than usual to anxious users, and to continuously offer reassuring suggestions to pharmacists.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The user uses the device to input information about medications, health conditions, and emotions. Input can be in text or voice format; if voice is used, the device converts the voice to text.

[0351] Step 2:

[0352] The terminal sends all the entered information to the server at once. This information includes the name of the medication, the user's symptoms, and their emotional response to them.

[0353] Step 3:

[0354] The server compares the received drug information with a database and extracts the efficacy, dosage, and precautions for the relevant drug. Simultaneously, it uses an emotion engine to analyze the user's emotions.

[0355] Step 4:

[0356] The server determines how the information should be presented based on the sentiment analysis results. For example, if anxiety is detected, the server prepares a detailed explanation to address it.

[0357] Step 5:

[0358] The server sends the generated information back to the terminal and displays it to the user in an interactive format. The information is presented in a relaxed tone, taking the user's emotions into consideration.

[0359] Step 6:

[0360] After taking medication, users input feedback via their device regarding changes in their physical condition and their emotions at the time. This information is also sent to the server.

[0361] Step 7:

[0362] The server evaluates the effectiveness of drug use based on feedback data. It also considers emotional data to provide additional support that reassures the user.

[0363] Step 8:

[0364] The server generates suggestions for pharmacists based on the user's physical condition and emotions, and notifies them via the terminal. This allows pharmacists to comprehensively understand the user's situation and adjust their support accordingly.

[0365] Step 9:

[0366] The pharmacist reviews the suggestion and provides prescription revisions and additional support as needed. The pharmacist's terminal displays response options that also take the user's feelings into consideration.

[0367] This process will lead to the creation of a system in which both users and pharmacists receive appropriate and effective support.

[0368] (Example 2)

[0369] 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".

[0370] In recent years, the amount of information available on chemicals has increased, making it difficult for users to quickly and accurately obtain information that is relevant to their needs. Furthermore, anxiety and doubts about the use of chemicals often become a psychological burden for users. Therefore, there is a need for experts to provide more effective support to ensure that users can use chemicals with confidence.

[0371] 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.

[0372] In this invention, the server includes means for receiving chemical information and health status information from the user, means for matching relevant information sources based on the received information and generating information using source technology, and means for dynamically adjusting the provision of information according to the user's emotions using emotion analysis technology. As a result, users can use chemicals with peace of mind, and experts can provide effective support that takes into account the user's psychological state.

[0373] "Chemical product information" refers to a series of pieces of information related to a chemical product, such as its name, ingredients, effects, usage instructions, and precautions.

[0374] "Health status information" refers to information such as the user's physical condition, symptoms, recent changes in their physical condition, and health concerns.

[0375] "Emotional analysis technology" is a technology that analyzes user input data to identify and evaluate the user's current emotional state.

[0376] "Source technology" refers to technologies that use artificial intelligence and machine learning models to generate new information and insights based on input data.

[0377] "Information sources" refer to databases and documents containing detailed information about chemical products.

[0378] A "specialist" refers to a professional who possesses knowledge about the use and effects of chemicals, typically such as a pharmacist or a doctor.

[0379] "Dynamic adjustment" means changing the content of information or services in real time in response to changes in circumstances or input data.

[0380] This system is initiated when the user inputs chemical information and health status information via an information terminal. This input can also be done using voice input technology, allowing for flexible information provision tailored to the user's situation. The terminal formats this data and immediately sends it to the server.

[0381] The server uses a sophisticated database system to compare received chemical information with existing information sources. Software components used include a database management system and a generative AI model. During this process, the server generates information on the efficacy, usage, and precautions of the chemicals, organizes it, and prepares it for optimal delivery to users.

[0382] Furthermore, the server uses emotion analysis technology to determine the user's emotional state from their input and dynamically adjusts the content and presentation of the information it provides. This adjustment process includes utilizing generative AI models to generate information that reduces the user's psychological burden. For example, if a user inputs "I'm worried about side effects," the server will provide detailed information about side effects and how to deal with them.

[0383] Furthermore, the system is designed so that the server notifies experts of the generated information and suggestions on their terminals, allowing experts to review, approve, or revise that information. This enables experts to communicate more accurately with users.

[0384] As a concrete example, a prompt might read, "Generate information to present detailed side effects of a medication and how to manage them, in case the user is feeling anxious." Based on this prompt, the server performs the process of generating and presenting the information.

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] Users input chemical information and health status information using an information terminal. Voice input functionality can be used to provide information more easily. The entered data is formatted into items such as chemical name, symptoms, and emotions.

[0388] Step 2:

[0389] The terminal sends user input data to the server. The input data is securely transferred over the network and received by the server. The received data is temporarily stored in a database, preparing it for subsequent processing.

[0390] Step 3:

[0391] The server compares the received chemical information with the information sources in the database. Using the generative AI model employed, it extracts relevant information such as efficacy, usage instructions, and precautions. In this process, the server identifies the information that best matches the entered chemical name, and the data is output in an organized format.

[0392] Step 4:

[0393] The server utilizes sentiment analysis technology to evaluate the user's emotional state. It analyzes keywords included in the input data to identify the user's anxieties and tensions. The analysis results are used to customize the information provided, generating emotion-based insights.

[0394] Step 5:

[0395] Based on the results of sentiment analysis, the server dynamically adjusts the content and presentation method of the information provided to the user. Using a generative AI model, the information is processed to appropriately meet the requested content, and detailed guides to alleviate anxiety and specific countermeasures are output.

[0396] Step 6:

[0397] The server sends the generated information to the user's terminal in an interactive format, allowing the user to review it. The information is displayed in a user-friendly format on the terminal's interface. This information provision allows users to deepen their understanding and gain a sense of security.

[0398] Step 7:

[0399] The server notifies the expert's terminal of the generated information and suggestions. The expert reviews the received information and makes corrections or approvals as needed. Through this process, the expert can provide more appropriate support to the user.

[0400] (Application Example 2)

[0401] 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."

[0402] In physical stores such as pharmacies and drugstores, there is a need to effectively alleviate the anxiety and worries that customers feel about medications and to enable them to use medications with peace of mind. In particular, there is a lack of systems that can provide appropriate medication information while taking into account the emotional state of the customer. Pharmacists need to improve the quality of service by communicating more appropriately according to the physical condition and emotional state of the customer.

[0403] 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.

[0404] In this invention, the server includes means for receiving drug information and health information from the user; means for matching the received drug information with a relevant database and generating drug efficacy, usage instructions, and precautions using a generative machine learning model; and means for analyzing the user's emotional data and adaptively adjusting the information presentation using a generative machine learning model. This enables flexible information provision according to the user's emotional state, thereby increasing their sense of security and supporting the pharmacist's work.

[0405] A "user" is an individual who uses this system to obtain medication information and provides information about their own health condition and emotions.

[0406] "Drug information" refers to information such as the name of the drug, its effects, how to use it, and precautions.

[0407] "Health information" refers to information about the user's health status, specifically including symptoms, body temperature, and other physical conditions.

[0408] A "generative machine learning model" is a sophisticated algorithm used to analyze received data and generate necessary information.

[0409] "Emotional data" refers to information about the user's emotional state, specifically data that uses indicators such as anxiety, tension, and a sense of security.

[0410] "Adaptively adjusting information presentation" refers to dynamically changing the content and format of the information provided according to the user's emotional state.

[0411] A "pharmacist" is a professional who provides medications and instructs users, supporting them in using medications safely through communication.

[0412] To implement this invention, the system requires a series of hardware and software components. This system includes a user terminal, a processing server, and a display device such as smart glasses.

[0413] The user's device is equipped with an interface for voice input and sentiment data collection. This interface includes a microphone for receiving voice input and speech recognition software for converting speech into text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the speech data into text. This data is then processed by IBM Watson's sentiment analysis API, which analyzes the user's sentiment data.

[0414] The server uses a machine learning model generated based on drug information, health information, and emotional data received from the terminal to determine the efficacy, usage, and precautions of the medications to be provided to the user. The server also communicates with a database in a cloud environment (such as AWS Lambda) to retrieve relevant drug information.

