System

Abstract

A system is provided.SOLUTION: The system includes a means for evaluating the combination of medicines by using the generated AI, a means for proposing a dosage schedule based on the life rhythm of the user, a means for inputting side effect information by the user, a means for storing the side effect information in a database and periodically feeding back the side effect information to a relevant organization, and a means for managing the remaining amount of the medicine of the user and automatically proposing replenishment.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Medication administration is subject to various constraints, and managing a medication schedule can be complicated, especially when multiple medications are being taken. Various management challenges exist, such as side effects due to forgetting to take a medication or taking it at inappropriate intervals. Managing remaining medication and supplement amounts and determining optimal dosing timing to maximize effectiveness are also important. There is a need to provide support that solves these problems and maximizes the effectiveness of medications. [Means for solving the problem]

[0005] The above-mentioned problems are solved by a system that uses generative AI to include a means for evaluating drug interactions, a means for proposing a dosing schedule based on the user's lifestyle, a means for the user to input side effect information, a means for storing the side effect information in a database and periodically providing feedback to relevant institutions, and a means for managing the user's remaining medication and automatically suggesting refills. This system further includes a means for the generative AI to generate appropriate answers to user questions, and a means for generating appropriate countermeasures for reports of drug side effects by the user.

[0006] "Generative AI" is a technology that uses artificial intelligence technology to perform natural language processing and data analysis to generate appropriate responses to users' questions and requests.

[0007] A "drug" is a chemical or biological substance used to treat or prevent a disease or condition, or to maintain health.

[0008] "Drug interactions" refers to interactions that occur when multiple drugs are taken simultaneously or within a short period of time, and takes into consideration the possibility that this may result in changes in effectiveness or side effects.

[0009] "Dosing schedule" refers to the planned time arrangement and sequence for taking medications at regular and appropriate times.

[0010] "Side effects" are undesirable effects that occur as a result of a drug other than its primary therapeutic purpose.

[0011] A "database" is a collection of electronically stored data, an organized system that allows specific information to be efficiently recorded, managed, and retrieved.

[0012] "Remaining quantity management" refers to the management activity of understanding the inventory status of medicines and supplements and taking measures such as replenishing them at the appropriate time.

[0013] "Automatic replenishment" is a function that allows the system to automatically arrange for the replenishment of medications and supplements when certain conditions are met.

[0014] "Relevant institutions" refers to public or private organizations, such as medical institutions, pharmacies, and research institutions, that require or provide information on the effects and side effects of drugs.

[0015] "User" refers to an individual who uses this system to manage or consult about medication use. [Brief explanation of the drawings]

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

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining amounts. The system operates by exchanging information between the server, terminals, and users.

[0038] User registration and initial drug information settings

[0039] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[0040] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[0041] Example: When Tanaka starts the app for the first time, she enters her name, age, medications she is taking (drug A, drug B), and any side effects she has had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Tanaka.

[0042] Conversational Concierge

[0043] User: Enter questions about drug interactions and side effects.

[0044] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[0045] Example: When Tanaka asks, "Can I take medicine A and medicine B at the same time?", the generated AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[0046] Suggested dosing schedule

[0047] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0048] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[0049] Example: Based on Tanaka's daily routine, the AI ​​generator will propose a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." and notify Tanaka's device of this.

[0050] Side effect reporting and feedback

[0051] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[0052] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[0053] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0054] Example: If Tanaka reports feeling nauseous after taking medicine B, the generated AI will advise him on how to deal with the situation, saying, "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[0055] Remaining quantity management and automatic replenishment

[0056] User: Enter the remaining amount of medication.

[0057] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[0058] Server: Places automatic orders to the online store as needed.

[0059] Example: When Tanaka inputs into the app that he is running low on medicine A, the server sends a notification saying, "You are running low on medicine A. Would you like to place an order automatically?" If Tanaka accepts, the server automatically completes the order with the online store.

[0060] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[0061] The processing flow will be explained below.

[0062] User registration and initial drug information settings

[0063] Step 1:

[0064] User: Launches the app for the first time.

[0065] Step 2:

[0066] Terminal: Display the user registration screen.

[0067] Step 3:

[0068] User: Enter personal information (name, age, gender, etc.), a list of medications being taken, and a history of side effects.

[0069] Step 4:

[0070] Terminal: Sends the entered information to the server.

[0071] Step 5:

[0072] Server: Stores the received personal information and medication information in a database.

[0073] Conversational Concierge

[0074] Step 1:

[0075] User: Enter questions about drug interactions and side effects.

[0076] Step 2:

[0077] Terminal: Sends the question to the generation AI.

[0078] Step 3:

[0079] Server: The generation AI analyzes the question and generates an appropriate answer.

[0080] Step 4:

[0081] Server: Sends the generated answer to the device.

[0082] Step 5:

[0083] Terminal: Display the answer to the user.

[0084] Suggested dosing schedule

[0085] Step 1:

[0086] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0087] Step 2:

[0088] Terminal: Sends the inputted life rhythm to the server.

[0089] Step 3:

[0090] Server: Based on lifestyle and medication information, the generation AI calculates the optimal dosing schedule.

[0091] Step 4:

[0092] Server: Sends the calculated medication schedule to the terminal.

[0093] Step 5:

[0094] Terminal: Notifies the user of their medication schedule.

[0095] Side effect reporting and feedback

[0096] Step 1:

[0097] User: If you experience any side effects, open the side effect report screen.

[0098] Step 2:

[0099] Device: Displays a screen for selecting the type of side effect and symptoms.

[0100] Step 3:

[0101] User: Enter specific symptoms and submit report.

[0102] Step 4:

[0103] Terminal: Sends the entered side effect information to the server.

[0104] Step 5:

[0105] Server: Stores the received side effect information in a database.

[0106] Step 6:

[0107] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[0108] Step 7:

[0109] Server: Sends the generated solution to the terminal.

[0110] Step 8:

[0111] Terminal: Show the user how to fix the problem.

[0112] Step 9:

[0113] Server: Regularly feeds back accumulated side effect information to relevant institutions.

[0114] Remaining quantity management and automatic replenishment

[0115] Step 1:

[0116] User: Enter remaining amount of medicine or supplement.

[0117] Step 2:

[0118] Terminal: Sends the entered remaining amount information to the server.

[0119] Step 3:

[0120] Server: Saves remaining information in a database.