[0415] When adaptively adjusting information presentation to take into account the user's emotional state, the level of detail and explanation of the information are flexibly changed based on the results of emotion analysis. The information generated by the server is visually displayed on smart glasses, enhancing the user's sense of security.

[0416] As a concrete example, a pharmacist wearing smart glasses could detect a customer's anxiety and then check detailed medication information and usage instructions, or provide guidance to offer additional reassurance. An example of a prompt message for the generating AI model might be, "A customer with cold symptoms is anxious about using a spray-type medication. Please suggest ways to provide detailed explanations and guidance to reassure them." This would create a system where pharmacists appropriately provide necessary information according to the user's emotional state, enabling effective communication.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The user's device receives voice input. When the user speaks information about medications into the device, the voice input module captures the voice and saves it as audio data. The input is the user's voice data, and the output is the data saved as an audio file.

[0420] Step 2:

[0421] The device's speech recognition software converts the received audio file into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the audio data and convert it into a string. The output is the user's spoken content in text format.

[0422] Step 3:

[0423] Text data, along with the user's health and emotional data, is sent to the server. The server then uses this data to prepare to retrieve medication information from relevant databases. The input consists of user text, health information, and emotional data, while the output is the transmission of data to the server.

[0424] Step 4:

[0425] The server extracts relevant drug information from the database based on the text data. Here, it searches the database based on the received information such as the drug name, and retrieves the efficacy, usage instructions, and precautions for the corresponding drug. The output is the relevant drug information.

[0426] Step 5:

[0427] The server analyzes the user's emotional data using IBM Watson's Sentiment Analysis API. The server analyzes the user's emotional state from text data and generates data identifying states such as tension, relief, and anxiety. The output is the analysis result regarding the user's emotions.

[0428] Step 6:

[0429] The server integrates drug information and sentiment analysis results, and uses a generative AI model to adjust the content of the information presented. Based on the emotional state, it flexibly changes the content and details of the information presented as needed. Through this process, the generated information is optimized to enhance the user's sense of security.

[0430] Step 7:

[0431] The server transmits the adjusted medication information to the smart glasses. This information is visually displayed on the smart glasses worn by the pharmacist, allowing the pharmacist to obtain appropriate information and provide appropriate care to the user. The input is the adjusted medication information, and the output is the display of this information on the smart glasses.

[0432] 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.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] This invention is implemented as a system for managing and evaluating drug information and health information through the exchange of information between a user, a terminal, and a server. Its specific form and processing flow are described below in natural language.

[0449] The user first uses a terminal to enter information about prescribed medications and their current health condition. This terminal includes a voice input function and is designed for easy use by the elderly and visually impaired. The terminal then sends the user's input data to the server.

[0450] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest pharmaceutical research data, allowing for the retrieval of information from all angles. Generative artificial intelligence is used to automatically generate detailed drug information in a user-friendly format.

[0451] The generated information is provided to the user interactively through the terminal. For example, if the user asks for detailed information about using "aspirin," the server will send information such as, "Aspirin is a medication that relieves pain and inflammation. It can be hard on the stomach, so it is recommended to take it after meals," and then set a prompt such as, "Is there anything else you would like to know?"

[0452] After taking medication, users input feedback into their device regarding any changes in their physical condition or side effects. The server evaluates the drug's effectiveness based on this feedback and performs further detailed information analysis. The user's information is then cross-referenced with the database to generate an individualized evaluation.

[0453] For pharmacists, the server continuously monitors changes in the user's health and automatically delivers information on points to be aware of and suggested alternatives. This allows pharmacists to respond more quickly to the user's situation and adjust medications or suggest new treatments as needed.

[0454] This system can deepen users' understanding of medication use and improve the efficiency of pharmacists' work. For example, if an adverse reaction is reported, the server analyzes the report and suggests additional gastroprotective medications to the pharmacist, thereby improving the user's medication experience. This entire process improves the quality of healthcare and provides a user-friendly support environment for both users and pharmacists.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The user uses the device to enter the names of prescribed medications and their health condition. Input can be either text or voice; in the case of voice input, the device converts it into text data.

[0458] Step 2:

[0459] The terminal transmits the entered medication information and health status information to the server. The transmission is in data format and is sent via a secure communication channel along with each user's identification information.

[0460] Step 3:

[0461] The server analyzes the received data to identify specific drug names and symptoms. This prepares it to search for relevant pharmacological data in the database.

[0462] Step 4:

[0463] The server accesses the database to retrieve information on the efficacy, usage, precautions, and other relevant details of a specified drug. The system is designed to take into account the latest research findings and guidelines.

[0464] Step 5:

[0465] The server uses artificial intelligence to generate information for the user in natural language based on the acquired data. The generated information is presented in a conversational format to make it easy for the user to understand.

[0466] Step 6:

[0467] The server sends the generated information back to the terminal and displays it to the user. The terminal provides the user with information about the drug's effects and usage, and also displays prompts for additional questions.

[0468] Step 7:

[0469] Users input feedback on changes in their physical condition or side effects after taking medication into their device. This feedback is also sent to the server.

[0470] Step 8:

[0471] The server receives feedback information and individually evaluates the effects of the drugs. Furthermore, it compares this information with a database and updates the evaluation of each drug.

[0472] Step 9:

[0473] The server generates necessary warnings and suggestions for pharmacists and notifies them via their terminals. This allows pharmacists to review prescriptions and provide additional care as needed.

[0474] This series of steps leads to a system that supports both users and pharmacists.

[0475] (Example 1)

[0476] 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."

[0477] There is a need for a system that allows users, including the elderly and visually impaired, to easily obtain detailed information on the proper use and side effects of medications, and to receive personalized advice based on feedback on their health status. In particular, a system is needed that enables rapid and accurate health management even for users in remote locations.

[0478] 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.

[0479] In this invention, the server includes means for receiving information about medications and health status from the user; means for comparing the received medication information with a relevant database and generating information about the effects, usage, and precautions of the medication using a generating AI; and means for displaying the generated information in an interactive format on the user's communication device and responding to additional questions from the user. This enables users, including the elderly and visually impaired, to understand how to use medications appropriately according to their health condition and to effectively manage their health.

[0480] A "user" refers to a person who uses this system to input medication information and health status information, and receives guidance and advice based on that information.

[0481] "Drug information" refers to detailed information about a prescribed medication, including its name, effects, usage, and precautions.

[0482] "Health status information" refers to information about the user's current physical condition and symptoms.

[0483] A "database" refers to a collection of information that includes drug effects, usage instructions, and the latest research data.

[0484] "Generative AI" refers to artificial intelligence technology that automatically generates explanatory text in natural language in a format easily understood by humans, based on input information.

[0485] A "communication device" refers to a device used by users to input medication information and health status information, or to receive generated information.

[0486] A "health manager" is a person who monitors the health status of users and provides appropriate suggestions and guidance when necessary.

[0487] "Feedback information" refers to information provided by users regarding changes in their physical condition or side effects after using medication.

[0488] This invention is a system that enables drug management and health management through the mutual cooperation of users, terminals, and servers. Its specific form is described below.

[0489] Users first use a terminal to input medication information and their own health status. This terminal is equipped with voice recognition software that can convert voice input into text format, making it easy for the elderly and visually impaired to use.

[0490] The entered information is transmitted to the server via a communication terminal. The server uses this information to compare it with its internal database. This database contains information on the effects, usage, and precautions of various drugs. It also stores the latest drug research data, which is updated regularly. The server uses a generative AI model to automatically generate detailed drug information required by the user. Specifically, the software used is a generative AI model, which creates information based on natural language processing technology.

[0491] The generated information is displayed on the user's terminal in an interactive format. For example, if a user inputs "What precautions should I take when using aspirin?", the server generates information such as "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals," and then prompts, "Is there anything else you would like to know?"

[0492] After taking medication, users input feedback into a terminal regarding changes in their health condition and any side effects. This feedback is then sent back to the server and cross-referenced with relevant databases. In this process, the server evaluates the effectiveness of the medication based on the feedback and generates personalized advice. This information is automatically notified to healthcare managers, who can then appropriately monitor the user's health and take appropriate action as needed.