[0121] Step 4:

[0122] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[0123] Step 5:

[0124] Server: Sends a notification message to the terminal.

[0125] Step 6:

[0126] Terminal: Display a restock notification to the user.

[0127] Step 7:

[0128] User: Approves the replenishment.

[0129] Step 8:

[0130] Terminal: Sends authorization information to the server.

[0131] Step 9:

[0132] Server: After receiving replenishment approval, automatically place an order to the online store.

[0133] Example 1

[0134] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0135] In modern self-medication, users are required to properly manage multiple medications and take them effectively. However, various tasks, such as drug interactions, appropriate dosing schedules, side effect management, and remaining medication amounts, are complex and place a significant burden on users. Furthermore, inadequate medication management, particularly for elderly people and those with chronic diseases, increases health risks. Another issue is the lack of reporting of side effects and appropriate feedback.

[0136] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0137] In this invention, the server includes a means for evaluating drug interactions using a generative AI, a means for proposing a drug-taking schedule based on the user's lifestyle, and a means for the user to input side effect information. This allows the user to easily manage multiple drugs, take them on an appropriate schedule, and report side effects, thereby reducing health risks.

[0138] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[0139] A "means for evaluating drug interactions" is a system that analyzes the possibility of multiple drugs interacting with each other and displays the results.

[0140] The "means for suggesting a medication schedule" is a system that calculates and suggests appropriate medication times based on the user's lifestyle and medication characteristics.

[0141] The "means for inputting side effect information" is an interface that allows users to input symptoms of side effects experienced after taking a drug into an application or system.

[0142] A "database" is a system that systematically stores information and data and is constructed to make it easy to search and use.

[0143] A "feedback mechanism" is a system that analyzes collected information and sends the results to specific relevant organizations and users.

[0144] The "means for managing remaining amounts" is a system that monitors the remaining amount of medication the user owns and suggests refilling it if necessary.

[0145] "Means for automatic ordering" refers to a system that automatically places orders with online stores and pharmacies based on remaining medication levels and the need for replenishment.

[0146] The "means of generating appropriate answers" is a system in which a generative AI derives and presents the most appropriate answer to a question from a user.

[0147] The "means of generating and notifying appropriate countermeasures" is a system in which a generative AI generates optimal countermeasures based on side effect information reported by users and notifies the user.

[0148] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining medications. The system operates by exchanging information between the server, terminals, and users.

[0149] User registration and initial drug information settings

[0150] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[0151] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[0152] Example: Person A starts the app for the first time and enters their name, age, medications they are taking (drug A, drug B), and any side effects they have had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Person A.

[0153] Conversational Concierge

[0154] User: Enter questions about drug interactions and side effects.

[0155] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[0156] Example: If Person A asks, "Is it okay to take medicine A and medicine B at the same time?", the generated AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[0157] Suggested dosing schedule

[0158] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0159] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[0160] Example: Based on Mr. A's daily routine, the generating AI proposes a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m.," and notifies Mr. A's device.

[0161] Side effect reporting and feedback

[0162] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[0163] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[0164] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0165] Example: If Person A reports feeling nauseous after taking Medicine B, the generated AI will advise the person on how to deal with the situation, such as "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[0166] Remaining quantity management and automatic replenishment

[0167] User: Enter the remaining amount of medication.

[0168] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[0169] Server: Places automatic orders to the online store as needed.

[0170] Example: When Person A enters into the app that he or she is running low on Medicine A, the server sends a notification saying, "Your remaining supply of Medicine A is low. Would you like to place an order automatically?" If Person A agrees, the server automatically completes the order with the online store.

[0171] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[0172] Example prompts for generative AI models

[0173] 1. "Can I take medication A and medication B at the same time?"

[0174] 2. "I have breakfast at 8:00, lunch at 12:00, and dinner at 19:00. Please tell me the medication schedule that fits this lifestyle."

[0175] 3. "I feel nauseous after taking medicine B. What should I do?"

[0176] The above is an embodiment of the present invention.

[0177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0178] User registration and initial drug information settings

[0179] Step 1:

[0180] User: Launch the app and the user registration screen will appear.

[0181] What it does: When a user launches the app for the first time, a form to enter personal information is displayed.

[0182] Step 2:

[0183] User: Enter personal information, a list of medications being taken, and a history of side effects.

[0184] Input: Name, age, gender, medications being taken, side effect history.

[0185] Specific behavior: The user fills in each field.

[0186] Step 3:

[0187] Terminal: Sends the entered information to the server.

[0188] Input: Personal and medication information entered by the user.

[0189] Specific operation: The terminal sends the input data to the server.

[0190] Output: Data packets sent to the server.

[0191] Step 4:

[0192] Server: Stores the received information in a database.

[0193] Input: Data packets sent from the terminal.

[0194] Specific operation: The server receives the data packet and stores it in a database.

[0195] Output: User information stored in the database.

[0196] Conversational Concierge

[0197] Step 1:

[0198] User: Enter questions about drug interactions and side effects.

[0199] Input: The question from the user.

[0200] What happens: The user types a question into an input field in the app.

[0201] Step 2:

[0202] Terminal: The generation AI analyzes the input question and generates an appropriate answer.

[0203] Input: The question from the user.

[0204] Specific operation: The generative AI performs natural language processing and analyzes the question content.

[0205] Output: Correct answer.

[0206] Step 3:

[0207] Terminal: Displays the generated answer to the user.

[0208] Input: Answer data from the generation AI.

[0209] What it does: Formats the response data for display to the user.

[0210] Output: The answer displayed on the user's terminal.

[0211] Suggested dosing schedule

[0212] Step 1:

[0213] User: Enter your daily routine.

[0214] Input: User's daily rhythm information (e.g., breakfast, lunch, and dinner times).

[0215] Specific operation: The user inputs information about their daily rhythm.

[0216] Step 2:

[0217] Device: Based on the inputted information on daily rhythms, the AI ​​generates the optimal medication schedule.

[0218] Input: User's lifestyle information.

[0219] Specific operation: The generating AI performs calculations based on the given life rhythm.

[0220] Output: Optimal dosing schedule.

[0221] Step 3:

[0222] Terminal: Notifies the user of the calculated medication schedule.

[0223] Input: Calculated dosing schedule.

[0224] Specific operation: Notification information is sent to the user's device and displayed.

[0225] Output: Medication schedule notification displayed on the user's device.