[0493] For example, if a user wants to know about the side effects of medication taken at night, the system can provide information quickly and appropriately by prompting them with a message such as, "Please tell me what precautions I should take when taking medication at night."

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The user inputs medication information and health status via voice through a terminal. This input is converted into text data by speech recognition software. The input includes the names of prescribed medications and information about the user's current health condition. The output is medication information and health status information in text format.

[0497] Step 2:

[0498] The terminal sends the converted text data to the server. In this process, the text data is formatted into the appropriate format and transferred to the server via the network. The input is the text data from the terminal, and the output is the drug information and health status information that reaches the server.

[0499] Step 3:

[0500] The server compares the received information with its internal database. The database contains information on the effects, usage, and precautions of the medication. Here, the server extracts relevant information using the medication name as a keyword. The input is the user's medication information, and the output is detailed information about the medication extracted as a result of the comparison.

[0501] Step 4:

[0502] The server uses a generative AI model to generate drug information in a user-friendly format. For example, it generates detailed information such as, "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals." The input is the result of a matching process, and the output is drug information in natural language format.

[0503] Step 5:

[0504] The terminal provides the user with generated information. The information is displayed interactively, and prompts are set to accommodate additional questions from the user. Input is generated information from the server, and output is the presentation of information to the user.

[0505] Step 6:

[0506] The user provides feedback on changes in their physical condition and side effects after taking medication. This feedback is again in voice format, and the device converts it into text data. The input is the user's feedback on their physical condition, and the output is text data that is sent back to the server.

[0507] Step 7:

[0508] The server evaluates the effectiveness of the medication based on the feedback information and generates advice to be provided to healthcare managers. Here, a generative AI model is used to create individual advice, which is then sent to the healthcare manager. The input is the user's feedback information, and the output is individual advice.

[0509] Step 8:

[0510] Health managers monitor the user's health status based on advice received from the server and make necessary adjustments to prescriptions or suggest new options. The input is advice from the server, and the output is the response and guidance from the health manager.

[0511] (Application Example 1)

[0512] 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."

[0513] In the use of pharmaceuticals, there is a problem in that effective medical therapy cannot be achieved because users do not properly understand the efficacy, usage, and safety of the drugs. Furthermore, the hurdles to obtaining information are high for the elderly and visually impaired, which increases the risk of inappropriate drug use. In addition, there is the challenge that it takes time and effort for pharmacists to manually analyze user information and provide appropriate advice.

[0514] 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.

[0515] In this invention, the server includes means for receiving drug information and health information from the user, means for matching the received drug information with a related database and generating drug efficacy, usage instructions, and precautions using artificial intelligence, and means for reading product identification information in a physical store such as a pharmacy and providing the user with drug information via voice. As a result, users can easily obtain and understand detailed drug information, and it is also possible to contribute to improving the work of pharmacists.

[0516] A "user" is an individual who provides information about medications and health conditions through the system and receives detailed information and suggestions about medications.

[0517] "Drug information" refers to information about the type of drug, its effects, how to use it, precautions, etc.

[0518] "Health information" refers to data related to the user's health status and physical condition, and is used to evaluate the effectiveness and applicability of medications.

[0519] A "related database" is a source of information that stores datasets containing information such as the efficacy and usage of drugs, as well as the latest research data.

[0520] "Generative artificial intelligence" refers to AI technology that analyzes received data and provides the generated information to users in an easily understandable way.

[0521] "Product identification information" refers to data used to individually identify products sold at pharmacies and retail stores, and is typically implemented as a barcode or QR code.

[0522] A "physical store" refers to a physical location where customers can actually visit and purchase products.

[0523] A "pharmacist" is a professional who possesses qualifications specializing in medicine and provides medications and advice to users.

[0524] The embodiment for carrying out the invention is configured as follows: In this system, three parties are involved: a server, a terminal, and a user, each playing a specific role.

[0525] First, the user enters medication information and health information using a device. The device can be a smartphone or tablet, and in some cases, smart glasses may also be used. Because it includes a voice input function, users can provide the necessary information via voice. The device then sends the entered information to the server.

[0526] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest research data. The server utilizes a generative AI model to generate detailed drug information in a user-friendly format based on the received information.

[0527] The generated information is presented to the device in an interactive format. When using a smartphone or smart glasses, information can also be provided via voice. For example, if a user wants to know more about a pain reliever, they might receive instructions such as, "This medication relieves headaches. Please note that it is recommended to take it after meals."

[0528] In physical stores like pharmacies, drug information is instantly obtained by scanning product identification information (barcodes or QR codes). This information is also obtained via a server and provided to the user through a terminal.

[0529] Furthermore, when users report the effects and side effects of medications as feedback, the server evaluates this information and analyzes it using a generative AI model. This evaluation is also provided to pharmacists, enabling them to suggest improvements to users.

[0530] For example, if a user needs medication for stomach pain, scanning the product identification information will cause the server to provide details about the medication, and voice guidance will be provided on the terminal. An example of a prompt message might be, "Please explain this medication in detail. Please include its effects, usage instructions, and precautions."

[0531] Through these activities, the acquisition and understanding of pharmaceutical information will be facilitated, and the work of pharmacists will be supported, leading to the proposal of safer and more effective pharmaceutical therapies for users.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The user uses a terminal to input medication and health information via voice input or touch operation. This input data is collected by the terminal and sent to the server. The input consists of information dictated or selected by the user, and this is sent to the server in digital format.

[0535] Step 2:

[0536] The server compares the received drug information with the relevant database. The database stores detailed information about the drugs, and by matching this data with the user's input data, the server retrieves the relevant information. In this process, the input is drug information from the user, and the output is detailed information about the matched drugs.

[0537] Step 3:

[0538] The server uses a generative AI model to generate drug efficacy, usage instructions, and precautions that are easy for users to understand, based on the cross-referenced information. The data processing performed here involves converting the data into natural language and preparing it for interactive presentation. The input is drug details from the database, and the output is drug information presented in a user-friendly format.

[0539] Step 4:

[0540] The terminal presents the user with generated drug information received from the server. The collected information is provided to the user through voice and screen displays, guiding them to ask additional questions in an interactive format. The input is formatted drug information from the server, and the output is voice guidance and screen displays for the user.

[0541] Step 5:

[0542] After taking medication, users input feedback about changes in their physical condition into a terminal. This feedback data is sent from the terminal to the server. The input is user feedback information, and the transmitted and stored output is detailed data used for further evaluation.

[0543] Step 6:

[0544] The server uses a generated AI model based on feedback information to evaluate the effectiveness of medications and, if necessary, generates improvement suggestions for pharmacists. The input is feedback data, and the output is support information that enables pharmacists to evaluate and make suggestions.

[0545] Step 7:

[0546] The generated suggestions are sent to the pharmacist's terminal. The pharmacist then uses this information to approve or modify the suggestions as needed. The input is suggestion data from the server, and the modified output is the safe drug information and advice ultimately provided to the user.

[0547] 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.

[0548] This invention provides a system that integrates the management of drug information and the handling of user emotions through a combination of user, terminal, server, and emotion engine. This system aims to support users in using medications with confidence and to enable pharmacists to perform their duties efficiently.

[0549] The user uses the device to input information about prescribed medications, their physical condition, and their emotions. Voice input is also available, allowing for smooth information provision even in situations where touch input is difficult. The device sends this information to the server, which centrally manages and processes the data.

[0550] The server first cross-references drug information with relevant databases to extract drug efficacy, usage instructions, precautions, and other relevant information. Next, it uses an emotion engine to analyze the user's emotions. Emotion recognition technology can identify the user's mental state, such as whether they are tense, anxious, or hesitant to ask a question.

[0551] Based on insights provided by the emotion engine, the generated drug information is adaptively adjusted. For example, if a user is particularly anxious, the server will adjust the way information is presented, such as providing more detailed explanations and additional support information. Flexible dialogue tailored to the user's emotions is also provided to encourage users to ask questions with confidence.