[0226] Side effect reporting and feedback

[0227] Step 1:

[0228] User: Open the side effect report screen and enter the symptoms.

[0229] Input: The side effect symptoms experienced by the user.

[0230] Specific action: The user enters their symptoms into the input field.

[0231] Step 2:

[0232] Terminal: Sends side effect information to the server.

[0233] Input: The side effect information entered.

[0234] Specific operation: The device sends side effect information to the server.

[0235] Output: Data packets sent to the server.

[0236] Step 3:

[0237] Server: Analyzes the input information and generates appropriate countermeasures.

[0238] Input: Side effect information.

[0239] Specific operation: The generative AI analyzes side effect information and considers appropriate countermeasures.

[0240] Output: Appropriate action.

[0241] Step 4:

[0242] Terminal: Notify the user of the generated solution.

[0243] Input: The generated solution.

[0244] Specific action: Format and send to inform the user of the corrective action.

[0245] Output: Actions displayed on the user's terminal.

[0246] Step 5:

[0247] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0248] Input: Side effect information.

[0249] Specific operation: The server stores side effect information and periodically creates and sends reports to relevant institutions.

[0250] Output: Side effect information stored in the database and feedback reports.

[0251] Remaining quantity management and automatic replenishment

[0252] Step 1:

[0253] User: Enter the remaining amount of medication.

[0254] Input: remaining amount of medication.

[0255] Specific behavior: The user enters the remaining amount of medication into the input field.

[0256] Step 2:

[0257] Terminal: Sends the entered remaining amount information to the server.

[0258] Input: Remaining medication information.

[0259] Specific operation: The device sends remaining battery information to the server.

[0260] Output: Data packets sent to the server.

[0261] Step 3:

[0262] Server: If the remaining amount falls below a threshold, an automatic replenishment suggestion is made.

[0263] Input: Remaining information.

[0264] What happens: The server checks the remaining amount and generates replenishment suggestions as needed.

[0265] Output: Automatic replenishment suggestion notification.

[0266] Step 4:

[0267] Terminal: If the user accepts automatic ordering, places an order with the online store.

[0268] Enter: Automatic Purchase Order Acceptance.

[0269] Specific action: The user presses the accept button.

[0270] Output: An order request to the online store.

[0271] Step 5:

[0272] Server: Completes the order with the online store.

[0273] Input: Order information accepted by the user.

[0274] Specific operation: The server sends an order request to the online store.

[0275] Output: Order data sent to the online store.

[0276] (Application example 1)

[0277] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] In conventional drug management systems, managing drug interactions and side effects is done manually, which is not only inefficient but also increases the risk of misuse. Furthermore, managing security devices requires checking their status and configuring them individually, which is extremely time-consuming. To solve these problems, there is a growing need for a centralized management system that utilizes generative AI.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0280] In this invention, the server includes means for evaluating drug interactions using a generating AI, means for proposing a dosing schedule based on the user's lifestyle, means for the user to input side effect information, means for evaluating the status of security devices and proposing optimal settings and operation schedules, and means for analyzing security-related problem reports and generating and notifying appropriate countermeasures. This enables drug management and security device management to be performed in a unified manner, enabling efficient and safe operation.

[0281] "Generative AI" uses artificial intelligence techniques such as natural language processing to generate appropriate answers to questions from users.

[0282] The "means for evaluating drug interactions" is a function that analyzes the effects of taking multiple drugs simultaneously and instructs the user on the appropriate method of taking them.

[0283] The "means for suggesting a medication schedule" is a function that calculates the optimal timing for taking medication based on the user's lifestyle and notifies the user.

[0284] "Means for inputting side effect information" refers to a data input interface that allows a user to report side effects of a drug.

[0285] The "means for storing in a database" refers to a system that safely stores the entered side effect information and manages it in a manner that allows it to be retrieved as needed.

[0286] "Means of providing feedback to relevant organizations" refers to a function that periodically sends accumulated side effect information to relevant organizations such as medical institutions.

[0287] "Means for managing remaining medication and automatically suggesting replenishment" refers to a function that monitors the remaining amount of medication possessed by the user and automatically suggests replenishment when it falls below a certain threshold.

[0288] "A means for evaluating the status of security equipment and proposing optimal settings and operation schedules" is a means for analyzing the operating status of security equipment such as surveillance cameras and sensors, and proposing optimal settings and schedules to users.

[0289] "Means for analyzing security-related problem reports and generating and notifying appropriate countermeasures" refers to a system that analyzes security device problems reported by users, generates appropriate countermeasures, and notifies the users.

[0290] The "means for managing automatic replenishment" is a function for monitoring the maintenance status of security equipment and the remaining amount of consumables, and for timely replenishment or replacement.

[0291] The system for implementing this invention uses generative AI to manage drug interactions, drug schedules, side effects, monitor the status of security devices, and automatically replenish medications. The system operates by exchanging information between a server, terminals, and users.

[0292] User registration and initial settings

[0293] User: When the application is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, side effect history, and security device information.

[0294] Terminal: Sends the entered information to the server. The user's personal information, medication information, and security device information are stored in the server's database.

[0295] Conversational Concierge

[0296] User: Enter questions about drug interactions and security device settings.

[0297] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[0298] For example, in response to the question, "My surveillance camera isn't recording. What should I do?", the AI ​​generator will answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[0299] Schedule suggestions

[0300] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times) and security device usage schedule.

[0301] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and security device operation schedule and notifies the user.

[0302] Server: Stores schedule information in a database and sends reminders to users when necessary.

[0303] For example, we suggest a schedule such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[0304] Reporting and Feedback on Side Effects and Problems

[0305] Users: If they experience a side effect from a medication or a problem with a security device, they can open a dedicated reporting screen and enter details of the symptoms or problem.

[0306] Device: Sends reported information to the server. The generated AI analyzes the input information and notifies the user of the appropriate countermeasures.

[0307] Server: The reported information is stored in a database and periodically provided to relevant authorities and security companies.

[0308] For example, if you report that "the sensor at the front door is not responding," the generating AI will notify you of the corrective action, such as "check the sensor battery and replace it if necessary," and this information will be regularly fed back to the relevant authorities.

[0309] Remaining quantity management and automatic replenishment

[0310] User: Enters remaining amounts and status of medications and security devices.

[0311] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[0312] Server: Automatically orders consumables and replacement parts as needed.

[0313] Example: "The sensor battery is low. Would you like to order a new battery automatically?" If the user agrees, the server will automatically complete the order with the online store.

[0314] Hardware and Software Used

[0315] Hardware: Smartphones, internet connections, security equipment (surveillance cameras, sensors, etc.)

[0316] software:

[0317] Programming language: Python

[0318] API: REST API for exchanging data related to the security system

[0319] Server: Security database on the cloud (e.g. Amazon AWS, Google Cloud)

[0320] Generative AI models: Natural Language Processing (NLP) models for generating answers to user questions (e.g., OpenAI GPT-4)

[0321] Add specific examples

[0322] Example prompts to input to the generative AI model

[0323] Question: "My security cameras aren't recording, what should I do?"

[0324] Prompt: "What should I do if my security camera isn't recording? Please list possible causes and solutions that apply to your answer."

[0325] My security camera isn't recording, what should I do?

[0326] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0327] Step 1:

[0328] (User registration and initial settings)

[0329] Input: The user enters personal information, medications, and security device information into the app.

[0330] Operation: The terminal sends the entered information to the server, which receives the information and stores it in a database.

[0331] Output: The user's personal information, medication information, and security device information are saved in the database.

[0332] Step 2:

[0333] (Interactive concierge function)

[0334] Input: The user inputs questions about drug interactions and security device settings.

[0335] How it works: The device sends a question to the generative AI model, which analyzes the question and generates an appropriate answer.

[0336] Output: The answer from the generative AI model is sent to the device and displayed to the user.

[0337] Specific operation: In response to the question, "My surveillance camera is not recording. What should I do?", the generative AI model generates the answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[0338] Step 3:

[0339] (Schedule proposal)

[0340] Input: The user inputs their daily routine and schedule for using security devices.

[0341] Operation: The device sends the input information to the server, which uses the generative AI model to calculate the optimal medication schedule and security device operation schedule.

[0342] Output: The generated schedule is notified to the user.

[0343] Specific operation: Based on the user's daily rhythm, the generating AI will make suggestions such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[0344] Step 4:

[0345] (Reporting and Feedback on Side Effects and Problems)

[0346] Input: To report side effects or problems with security devices, users enter details of symptoms or problems into a dedicated reporting screen.

[0347] Operation: The device sends the report information to the server, which uses the generative AI model to analyze the report information and generate appropriate countermeasures.

[0348] Output: The generated solutions are notified to the user. The reported information is also saved in a database and periodically fed back to the relevant organizations.

[0349] Specific operation: When you report that "the front door sensor is not responding," the generating AI will generate and notify you of the solution, such as "check the sensor battery and replace it if necessary."

[0350] Step 5:

[0351] (Remaining amount management and automatic replenishment)

[0352] Input: The user inputs the remaining amount and status of medicines and security devices.

[0353] Operation: The device sends the input information to the server. The server references the database and suggests automatic replenishment if the remaining amount falls below a threshold. If necessary, it automatically places an order with the online store.

[0354] Output: The user is notified of the replenishment proposal, and after user approval, automatic ordering is completed.

[0355] Specific operation: The user is notified, "The sensor battery is low. Would you like to automatically order a new battery?" If the user agrees, the server will automatically complete the order.

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

[0357] The following is a detailed description of the mode for carrying out this invention. This system uses generative AI to manage drug interactions, dosage schedules, and side effects, and automatically replenish remaining amounts. In addition, by combining it with an emotion engine, it can also respond to the user's emotional state.

[0358] User registration and initial drug information settings

[0359] User: When the app is first launched, the user is presented with a registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, any side effects they have experienced, and their emotional state.

[0360] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[0361] Example: When Tanaka starts the app for the first time, she answers questions about her name, age, medications she is taking (drug A, drug B), past side effects (skin rash), and initial emotional state. This allows the app to suggest the optimal medication schedule and emotional advice for Tanaka.

[0362] Conversational Concierge

[0363] User: Enter questions about drug interactions and side effects.

[0364] Terminal: Sends the question content to the generation AI and emotion engine.

[0365] Server: The generation AI analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The answer is then displayed to the user.

[0366] Example: When Tanaka asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." The emotion engine recognizes Tanaka's worried expression and advises, "If you are worried, we recommend that you consult a doctor."

[0367] Suggested dosing schedule

[0368] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0369] Terminal: Sends the inputted life rhythm to the server.

[0370] Server: Based on lifestyle and medication information, the generative AI and emotion engine calculate the optimal medication schedule.

[0371] Example: Based on Tanaka's daily rhythm, a schedule is suggested such as "Take medicine A at 8:00 AM and medicine B at 8:30 AM." If Tanaka looks busy, the emotion engine will make a flexible suggestion such as "If these times are difficult, it is also possible to shift the times of each dose by one hour."

[0372] Side effect reporting and feedback

[0373] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[0374] Terminal: Sends side effect information to the server. The generation AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of the appropriate course of action.

[0375] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0376] Example: If Tanaka reports feeling nauseous after taking drug B, the generative AI will advise him to "drink water and rest," and the emotion engine will recognize Tanaka's anxious feelings and send an additional message saying, "We will inform you of different measures the next time you take the drug."

[0377] Remaining quantity management and automatic replenishment

[0378] User: Enter remaining amount of medicine or supplement.

[0379] Terminal: Sends the entered remaining amount information to the server.

[0380] Server: Saves remaining information in a database. The emotion engine also references this information.

[0381] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[0382] Terminal: Display a restock notification to the user.

[0383] User: Approves replenishment.

[0384] Server: After receiving approval, the order is automatically placed with the online store.

[0385] Example: When Tanaka inputs that he is running low on medicine A, a notification is sent saying, "Your remaining medicine A is low. Would you like us to automatically place an order for you?" Tanaka accepts, and the server automatically completes the order. Furthermore, if the emotion engine recognizes that Tanaka is stressed, it displays the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[0386] This system allows users to manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in a unified manner, according to their emotional state, allowing them to maximize the effectiveness of their medications.

[0387] The processing flow will be explained below.

[0388] User registration and initial drug information settings

[0389] Step 1:

[0390] User: Launches the app for the first time.

[0391] Step 2:

[0392] Terminal: Display the user registration screen.

[0393] Step 3:

[0394] User: Answers initial setup questions to identify personal information (such as name, age, and gender), a list of medications taken, side effects, and emotional state.