[0552] The server also provides pharmacists with information that takes emotional data into account when making suggestions based on changes in the user's physical condition. This allows pharmacists to communicate with users while considering their psychological state, enabling them to provide more effective care. For example, if the data indicates that the user is anxious, the pharmacist can receive suggestions to provide careful explanations accordingly.

[0553] This system integrates drug information management with emotion-based responses, enhancing user confidence and improving the quality of pharmacist services. For example, it could be used to provide more detailed drug information than usual to anxious users, and to continuously offer reassuring suggestions to pharmacists.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The user uses the device to input information about medications, health conditions, and emotions. Input can be in text or voice format; if voice is used, the device converts the voice to text.

[0557] Step 2:

[0558] The terminal sends all the entered information to the server at once. This information includes the name of the medication, the user's symptoms, and their emotional response to them.

[0559] Step 3:

[0560] The server compares the received drug information with a database and extracts the efficacy, dosage, and precautions for the relevant drug. Simultaneously, it uses an emotion engine to analyze the user's emotions.

[0561] Step 4:

[0562] The server determines how the information should be presented based on the sentiment analysis results. For example, if anxiety is detected, the server prepares a detailed explanation to address it.

[0563] Step 5:

[0564] The server sends the generated information back to the terminal and displays it to the user in an interactive format. The information is presented in a relaxed tone, taking the user's emotions into consideration.

[0565] Step 6:

[0566] After taking medication, users input feedback via their device regarding changes in their physical condition and their emotions at the time. This information is also sent to the server.

[0567] Step 7:

[0568] The server evaluates the effectiveness of drug use based on feedback data. It also considers emotional data to provide additional support that reassures the user.

[0569] Step 8:

[0570] The server generates suggestions for pharmacists based on the user's physical condition and emotions, and notifies them via the terminal. This allows pharmacists to comprehensively understand the user's situation and adjust their support accordingly.

[0571] Step 9:

[0572] The pharmacist reviews the suggestion and provides prescription revisions and additional support as needed. The pharmacist's terminal displays response options that also take the user's feelings into consideration.

[0573] This process will lead to the creation of a system in which both users and pharmacists receive appropriate and effective support.

[0574] (Example 2)

[0575] 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."

[0576] In recent years, the amount of information available on chemicals has increased, making it difficult for users to quickly and accurately obtain information that is relevant to their needs. Furthermore, anxiety and doubts about the use of chemicals often become a psychological burden for users. Therefore, there is a need for experts to provide more effective support to ensure that users can use chemicals with confidence.

[0577] 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.

[0578] In this invention, the server includes means for receiving chemical information and health status information from the user, means for matching relevant information sources based on the received information and generating information using source technology, and means for dynamically adjusting the provision of information according to the user's emotions using emotion analysis technology. As a result, users can use chemicals with peace of mind, and experts can provide effective support that takes into account the user's psychological state.

[0579] "Chemical product information" refers to a series of pieces of information related to a chemical product, such as its name, ingredients, effects, usage instructions, and precautions.

[0580] "Health status information" refers to information such as the user's physical condition, symptoms, recent changes in their physical condition, and health concerns.

[0581] "Emotional analysis technology" is a technology that analyzes user input data to identify and evaluate the user's current emotional state.

[0582] "Source technology" refers to technologies that use artificial intelligence and machine learning models to generate new information and insights based on input data.

[0583] "Information sources" refer to databases and documents containing detailed information about chemical products.

[0584] A "specialist" refers to a professional who possesses knowledge about the use and effects of chemicals, typically such as a pharmacist or a doctor.

[0585] "Dynamic adjustment" means changing the content of information or services in real time in response to changes in circumstances or input data.

[0586] This system is initiated when the user inputs chemical information and health status information via an information terminal. This input can also be done using voice input technology, allowing for flexible information provision tailored to the user's situation. The terminal formats this data and immediately sends it to the server.

[0587] The server uses a sophisticated database system to compare received chemical information with existing information sources. Software components used include a database management system and a generative AI model. During this process, the server generates information on the efficacy, usage, and precautions of the chemicals, organizes it, and prepares it for optimal delivery to users.

[0588] Furthermore, the server uses emotion analysis technology to determine the user's emotional state from their input and dynamically adjusts the content and presentation of the information it provides. This adjustment process includes utilizing generative AI models to generate information that reduces the user's psychological burden. For example, if a user inputs "I'm worried about side effects," the server will provide detailed information about side effects and how to deal with them.

[0589] Furthermore, the system is designed so that the server notifies experts of the generated information and suggestions on their terminals, allowing experts to review, approve, or revise that information. This enables experts to communicate more accurately with users.

[0590] As a concrete example, a prompt might read, "Generate information to present detailed side effects of a medication and how to manage them, in case the user is feeling anxious." Based on this prompt, the server performs the process of generating and presenting the information.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] Users input chemical information and health status information using an information terminal. Voice input functionality can be used to provide information more easily. The entered data is formatted into items such as chemical name, symptoms, and emotions.

[0594] Step 2:

[0595] The terminal sends user input data to the server. The input data is securely transferred over the network and received by the server. The received data is temporarily stored in a database, preparing it for subsequent processing.

[0596] Step 3:

[0597] The server compares the received chemical information with the information sources in the database. Using the generative AI model employed, it extracts relevant information such as efficacy, usage instructions, and precautions. In this process, the server identifies the information that best matches the entered chemical name, and the data is output in an organized format.

[0598] Step 4:

[0599] The server utilizes sentiment analysis technology to evaluate the user's emotional state. It analyzes keywords included in the input data to identify the user's anxieties and tensions. The analysis results are used to customize the information provided, generating emotion-based insights.

[0600] Step 5:

[0601] Based on the results of sentiment analysis, the server dynamically adjusts the content and presentation method of the information provided to the user. Using a generative AI model, the information is processed to appropriately meet the requested content, and detailed guides to alleviate anxiety and specific countermeasures are output.

[0602] Step 6:

[0603] The server sends the generated information to the user's terminal in an interactive format, allowing the user to review it. The information is displayed in a user-friendly format on the terminal's interface. This information provision allows users to deepen their understanding and gain a sense of security.

[0604] Step 7:

[0605] The server notifies the expert's terminal of the generated information and suggestions. The expert reviews the received information and makes corrections or approvals as needed. Through this process, the expert can provide more appropriate support to the user.

[0606] (Application Example 2)

[0607] 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."

[0608] In physical stores such as pharmacies and drugstores, there is a need to effectively alleviate the anxiety and worries that customers feel about medications and to enable them to use medications with peace of mind. In particular, there is a lack of systems that can provide appropriate medication information while taking into account the emotional state of the customer. Pharmacists need to improve the quality of service by communicating more appropriately according to the physical condition and emotional state of the customer.

[0609] 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.

[0610] In this invention, the server includes means for receiving drug information and health information from the user; means for matching the received drug information with a relevant database and generating drug efficacy, usage instructions, and precautions using a generative machine learning model; and means for analyzing the user's emotional data and adaptively adjusting the information presentation using a generative machine learning model. This enables flexible information provision according to the user's emotional state, thereby increasing their sense of security and supporting the pharmacist's work.

[0611] A "user" is an individual who uses this system to obtain medication information and provides information about their own health condition and emotions.

[0612] "Drug information" refers to information such as the name of the drug, its effects, how to use it, and precautions.

[0613] "Health information" refers to information about the user's health status, specifically including symptoms, body temperature, and other physical conditions.

[0614] A "generative machine learning model" is a sophisticated algorithm used to analyze received data and generate necessary information.

[0615] "Emotional data" refers to information about the user's emotional state, specifically data that uses indicators such as anxiety, tension, and a sense of security.

[0616] "Adaptively adjusting information presentation" refers to dynamically changing the content and format of the information provided according to the user's emotional state.

[0617] A "pharmacist" is a professional who provides medications and instructs users, supporting them in using medications safely through communication.

[0618] To implement this invention, the system requires a series of hardware and software components. This system includes a user terminal, a processing server, and a display device such as smart glasses.