[0395] Step 4:

[0396] Terminal: Sends the entered information to the server.

[0397] Step 5:

[0398] Server: Stores the received personal information and medication information in a database.

[0399] Conversational Concierge

[0400] Step 1:

[0401] User: Enter questions about drug interactions and side effects.

[0402] Step 2:

[0403] Terminal: Sends the question content to the generation AI and emotion engine.

[0404] Step 3:

[0405] Server: The generation AI analyzes the question content, and the emotion engine evaluates the user's emotional state.

[0406] Step 4:

[0407] Server: The generation AI generates an appropriate answer based on the analysis results.

[0408] Step 5:

[0409] Server: The emotion engine generates supplementary advice according to the emotional state.

[0410] Step 6:

[0411] Server: Sends the generated answers and supplementary advice to the terminal.

[0412] Step 7:

[0413] Terminal: The answer and supplementary advice are displayed to the user.

[0414] Suggested dosing schedule

[0415] Step 1:

[0416] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0417] Step 2:

[0418] Terminal: Sends the inputted life rhythm to the server.

[0419] Step 3:

[0420] Server: Generative AI calculates the optimal dosing schedule based on lifestyle rhythm information and medication information.

[0421] Step 4:

[0422] Server: The emotion engine evaluates the user's emotional state and checks whether the schedule places a burden on the user.

[0423] Step 5:

[0424] Server: Sends the calculated dosing schedule to the terminal.

[0425] Step 6:

[0426] Terminal: Notifies the user of their medication schedule.

[0427] Side effect reporting and feedback

[0428] Step 1:

[0429] User: If you experience any side effects, open the side effect report screen.

[0430] Step 2:

[0431] Device: Displays a screen for selecting the type of side effect and symptoms.

[0432] Step 3:

[0433] User: Enter specific symptoms and submit report.

[0434] Step 4:

[0435] Terminal: Sends the entered side effect information to the server.

[0436] Step 5:

[0437] Server: Stores the received side effect information in a database.

[0438] Step 6:

[0439] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[0440] Step 7:

[0441] Server: The emotion engine evaluates the user's emotional state and adds additional information to the response.

[0442] Step 8:

[0443] Server: Sends the generated solution and additional information to the terminal.

[0444] Step 9:

[0445] Terminal: Display solutions and additional information to the user.

[0446] Step 10:

[0447] Server: Stores accumulated side effect information in a database and periodically provides feedback to relevant institutions.

[0448] Remaining quantity management and automatic replenishment

[0449] Step 1:

[0450] User: Enter remaining amount of medicine or supplement.

[0451] Step 2:

[0452] Terminal: Sends the entered remaining amount information to the server.

[0453] Step 3:

[0454] Server: Saves remaining information in a database.

[0455] Step 4:

[0456] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[0457] Step 5:

[0458] Server: The emotion engine evaluates the user's emotional state and predicts their reaction to the notification.

[0459] Step 6:

[0460] Server: Sends a notification message to the terminal.

[0461] Step 7:

[0462] Terminal: Display a restock notification to the user.

[0463] Step 8:

[0464] User: Approves replenishment.

[0465] Step 9:

[0466] Terminal: Sends authorization information to the server.

[0467] Step 10:

[0468] Server: After receiving approval for replenishment, the server automatically places an order with the online store.

[0469] In this way, a system is realized that performs integrated medication management while also taking into account the user's emotional state.

[0470] Example 2

[0471] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0472] Conventional medication management systems make it difficult for users to efficiently manage multiple medications, requiring a great deal of effort to manage drug interactions, dosage schedules, side effects, and remaining medication amounts. Furthermore, the system does not respond to the user's emotional state, which can increase anxiety and stress. To solve this problem, a comprehensive medication management system that combines a generative AI model and an emotion engine is needed.

[0473] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for transmitting personal information and drug information entered by the user to the server and storing them in a database; a means for transmitting the user's question to the generation AI model and emotion engine, analyzing the question, evaluating the user's emotional state, and generating an appropriate answer; a means for calculating an optimal drug schedule based on the user's lifestyle information; a means for the user to input side effect information; a means for analyzing the side effect information, evaluating the user's emotional state, and notifying the user of appropriate measures; a means for storing the side effect information in a database and periodically providing feedback to relevant institutions; and a means for managing the user's remaining medication, automatically suggesting replenishment, and placing an order. This allows the user to efficiently manage multiple medications and quickly take appropriate measures. Furthermore, the support provided by the emotion engine minimizes the user's anxiety and stress.

[0474] 1. "Server" refers to a computer system that manages, stores, and processes data over a network.

[0475] 2. "Database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[0476] 3. "Generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to generate appropriate outputs (e.g., answers, suggestions, countermeasures, etc.) from input information.

[0477] 4. "Emotion engine" refers to a computer program that analyzes a user's emotional state based on input, facial expressions, voice, etc., and determines an appropriate response.

[0478] 5. "User" refers to an individual who uses this system.

[0479] 6. "Personal information" refers to information that can identify a specific individual, such as name, age, and gender.

[0480] 7. "Medication information" refers to detailed information about the medications a user is taking, such as the name, dosage, and time of administration.

[0481] 8. "Side effect information" refers to information about undesirable symptoms or reactions that occur as a result of taking a drug.

[0482] 9. "Lifestyle information" refers to information such as meal and activity times in a user's daily life.

[0483] 10. "Dosing schedule" refers to the specific time and order in which a user takes medication.

[0484] 11. "Balance management" refers to the process of monitoring the current level of medications or supplements taken by a user and replenishing them appropriately.

[0485] 12. "Relevant organizations" refers to organizations involved in the management of drugs and the monitoring of side effects, such as medical institutions, pharmacies, and insurance companies.

[0486] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI model and an emotion engine to manage drug interactions, dosage schedules, and side effects, automatically replenish remaining doses, and also responds to the user's emotional state. Hardware used includes a server, cloud storage, and user devices (smartphones, tablets, etc.). Software includes a generative AI, emotion engine, database management system, etc.

[0487] User registration and initial drug information settings

[0488] When a user launches the app, they are presented with a user registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. The device then sends this information to the server, which stores it in a database.

[0489] Examples:

[0490] When a user launches the app for the first time, they answer some initial questions about their name, age, medications they are taking (drug X, drug Y), past side effects (rashes), and emotional state. The device sends this information to the server, which stores it in a database.