[0619] The user's device is equipped with an interface for voice input and sentiment data collection. This interface includes a microphone for receiving voice input and speech recognition software for converting speech into text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the speech data into text. This data is then processed by IBM Watson's sentiment analysis API, which analyzes the user's sentiment data.

[0620] The server uses a machine learning model generated based on drug information, health information, and emotional data received from the terminal to determine the efficacy, usage, and precautions of the medications to be provided to the user. The server also communicates with a database in a cloud environment (such as AWS Lambda) to retrieve relevant drug information.

[0621] When adaptively adjusting information presentation to take into account the user's emotional state, the level of detail and explanation of the information are flexibly changed based on the results of emotion analysis. The information generated by the server is visually displayed on smart glasses, enhancing the user's sense of security.

[0622] As a concrete example, a pharmacist wearing smart glasses could detect a customer's anxiety and then check detailed medication information and usage instructions, or provide guidance to offer additional reassurance. An example of a prompt message for the generating AI model might be, "A customer with cold symptoms is anxious about using a spray-type medication. Please suggest ways to provide detailed explanations and guidance to reassure them." This would create a system where pharmacists appropriately provide necessary information according to the user's emotional state, enabling effective communication.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The user's device receives voice input. When the user speaks information about medications into the device, the voice input module captures the voice and saves it as audio data. The input is the user's voice data, and the output is the data saved as an audio file.

[0626] Step 2:

[0627] The device's speech recognition software converts the received audio file into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the audio data and convert it into a string. The output is the user's spoken content in text format.

[0628] Step 3:

[0629] Text data, along with the user's health and emotional data, is sent to the server. The server then uses this data to prepare to retrieve medication information from relevant databases. The input consists of user text, health information, and emotional data, while the output is the transmission of data to the server.

[0630] Step 4:

[0631] The server extracts relevant drug information from the database based on the text data. Here, it searches the database based on the received information such as the drug name, and retrieves the efficacy, usage instructions, and precautions for the corresponding drug. The output is the relevant drug information.

[0632] Step 5:

[0633] The server analyzes the user's emotional data using IBM Watson's Sentiment Analysis API. The server analyzes the user's emotional state from text data and generates data identifying states such as tension, relief, and anxiety. The output is the analysis result regarding the user's emotions.

[0634] Step 6:

[0635] The server integrates drug information and sentiment analysis results, and uses a generative AI model to adjust the content of the information presented. Based on the emotional state, it flexibly changes the content and details of the information presented as needed. Through this process, the generated information is optimized to enhance the user's sense of security.

[0636] Step 7:

[0637] The server transmits the adjusted medication information to the smart glasses. This information is visually displayed on the smart glasses worn by the pharmacist, allowing the pharmacist to obtain appropriate information and provide appropriate care to the user. The input is the adjusted medication information, and the output is the display of this information on the smart glasses.

[0638] 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.

[0639] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] 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.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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".

[0655] This invention is implemented as a system for managing and evaluating drug information and health information through the exchange of information between a user, a terminal, and a server. Its specific form and processing flow are described below in natural language.

[0656] The user first uses a terminal to enter information about prescribed medications and their current health condition. This terminal includes a voice input function and is designed for easy use by the elderly and visually impaired. The terminal then sends the user's input data to the server.

[0657] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest pharmaceutical research data, allowing for the retrieval of information from all angles. Generative artificial intelligence is used to automatically generate detailed drug information in a user-friendly format.

[0658] The generated information is provided to the user interactively through the terminal. For example, if the user asks for detailed information about using "aspirin," the server will send information such as, "Aspirin is a medication that relieves pain and inflammation. It can be hard on the stomach, so it is recommended to take it after meals," and then set a prompt such as, "Is there anything else you would like to know?"

[0659] After taking medication, users input feedback into their device regarding any changes in their physical condition or side effects. The server evaluates the drug's effectiveness based on this feedback and performs further detailed information analysis. The user's information is then cross-referenced with the database to generate an individualized evaluation.

[0660] For pharmacists, the server continuously monitors changes in the user's health and automatically delivers information on points to be aware of and suggested alternatives. This allows pharmacists to respond more quickly to the user's situation and adjust medications or suggest new treatments as needed.

[0661] This system can deepen users' understanding of medication use and improve the efficiency of pharmacists' work. For example, if an adverse reaction is reported, the server analyzes the report and suggests additional gastroprotective medications to the pharmacist, thereby improving the user's medication experience. This entire process improves the quality of healthcare and provides a user-friendly support environment for both users and pharmacists.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The user uses the device to enter the names of prescribed medications and their health condition. Input can be either text or voice; in the case of voice input, the device converts it into text data.

[0665] Step 2:

[0666] The terminal transmits the entered medication information and health status information to the server. The transmission is in data format and is sent via a secure communication channel along with each user's identification information.

[0667] Step 3:

[0668] The server analyzes the received data to identify specific drug names and symptoms. This prepares it to search for relevant pharmacological data in the database.

[0669] Step 4:

[0670] The server accesses the database to retrieve information on the efficacy, usage, precautions, and other relevant details of a specified drug. The system is designed to take into account the latest research findings and guidelines.

[0671] Step 5:

[0672] The server uses artificial intelligence to generate information for the user in natural language based on the acquired data. The generated information is presented in a conversational format to make it easy for the user to understand.

[0673] Step 6:

[0674] The server sends the generated information back to the terminal and displays it to the user. The terminal provides the user with information about the drug's effects and usage, and also displays prompts for additional questions.

[0675] Step 7:

[0676] Users input feedback on changes in their physical condition or side effects after taking medication into their device. This feedback is also sent to the server.

[0677] Step 8:

[0678] The server receives feedback information and individually evaluates the effects of the drugs. Furthermore, it compares this information with a database and updates the evaluation of each drug.

[0679] Step 9:

[0680] The server generates necessary warnings and suggestions for pharmacists and notifies them via their terminals. This allows pharmacists to review prescriptions and provide additional care as needed.

[0681] This series of steps leads to a system that supports both users and pharmacists.

[0682] (Example 1)

[0683] 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".

[0684] There is a need for a system that allows users, including the elderly and visually impaired, to easily obtain detailed information on the proper use and side effects of medications, and to receive personalized advice based on feedback on their health status. In particular, a system is needed that enables rapid and accurate health management even for users in remote locations.

[0685] 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.

[0686] In this invention, the server includes means for receiving information about medications and health status from the user; means for comparing the received medication information with a relevant database and generating information about the effects, usage, and precautions of the medication using a generating AI; and means for displaying the generated information in an interactive format on the user's communication device and responding to additional questions from the user. This enables users, including the elderly and visually impaired, to understand how to use medications appropriately according to their health condition and to effectively manage their health.

[0687] A "user" refers to a person who uses this system to input medication information and health status information, and receives guidance and advice based on that information.

[0688] "Drug information" refers to detailed information about a prescribed medication, including its name, effects, usage, and precautions.

[0689] "Health status information" refers to information about the user's current physical condition and symptoms.

[0690] A "database" refers to a collection of information that includes drug effects, usage instructions, and the latest research data.

[0691] "Generative AI" refers to artificial intelligence technology that automatically generates explanatory text in natural language in a format easily understood by humans, based on input information.

[0692] A "communication device" refers to a device used by users to input medication information and health status information, or to receive generated information.

[0693] A "health manager" is a person who monitors the health status of users and provides appropriate suggestions and guidance when necessary.

[0694] "Feedback information" refers to information provided by users regarding changes in their physical condition or side effects after using medication.

[0695] This invention is a system that enables drug management and health management through the mutual cooperation of users, terminals, and servers. Its specific form is described below.

[0696] Users first use a terminal to input medication information and their own health status. This terminal is equipped with voice recognition software that can convert voice input into text format, making it easy for the elderly and visually impaired to use.

[0697] The entered information is transmitted to the server via a communication terminal. The server uses this information to compare it with its internal database. This database contains information on the effects, usage, and precautions of various drugs. It also stores the latest drug research data, which is updated regularly. The server uses a generative AI model to automatically generate detailed drug information required by the user. Specifically, the software used is a generative AI model, which creates information based on natural language processing technology.

[0698] The generated information is displayed on the user's terminal in an interactive format. For example, if a user inputs "What precautions should I take when using aspirin?", the server generates information such as "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals," and then prompts, "Is there anything else you would like to know?"

[0699] After taking medication, users input feedback into a terminal regarding changes in their health condition and any side effects. This feedback is then sent back to the server and cross-referenced with relevant databases. In this process, the server evaluates the effectiveness of the medication based on the feedback and generates personalized advice. This information is automatically notified to healthcare managers, who can then appropriately monitor the user's health and take appropriate action as needed.

[0700] For example, if a user wants to know about the side effects of medication taken at night, the system can provide information quickly and appropriately by prompting them with a message such as, "Please tell me what precautions I should take when taking medication at night."

[0701] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0702] Step 1:

[0703] The user inputs medication information and health status via voice through a terminal. This input is converted into text data by speech recognition software. The input includes the names of prescribed medications and information about the user's current health condition. The output is medication information and health status information in text format.

[0704] Step 2:

[0705] The terminal sends the converted text data to the server. In this process, the text data is formatted into the appropriate format and transferred to the server via the network. The input is the text data from the terminal, and the output is the drug information and health status information that reaches the server.

[0706] Step 3:

[0707] The server compares the received information with its internal database. The database contains information on the effects, usage, and precautions of the medication. Here, the server extracts relevant information using the medication name as a keyword. The input is the user's medication information, and the output is detailed information about the medication extracted as a result of the comparison.

[0708] Step 4:

[0709] The server uses a generative AI model to generate drug information in a user-friendly format. For example, it generates detailed information such as, "Aspirin is a medication that relieves pain and inflammation. It is recommended to take it after meals." The input is the result of a matching process, and the output is drug information in natural language format.

[0710] Step 5:

[0711] The terminal provides the user with generated information. The information is displayed interactively, and prompts are set to accommodate additional questions from the user. Input is generated information from the server, and output is the presentation of information to the user.

[0712] Step 6:

[0713] The user provides feedback on changes in their physical condition and side effects after taking medication. This feedback is again in voice format, and the device converts it into text data. The input is the user's feedback on their physical condition, and the output is text data that is sent back to the server.

[0714] Step 7:

[0715] The server evaluates the effectiveness of the medication based on the feedback information and generates advice to be provided to healthcare managers. Here, a generative AI model is used to create individual advice, which is then sent to the healthcare manager. The input is the user's feedback information, and the output is individual advice.

[0716] Step 8:

[0717] Health managers monitor the user's health status based on advice received from the server and make necessary adjustments to prescriptions or suggest new options. The input is advice from the server, and the output is the response and guidance from the health manager.

[0718] (Application Example 1)

[0719] 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".

[0720] In the use of pharmaceuticals, there is a problem in that effective medical therapy cannot be achieved because users do not properly understand the efficacy, usage, and safety of the drugs. Furthermore, the hurdles to obtaining information are high for the elderly and visually impaired, which increases the risk of inappropriate drug use. In addition, there is the challenge that it takes time and effort for pharmacists to manually analyze user information and provide appropriate advice.

[0721] 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.

[0722] In this invention, the server includes means for receiving drug information and health information from the user, means for matching the received drug information with a related database and generating drug efficacy, usage instructions, and precautions using artificial intelligence, and means for reading product identification information in a physical store such as a pharmacy and providing the user with drug information via voice. As a result, users can easily obtain and understand detailed drug information, and it is also possible to contribute to improving the work of pharmacists.

[0723] A "user" is an individual who provides information about medications and health conditions through the system and receives detailed information and suggestions about medications.

[0724] "Drug information" refers to information about the type of drug, its effects, how to use it, precautions, etc.

[0725] "Health information" refers to data related to the user's health status and physical condition, and is used to evaluate the effectiveness and applicability of medications.

[0726] A "related database" is a source of information that stores datasets containing information such as the efficacy and usage of drugs, as well as the latest research data.

[0727] "Generative artificial intelligence" refers to AI technology that analyzes received data and provides the generated information to users in an easily understandable way.

[0728] "Product identification information" refers to data used to individually identify products sold at pharmacies and retail stores, and is typically implemented as a barcode or QR code.

[0729] A "physical store" refers to a physical location where customers can actually visit and purchase products.

[0730] A "pharmacist" is a professional who possesses qualifications specializing in medicine and provides medications and advice to users.

[0731] The embodiment for carrying out the invention is configured as follows: In this system, three parties are involved: a server, a terminal, and a user, each playing a specific role.

[0732] First, the user enters medication information and health information using a device. The device can be a smartphone or tablet, and in some cases, smart glasses may also be used. Because it includes a voice input function, users can provide the necessary information via voice. The device then sends the entered information to the server.

[0733] The server compares the received drug information with relevant databases. These databases contain information on drug efficacy, usage instructions, precautions, and the latest research data. The server utilizes a generative AI model to generate detailed drug information in a user-friendly format based on the received information.

[0734] The generated information is presented to the device in an interactive format. When using a smartphone or smart glasses, information can also be provided via voice. For example, if a user wants to know more about a pain reliever, they might receive instructions such as, "This medication relieves headaches. Please note that it is recommended to take it after meals."

[0735] In physical stores like pharmacies, drug information is instantly obtained by scanning product identification information (barcodes or QR codes). This information is also obtained via a server and provided to the user through a terminal.

[0736] Furthermore, when users report the effects and side effects of medications as feedback, the server evaluates this information and analyzes it using a generative AI model. This evaluation is also provided to pharmacists, enabling them to suggest improvements to users.

[0737] For example, if a user needs medication for stomach pain, scanning the product identification information will cause the server to provide details about the medication, and voice guidance will be provided on the terminal. An example of a prompt message might be, "Please explain this medication in detail. Please include its effects, usage instructions, and precautions."

[0738] Through these activities, the acquisition and understanding of pharmaceutical information will be facilitated, and the work of pharmacists will be supported, leading to the proposal of safer and more effective pharmaceutical therapies for users.

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The user uses a terminal to input medication and health information via voice input or touch operation. This input data is collected by the terminal and sent to the server. The input consists of information dictated or selected by the user, and this is sent to the server in digital format.

[0742] Step 2:

[0743] The server compares the received drug information with the relevant database. The database stores detailed information about the drugs, and by matching this data with the user's input data, the server retrieves the relevant information. In this process, the input is drug information from the user, and the output is detailed information about the matched drugs.

[0744] Step 3:

[0745] The server uses a generative AI model to generate drug efficacy, usage instructions, and precautions that are easy for users to understand, based on the cross-referenced information. The data processing performed here involves converting the data into natural language and preparing it for interactive presentation. The input is drug details from the database, and the output is drug information presented in a user-friendly format.

[0746] Step 4:

[0747] The terminal presents the user with generated drug information received from the server. The collected information is provided to the user through voice and screen displays, guiding them to ask additional questions in an interactive format. The input is formatted drug information from the server, and the output is voice guidance and screen displays for the user.

[0748] Step 5:

[0749] After taking medication, users input feedback about changes in their physical condition into a terminal. This feedback data is sent from the terminal to the server. The input is user feedback information, and the transmitted and stored output is detailed data used for further evaluation.

[0750] Step 6:

[0751] The server uses a generated AI model based on feedback information to evaluate the effectiveness of medications and, if necessary, generates improvement suggestions for pharmacists. The input is feedback data, and the output is support information that enables pharmacists to evaluate and make suggestions.

[0752] Step 7:

[0753] The generated suggestions are sent to the pharmacist's terminal. The pharmacist then uses this information to approve or modify the suggestions as needed. The input is suggestion data from the server, and the modified output is the safe drug information and advice ultimately provided to the user.

[0754] 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.

[0755] This invention provides a system that integrates the management of drug information and the handling of user emotions through a combination of user, terminal, server, and emotion engine. This system aims to support users in using medications with confidence and to enable pharmacists to perform their duties efficiently.

[0756] The user uses the device to input information about prescribed medications, their physical condition, and their emotions. Voice input is also available, allowing for smooth information provision even in situations where touch input is difficult. The device sends this information to the server, which centrally manages and processes the data.