[0491] Conversational Concierge

[0492] When a user inputs a question about drug interactions or side effects, the device sends the question to the generation AI and emotion engine. The server then uses the generation AI to analyze the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The device then displays the generated answer to the user.

[0493] Examples:

[0494] When a user asks, "Is it okay to take medication X and medication Y at the same time?", the generative AI responds, "Taking medication X and medication Y at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two," and the emotion engine recognizes the user's anxious expression and advises, "If you are worried, we recommend consulting a doctor." This is displayed to the user on the device.

[0495] Suggested dosing schedule

[0496] When a user inputs their daily rhythm (e.g., breakfast, lunch, and dinner times), the device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule and presents it to the user via the device.

[0497] Examples:

[0498] When a user inputs their daily routine, the system suggests a schedule, such as "Take medication X at 8:00 a.m. and medication Y at 8:30 a.m." If the user looks busy, the emotion engine suggests, "If these times are difficult, you can shift the times of each dose by one hour."

[0499] Side effect reporting and feedback

[0500] When a user experiences a side effect, they enter their symptoms on the side effect reporting screen, and the device sends the information to the server. The server then uses generative AI and an emotion engine to analyze the side effect information, generate appropriate countermeasures, and store them in a database. The appropriate countermeasures are notified to the user and periodically provided as feedback to relevant organizations.

[0501] Examples:

[0502] If a user reports feeling nauseous after taking drug Y, the generation AI will advise them to "drink water and rest," and the emotion engine will recognize the user's anxious feelings and generate an additional message saying, "We will inform you of different measures the next time you take the drug." The device will notify the user of this.

[0503] Remaining quantity management and automatic replenishment

[0504] When a user inputs the remaining amount of medicine or supplement, the device sends the remaining amount information to the server. The server stores the remaining amount information in a database, and when the remaining amount falls below a set threshold, it automatically generates a notification to replenish and displays the notification on the device. If the user approves the replenishment, the server automatically places an order with the online store.

[0505] Examples:

[0506] When the user inputs that they are low on medicine X, a notification is sent saying, "Your remaining medicine X is low. Would you like us to automatically place an order for you?" If the user agrees, the server will automatically complete the order. Also, if the emotion engine recognizes that the user is stressed, it will display the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[0507] Example prompts for generative AI models

[0508] Example prompt 1: Suggested medication schedule

[0509] "Please suggest the best dosing schedule based on the user's daily rhythm of taking medication X at 8:00 AM and medication Y at 8:30 AM."

[0510] Prompt example 2: Conversational concierge function

[0511] "User is asking if it's okay to take medication X and medication Y at the same time. Please provide some advice on how to do this safely. User seems a little nervous."

[0512] This system allows users to efficiently manage multiple medications and take appropriate measures quickly, while its emotional engine supports users in minimizing anxiety and stress.

[0513] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0514] Program processing flow

[0515] User registration and initial drug information settings

[0516] Step 1:

[0517] User: Launch the app and open the user registration screen.

[0518] Input: Name, age, gender, list of medications you are taking, side effect history, and default questions about your emotional state.

[0519] What happens: A user fills out a form.

[0520] Output: The entered user information.

[0521] Step 2:

[0522] Terminal: Sends the entered information to the server.

[0523] Input: Personal information and medication information entered by the user.

[0524] How it works: The device sends information over the network to the server.

[0525] Output: The user information sent.

[0526] Step 3:

[0527] Server: Stores the received user information in a database.

[0528] Input: User information sent from the device.

[0529] What it does: Executes a query that stores information in a database.

[0530] Output: User information stored in the database.

[0531] Conversational Concierge

[0532] Step 1:

[0533] User: Enter questions about drug interactions and side effects.

[0534] Input: The user's question.

[0535] What it does: Enter a question into the form.

[0536] Output: The question entered.

[0537] Step 2:

[0538] Terminal: Sends the question content to the generative AI model and emotion engine.

[0539] Input: The user's question.

[0540] Operation: Sends the query to the server.

[0541] Output: The question submitted.

[0542] Step 3:

[0543] Server: The generative AI model analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer.

[0544] Input: The user's question.

[0545] How it works: A generative AI model analyzes the question, and an emotion engine assesses the user's emotional state.

[0546] Output: The generated answer.

[0547] Step 4:

[0548] Terminal: Displays the generated answer to the user.

[0549] Input: The answer output from the generative AI model and the emotion engine.

[0550] Behavior: Displays the answer in the user interface.

[0551] Output: The answer shown to the user.

[0552] Suggested dosing schedule

[0553] Step 1:

[0554] User: Enter lifestyle information (breakfast, lunch, dinner times, etc.).

[0555] Input: User's lifestyle information.

[0556] Action: Enter your lifestyle information in the input form.

[0557] Output: Inputted life rhythm information.

[0558] Step 2:

[0559] Terminal: Sends the inputted life rhythm information to the server.

[0560] Input: User's lifestyle information.

[0561] Operation: Sends life rhythm information to the server.

[0562] Output: Transmitted life rhythm information.

[0563] Step 3:

[0564] Server: A generative AI model and emotion engine calculate the optimal medication schedule based on lifestyle information.

[0565] Input: User's lifestyle information.

[0566] How it works: The generative AI model analyzes daily rhythm information, and the emotion engine evaluates the patient's emotional state to create an optimal medication schedule.

[0567] Output: Optimal dosing schedule.

[0568] Step 4:

[0569] Terminal: Displays the generated dosing schedule to the user.

[0570] Input: The dose schedule output from the generative AI model and the emotion engine.

[0571] What it does: Displays the medication schedule in the user interface.

[0572] Output: The dosing schedule displayed to the user.

[0573] Side effect reporting and feedback

[0574] Step 1:

[0575] User: Open the side effect report screen and enter the symptoms.

[0576] Input: Side effect symptoms.

[0577] Action: Enter your symptoms in the form.

[0578] Output: The entered side effect information.

[0579] Step 2:

[0580] Terminal: Sends side effect information to the server.

[0581] Input: Side effect symptoms.

[0582] Action: Sends side effect information to the server.

[0583] Output: The side effect information sent.

[0584] Step 3:

[0585] Server: The generative AI model analyzes side effect information, and the emotion engine evaluates the emotional state and generates appropriate countermeasures.

[0586] Input: Side effect symptoms.

[0587] How it works: A generative AI model analyzes side effect information, and an emotion engine evaluates the user's emotional state.

[0588] Output: The generated solutions.

[0589] Step 4:

[0590] Device: Notify the user of the generated solution.

[0591] Input: The generative AI model and the response output from the emotion engine.

[0592] Action: Notify the user on what to do.

[0593] Output: The action that was notified to the user.

[0594] Step 5:

[0595] Server: Stores side effect information in a database and periodically provides feedback to relevant organizations.

[0596] Input: Side effect information and generated remedies.

[0597] What it does: Runs queries that are saved in a database and fed back to relevant authorities.

[0598] Output: Information stored in database and feedback report.

[0599] Remaining quantity management and automatic replenishment

[0600] Step 1:

[0601] User: Enter remaining amount of medication or supplements.

[0602] Input: Remaining amount of medication or supplements.

[0603] Operation: Enter the remaining amount in the input form.

[0604] Output: The remaining amount information entered.

[0605] Step 2:

[0606] Device: Sends remaining charge information to the server.

[0607] Input: The remaining amount information entered by the user.

[0608] Operation: Sends remaining amount information to the server.

[0609] Output: The remaining information sent.

[0610] Step 3:

[0611] Server: Stores remaining quantity information in a database and generates a replenishment notification when the quantity falls below a set threshold.

[0612] Input: Remaining information.

[0613] What it does: It stores it in a database and runs an algorithm that monitors the remaining amount.

[0614] Output: Replenishment notification.

[0615] Step 4:

[0616] Terminal: Display a restock notification to the user.

[0617] Input: The generated replenishment notice.

[0618] Behavior: Displays a refill notification in the user interface.

[0619] Output: Refill notification displayed to the user.

[0620] Step 5:

[0621] User: Approves replenishment.

[0622] Enter: Refill Notice.

[0623] Action: Enter an acknowledgement for the replenishment notice.

[0624] Output: Replenishment approved.

[0625] Step 6:

[0626] Server: After receiving approval, the order is automatically placed with the online store.

[0627] Input: User's replenishment authorization.

[0628] Operation: Calls the order API to the online store.

[0629] Output: Order completion notification.

[0630] How it works: The user enters the remaining amount of medicine or supplements, and the information is sent to the server. The server stores the information in a database, and if the amount falls below a set threshold, it generates a replenishment notification and displays it to the user. If the user approves the replenishment, the server automatically completes the order with the online store. In addition, an emotion engine evaluates the user's emotions and generates appropriate feedback messages.

[0631] (Application example 2)

[0632] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0633] The present invention focuses on the need for medication management that goes beyond simple schedule management to provide advice tailored to the user's emotional state, manage side effects, and automatically replenish medications in a unified manner. Conventional systems do not take the user's emotional state into account, making it difficult to provide appropriate support and making it difficult for users to continue taking their medication. This can result in medications not being as effective as they could be, potentially hindering the user's health management.

[0634] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for evaluating drug interactions using a generation AI, means for proposing a drug administration schedule based on the user's lifestyle, means for the user to input side effect information, means for storing the side effect information in a database and periodically providing feedback to relevant institutions, means for managing the user's remaining medication and automatically suggesting replenishment, and means for responding to the user's emotional state using an emotion engine. This allows the user to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in accordance with their emotional state.

[0635] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate new information and answers.

[0636] The "means for evaluating drug interactions" is a function that evaluates the interactions when multiple drugs are taken simultaneously and suggests appropriate dosage methods.

[0637] The "means for proposing a medication schedule based on the user's lifestyle" is a function that takes into account the user's daily rhythm and suggests the optimal timing for taking medication.

[0638] "Means for users to input side effect information" is a function that allows users to input information about drug side effects they have experienced into the system.

[0639] "Means for storing side effect information in a database and providing regular feedback to relevant institutions" refers to a function that stores collected side effect information in a database and provides that information to medical institutions and relevant institutions on a regular basis.

[0640] "Means for managing the remaining amount of medication for the user and automatically suggesting refills" refers to a function that monitors the remaining amount of medication for the user and automatically suggests and arranges for refills as necessary.

[0641] "Means for responding to the user's emotional state using an emotion engine" is a function that analyzes the user's emotional state and provides appropriate advice and responses accordingly.

[0642] The following is a detailed description of the mode for carrying out this invention. This system uses a smartphone application to support users in managing their medications, and by combining a generative AI and an emotion engine, it manages drug interactions, dosage schedules, side effects, and remaining amounts, providing support according to the user's emotional state.

[0643] User registration and initial drug information settings

[0644] The server provides a user registration screen where the user enters personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. This information is stored in a database for later use by the generative AI model and emotion engine.

[0645] For example, when a user first launches the app, they answer questions about their name, age, medications they are taking (drug A, drug B), past side effects (skin rashes), and initial emotional state. This allows the app to provide the user with advice based on their optimal medication schedule and emotions.

[0646] Conversational Concierge

[0647] Users ask questions about drug interactions and side effects. The generative AI model analyzes the question, and the emotion engine evaluates the user's facial expressions and emotional state to generate an appropriate answer and display it to the user.

[0648] For example, if a user asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." If the emotion engine recognizes that the user looks worried, it will advise, "If you are concerned, we recommend that you consult a doctor."

[0649] Suggested dosing schedule

[0650] The server uses a generative AI and emotion engine to calculate and propose an optimal medication schedule to the user based on the user's lifestyle information. The user inputs their lifestyle information (e.g., breakfast, lunch, and dinner times).

[0651] For example, based on the user's daily rhythm, the system suggests a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." If the user looks busy, the emotion engine will make a flexible suggestion such as, "If these times are difficult, we can also shift the times of each dose by one hour."

[0652] Side effect reporting and feedback

[0653] If a user experiences a side effect, they open the side effect reporting screen and enter their symptoms. Based on this, the generative AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of appropriate measures. The server stores the reported side effect information in a database and periodically provides feedback to relevant institutions.

[0654] For example, if a user reports feeling nauseous after taking drug B, the generative AI will suggest a course of action such as "drink water and rest," and if the emotion engine recognizes the user's feelings of anxiety, it will send an additional message saying, "We will inform you of different ways to deal with the situation the next time you take the drug."

[0655] Remaining quantity management and automatic replenishment

[0656] When a user enters the remaining amount of medicine or supplement, the server stores the information in a database and automatically generates a notification for refilling when the amount falls below a set threshold. If the user approves the refill, an order is automatically placed with the online store.