[0757] The server first cross-references drug information with relevant databases to extract drug efficacy, usage instructions, precautions, and other relevant information. Next, it uses an emotion engine to analyze the user's emotions. Emotion recognition technology can identify the user's mental state, such as whether they are tense, anxious, or hesitant to ask a question.

[0758] Based on insights provided by the emotion engine, the generated drug information is adaptively adjusted. For example, if a user is particularly anxious, the server will adjust the way information is presented, such as providing more detailed explanations and additional support information. Flexible dialogue tailored to the user's emotions is also provided to encourage users to ask questions with confidence.

[0759] The server also provides pharmacists with information that takes emotional data into account when making suggestions based on changes in the user's physical condition. This allows pharmacists to communicate with users while considering their psychological state, enabling them to provide more effective care. For example, if the data indicates that the user is anxious, the pharmacist can receive suggestions to provide careful explanations accordingly.

[0760] This system integrates drug information management with emotion-based responses, enhancing user confidence and improving the quality of pharmacist services. For example, it could be used to provide more detailed drug information than usual to anxious users, and to continuously offer reassuring suggestions to pharmacists.

[0761] The following describes the processing flow.

[0762] Step 1:

[0763] The user uses the device to input information about medications, health conditions, and emotions. Input can be in text or voice format; if voice is used, the device converts the voice to text.

[0764] Step 2:

[0765] The terminal sends all the entered information to the server at once. This information includes the name of the medication, the user's symptoms, and their emotional response to them.

[0766] Step 3:

[0767] The server compares the received drug information with a database and extracts the efficacy, dosage, and precautions for the relevant drug. Simultaneously, it uses an emotion engine to analyze the user's emotions.

[0768] Step 4:

[0769] The server determines how the information should be presented based on the sentiment analysis results. For example, if anxiety is detected, the server prepares a detailed explanation to address it.

[0770] Step 5:

[0771] The server sends the generated information back to the terminal and displays it to the user in an interactive format. The information is presented in a relaxed tone, taking the user's emotions into consideration.

[0772] Step 6:

[0773] After taking medication, users input feedback via their device regarding changes in their physical condition and their emotions at the time. This information is also sent to the server.

[0774] Step 7:

[0775] The server evaluates the effectiveness of drug use based on feedback data. It also considers emotional data to provide additional support that reassures the user.

[0776] Step 8:

[0777] The server generates suggestions for pharmacists based on the user's physical condition and emotions, and notifies them via the terminal. This allows pharmacists to comprehensively understand the user's situation and adjust their support accordingly.

[0778] Step 9:

[0779] The pharmacist reviews the suggestion and provides prescription revisions and additional support as needed. The pharmacist's terminal displays response options that also take the user's feelings into consideration.

[0780] This process will lead to the creation of a system in which both users and pharmacists receive appropriate and effective support.

[0781] (Example 2)

[0782] 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".

[0783] In recent years, the amount of information available on chemicals has increased, making it difficult for users to quickly and accurately obtain information that is relevant to their needs. Furthermore, anxiety and doubts about the use of chemicals often become a psychological burden for users. Therefore, there is a need for experts to provide more effective support to ensure that users can use chemicals with confidence.

[0784] 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.

[0785] In this invention, the server includes means for receiving chemical information and health status information from the user, means for matching relevant information sources based on the received information and generating information using source technology, and means for dynamically adjusting the provision of information according to the user's emotions using emotion analysis technology. As a result, users can use chemicals with peace of mind, and experts can provide effective support that takes into account the user's psychological state.

[0786] "Chemical product information" refers to a series of pieces of information related to a chemical product, such as its name, ingredients, effects, usage instructions, and precautions.

[0787] "Health status information" refers to information such as the user's physical condition, symptoms, recent changes in their physical condition, and health concerns.

[0788] "Emotional analysis technology" is a technology that analyzes user input data to identify and evaluate the user's current emotional state.

[0789] "Source technology" refers to technologies that use artificial intelligence and machine learning models to generate new information and insights based on input data.

[0790] "Information sources" refer to databases and documents containing detailed information about chemical products.

[0791] A "specialist" refers to a professional who possesses knowledge about the use and effects of chemicals, typically such as a pharmacist or a doctor.

[0792] "Dynamic adjustment" means changing the content of information or services in real time in response to changes in circumstances or input data.

[0793] This system is initiated when the user inputs chemical information and health status information via an information terminal. This input can also be done using voice input technology, allowing for flexible information provision tailored to the user's situation. The terminal formats this data and immediately sends it to the server.

[0794] The server uses a sophisticated database system to compare received chemical information with existing information sources. Software components used include a database management system and a generative AI model. During this process, the server generates information on the efficacy, usage, and precautions of the chemicals, organizes it, and prepares it for optimal delivery to users.

[0795] Furthermore, the server uses emotion analysis technology to determine the user's emotional state from their input and dynamically adjusts the content and presentation of the information it provides. This adjustment process includes utilizing generative AI models to generate information that reduces the user's psychological burden. For example, if a user inputs "I'm worried about side effects," the server will provide detailed information about side effects and how to deal with them.

[0796] Furthermore, the system is designed so that the server notifies experts of the generated information and suggestions on their terminals, allowing experts to review, approve, or revise that information. This enables experts to communicate more accurately with users.

[0797] As a concrete example, a prompt might read, "Generate information to present detailed side effects of a medication and how to manage them, in case the user is feeling anxious." Based on this prompt, the server performs the process of generating and presenting the information.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] Users input chemical information and health status information using an information terminal. Voice input functionality can be used to provide information more easily. The entered data is formatted into items such as chemical name, symptoms, and emotions.

[0801] Step 2:

[0802] The terminal sends user input data to the server. The input data is securely transferred over the network and received by the server. The received data is temporarily stored in a database, preparing it for subsequent processing.

[0803] Step 3:

[0804] The server compares the received chemical information with the information sources in the database. Using the generative AI model employed, it extracts relevant information such as efficacy, usage instructions, and precautions. In this process, the server identifies the information that best matches the entered chemical name, and the data is output in an organized format.

[0805] Step 4:

[0806] The server utilizes sentiment analysis technology to evaluate the user's emotional state. It analyzes keywords included in the input data to identify the user's anxieties and tensions. The analysis results are used to customize the information provided, generating emotion-based insights.

[0807] Step 5:

[0808] Based on the results of sentiment analysis, the server dynamically adjusts the content and presentation method of the information provided to the user. Using a generative AI model, the information is processed to appropriately meet the requested content, and detailed guides to alleviate anxiety and specific countermeasures are output.

[0809] Step 6:

[0810] The server sends the generated information to the user's terminal in an interactive format, allowing the user to review it. The information is displayed in a user-friendly format on the terminal's interface. This information provision allows users to deepen their understanding and gain a sense of security.

[0811] Step 7:

[0812] The server notifies the expert's terminal of the generated information and suggestions. The expert reviews the received information and makes corrections or approvals as needed. Through this process, the expert can provide more appropriate support to the user.

[0813] (Application Example 2)

[0814] 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".

[0815] In physical stores such as pharmacies and drugstores, there is a need to effectively alleviate the anxiety and worries that customers feel about medications and to enable them to use medications with peace of mind. In particular, there is a lack of systems that can provide appropriate medication information while taking into account the emotional state of the customer. Pharmacists need to improve the quality of service by communicating more appropriately according to the physical condition and emotional state of the customer.

[0816] 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.

[0817] In this invention, the server includes means for receiving drug information and health information from the user; means for matching the received drug information with a relevant database and generating drug efficacy, usage instructions, and precautions using a generative machine learning model; and means for analyzing the user's emotional data and adaptively adjusting the information presentation using a generative machine learning model. This enables flexible information provision according to the user's emotional state, thereby increasing their sense of security and supporting the pharmacist's work.

[0818] A "user" is an individual who uses this system to obtain medication information and provides information about their own health condition and emotions.

[0819] "Drug information" refers to information such as the name of the drug, its effects, how to use it, and precautions.

[0820] "Health information" refers to information about the user's health status, specifically including symptoms, body temperature, and other physical conditions.

[0821] A "generative machine learning model" is a sophisticated algorithm used to analyze received data and generate necessary information.

[0822] "Emotional data" refers to information about the user's emotional state, specifically data that uses indicators such as anxiety, tension, and a sense of security.

[0823] "Adaptively adjusting information presentation" refers to dynamically changing the content and format of the information provided according to the user's emotional state.

[0824] A "pharmacist" is a professional who provides medications and instructs users, supporting them in using medications safely through communication.

[0825] To implement this invention, the system requires a series of hardware and software components. This system includes a user terminal, a processing server, and a display device such as smart glasses.

[0826] The user's device is equipped with an interface for voice input and sentiment data collection. This interface includes a microphone for receiving voice input and speech recognition software for converting speech into text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the speech data into text. This data is then processed by IBM Watson's sentiment analysis API, which analyzes the user's sentiment data.

[0827] The server uses a machine learning model generated based on drug information, health information, and emotional data received from the terminal to determine the efficacy, usage, and precautions of the medications to be provided to the user. The server also communicates with a database in a cloud environment (such as AWS Lambda) to retrieve relevant drug information.

[0828] When adaptively adjusting information presentation to take into account the user's emotional state, the level of detail and explanation of the information are flexibly changed based on the results of emotion analysis. The information generated by the server is visually displayed on smart glasses, enhancing the user's sense of security.

[0829] As a concrete example, a pharmacist wearing smart glasses could detect a customer's anxiety and then check detailed medication information and usage instructions, or provide guidance to offer additional reassurance. An example of a prompt message for the generating AI model might be, "A customer with cold symptoms is anxious about using a spray-type medication. Please suggest ways to provide detailed explanations and guidance to reassure them." This would create a system where pharmacists appropriately provide necessary information according to the user's emotional state, enabling effective communication.

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] The user's device receives voice input. When the user speaks information about medications into the device, the voice input module captures the voice and saves it as audio data. The input is the user's voice data, and the output is the data saved as an audio file.

[0833] Step 2:

[0834] The device's speech recognition software converts the received audio file into text data. Specifically, it uses the Google Cloud Speech-to-Text API to analyze the audio data and convert it into a string. The output is the user's spoken content in text format.

[0835] Step 3:

[0836] Text data, along with the user's health and emotional data, is sent to the server. The server then uses this data to prepare to retrieve medication information from relevant databases. The input consists of user text, health information, and emotional data, while the output is the transmission of data to the server.

[0837] Step 4:

[0838] The server extracts relevant drug information from the database based on the text data. Here, it searches the database based on the received information such as the drug name, and retrieves the efficacy, usage instructions, and precautions for the corresponding drug. The output is the relevant drug information.

[0839] Step 5:

[0840] The server analyzes the user's emotional data using IBM Watson's Sentiment Analysis API. The server analyzes the user's emotional state from text data and generates data identifying states such as tension, relief, and anxiety. The output is the analysis result regarding the user's emotions.

[0841] Step 6:

[0842] The server integrates drug information and sentiment analysis results, and uses a generative AI model to adjust the content of the information presented. Based on the emotional state, it flexibly changes the content and details of the information presented as needed. Through this process, the generated information is optimized to enhance the user's sense of security.

[0843] Step 7:

[0844] The server transmits the adjusted medication information to the smart glasses. This information is visually displayed on the smart glasses worn by the pharmacist, allowing the pharmacist to obtain appropriate information and provide appropriate care to the user. The input is the adjusted medication information, and the output is the display of this information on the smart glasses.

[0845] 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.

[0846] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] 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 robot 414.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means of receiving drug information and health information from users,

[0869] A means for generating drug efficacy, usage instructions, and precautions using artificial intelligence, based on received drug information, and by cross-referencing it with relevant databases.

[0870] A means of displaying the generated information in an interactive format on the user's terminal,

[0871] A means of receiving additional feedback from users and evaluating the effects of a drug using artificial intelligence,

[0872] A means for pharmacists to generate suggestions based on changes in the user's physical condition,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising means for receiving voice input and converting it into text format.

[0876] (Claim 3)

[0877] The system according to claim 1, comprising means for notifying the generated drug information and suggestions on the pharmacist's terminal and enabling the pharmacist to approve or modify them.

[0878] "Example 1"

[0879] (Claim 1)

[0880] A means of receiving information about medications and health status from users,

[0881] A means for generating drug effects, usage instructions, and precautions using a generating AI, based on received drug information, and matching it with relevant databases.

[0882] A means of displaying the generated information in an interactive format on the user's communication device and responding to the user's additional questions,

[0883] A means of receiving feedback information from users and evaluating the efficacy of a drug using a generating AI,

[0884] A means for automatically generating suggestions for health managers based on changes in the user's health status,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, comprising means for processing voice input and converting it into text data.

[0888] (Claim 3)

[0889] The system according to claim 1, wherein the generated drug information and suggested content are notified to the health manager's communication device, and the health manager has means to approve or edit them.

[0890] "Application Example 1"

[0891] (Claim 1)

[0892] A means of receiving drug information and health information from users,

[0893] A means for generating drug efficacy, usage instructions, and precautions using artificial intelligence, based on received drug information, and by cross-referencing it with relevant databases.

[0894] A means of displaying the generated information in an interactive format on the user's terminal,

[0895] A means of receiving additional feedback from users and evaluating the effects of a drug using artificial intelligence,

[0896] A means of reading product identification information in physical stores such as pharmacies and providing users with audio information about the medication,

[0897] A means for pharmacists to generate suggestions based on changes in the user's physical condition,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, comprising means for receiving voice input and converting it into text format.

[0901] (Claim 3)

[0902] The system according to claim 1, comprising means for notifying the generated drug information and suggestions on the pharmacist's terminal and enabling the pharmacist to approve or modify them.

[0903] "Example 2 of combining an emotion engine"

[0904] (Claim 1)

[0905] A means of receiving chemical information and health status information from users,

[0906] A means for matching relevant information sources based on received chemical information and generating the effects, usage methods, and precautions of the chemical using generation technology,

[0907] A means for generating information, evaluating the user's emotional state, and displaying it on the user's information terminal in an optimal format according to that state,

[0908] A means for identifying the user's emotions using emotion analysis technology and dynamically adjusting the information provided based on those emotions,

[0909] A means of receiving additional information from users and analyzing the effects of chemicals using source technology,

[0910] A means for generating suggestions for experts based on changes in the user's health status and emotional information,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, comprising means for receiving audio data and converting it into text information.

[0914] (Claim 3)

[0915] The system according to claim 1, comprising means for notifying experts of generated chemical information and proposals on their information terminals, and enabling experts to approve or modify them.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] A means of receiving drug information and health information from users,

[0919] A means for generating drug efficacy, usage instructions, and precautions based on received drug information, by matching it with relevant databases and using a generative machine learning model,

[0920] A means of displaying the generated information interactively on the user's display device,

[0921] A means of receiving additional feedback from users and evaluating the effects of a drug using a generative machine learning model,

[0922] A means for pharmacists to generate suggestions based on changes in the user's physical condition,

[0923] A means of analyzing user emotion data and adaptively adjusting information presentation using a generative machine learning model,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, comprising means for receiving voice input and converting it into text format.

[0927] (Claim 3)

[0928] The system according to claim 1, comprising means for notifying healthcare professionals of generated drug information and suggestions on their display devices, and enabling approval or modification by healthcare professionals. [Explanation of symbols]

[0929] 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. A means of receiving drug information and health information from users, A means for generating drug efficacy, usage instructions, and precautions using artificial intelligence, based on received drug information, and by cross-referencing it with relevant databases. A means of displaying the generated information in an interactive format on the user's terminal, A means of receiving additional feedback from users and evaluating the effects of a drug using artificial intelligence, A means for pharmacists to generate suggestions based on changes in the user's physical condition, A system that includes this.

2. The system according to claim 1, comprising means for receiving voice input and converting it into text format.

3. The system according to claim 1, comprising means for notifying the generated drug information and suggestions on the pharmacist's terminal and enabling the pharmacist to approve or modify them.

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