[0657] For example, if a user inputs that they are running low on medication A, a notification will be sent saying, "Your remaining medication A is low. Would you like us to automatically place an order for you?" If the user agrees, the server will automatically complete the order. Also, if the emotion engine recognizes that the user is stressed, it will display a message saying, "The order has been completed. Please wait a moment. Thank you for your hard work."

[0658] Hardware and software used

[0659] Hardware: Smartphone (iOS or Android)

[0660] Software: Smartphone apps (e.g., MediGuard Pro), server-side platforms (cloud services such as AWS)

[0661] Generative AI models: GPT-4, etc.

[0662] Emotion engine: EmotionAPI, etc.

[0663] Examples of prompt statements

[0664] I have a question about the functionality of the medication management app "MediGuard Pro." Is it okay to take "Medicine A" and "Medicine B" at the same time?

[0665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0666] Step 1:

[0667] When a user launches the smartphone application for the first time, they enter their personal information (such as name, age, and gender), a list of medications they are taking, their side effect history, and their emotional state. This data is then sent from the device to the server, which then stores the entered information in a database.

[0668] input:

[0669] User personal and medication information

[0670] output:

[0671] Stored user information database

[0672] Specific behavior:

[0673] The user enters information into the application screen and presses the "Submit" button.

[0674] The server receives the data and stores it in a database

[0675] Step 2:

[0676] The user inputs a question. The device sends the user's question to the generative AI model. The generative AI model analyzes the question and generates an answer. This answer is then sent to the device via the server and finally displayed to the user. At the same time, the emotion engine analyzes the user's facial expressions and emotional state and provides appropriate additional advice.

[0677] input:

[0678] User Questions

[0679] output:

[0680] Answers from generative AI models

[0681] Additional advice from the emotion engine

[0682] Specific behavior:

[0683] The user enters a question into a text box within the application and presses the "Ask" button.

[0684] Generative AI model analyzes and generates answers

[0685] The emotion engine analyzes the user's facial expressions and generates advice

[0686] Answers and advice are displayed to the user

[0687] Step 3:

[0688] The user inputs information about their daily rhythm (e.g., breakfast, lunch, and dinner times). The device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule. This schedule is then displayed to the user via the device.

[0689] input:

[0690] User's lifestyle information

[0691] output:

[0692] Optimal dosing schedule

[0693] Specific behavior:

[0694] The user enters their lifestyle information and presses the "Submit" button.

[0695] The server receives the information and calculates the schedule using a generative AI model and emotion engine.

[0696] The calculated schedule is displayed to the user

[0697] Step 4:

[0698] If a user experiences a side effect, they open the side effect report screen and enter their symptoms. The device sends this information to the server, where the generative AI model analyzes it and generates a solution. The emotion engine also evaluates the user's emotional state and generates additional advice. This information is then communicated to the user via the device.

[0699] input:

[0700] User side effects information

[0701] output:

[0702] How to address this with generative AI models

[0703] Additional advice from the emotion engine

[0704] Specific behavior:

[0705] The user enters the side effect information and presses the "Submit" button.

[0706] The server receives the data, and the generative AI model analyzes it and generates a solution.

[0707] The emotion engine analyzes the user's facial expressions and generates advice

[0708] Users are notified of solutions and advice

[0709] Step 5:

[0710] The user enters the remaining amount of medicine or supplement. The device sends this information to the server, which stores it in a database. If the remaining amount falls below a set threshold, the server generates a refill notification. The notification is sent to the device and displayed to the user. If the user approves the refill, the server automatically places an order with the online store.

[0711] input:

[0712] User's remaining medication information

[0713] output:

[0714] Replenishment Notification and Ordering

[0715] Specific behavior:

[0716] The user enters the remaining amount information and presses the "Submit" button.

[0717] The server receives the information and stores it in a database

[0718] Generates a refill notification when the remaining amount falls below a threshold

[0719] When the user approves the replenishment, the server automatically places an order with the online store.

[0720] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0721] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0723] [Second embodiment]

[0724] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0725] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0726] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0727] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0728] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0729] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0730] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0731] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0732] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0733] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0734] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0735] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0736] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining amounts. The system operates by exchanging information between the server, terminals, and users.

[0737] User registration and initial drug information settings

[0738] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[0739] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[0740] Example: When Tanaka starts the app for the first time, she enters her name, age, medications she is taking (drug A, drug B), and any side effects she has had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Tanaka.

[0741] Conversational Concierge

[0742] User: Enter questions about drug interactions and side effects.

[0743] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[0744] Example: When Tanaka asks, "Can I take medicine A and medicine B at the same time?", the generated AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[0745] Suggested dosing schedule

[0746] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0747] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[0748] Example: Based on Tanaka's daily routine, the AI ​​generator will propose a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." and notify Tanaka's device of this.

[0749] Side effect reporting and feedback

[0750] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[0751] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[0752] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0753] Example: If Tanaka reports feeling nauseous after taking medicine B, the generated AI will advise him on how to deal with the situation, saying, "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[0754] Remaining quantity management and automatic replenishment

[0755] User: Enter the remaining amount of medication.

[0756] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[0757] Server: Places automatic orders to the online store as needed.

[0758] Example: When Tanaka inputs into the app that he is running low on medicine A, the server sends a notification saying, "You are running low on medicine A. Would you like to place an order automatically?" If Tanaka accepts, the server automatically completes the order with the online store.

[0759] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[0760] The processing flow will be explained below.

[0761] User registration and initial drug information settings

[0762] Step 1:

[0763] User: Launches the app for the first time.

[0764] Step 2:

[0765] Terminal: Display the user registration screen.

[0766] Step 3:

[0767] User: Enter personal information (name, age, gender, etc.), a list of medications being taken, and a history of side effects.

[0768] Step 4:

[0769] Terminal: Sends the entered information to the server.

[0770] Step 5:

[0771] Server: Stores the received personal information and medication information in a database.

[0772] Conversational Concierge

[0773] Step 1:

[0774] User: Enter questions about drug interactions and side effects.

[0775] Step 2:

[0776] Terminal: Sends the question to the generation AI.

[0777] Step 3:

[0778] Server: The generation AI analyzes the question and generates an appropriate answer.

[0779] Step 4:

[0780] Server: Sends the generated answer to the device.

[0781] Step 5:

[0782] Terminal: Display the answer to the user.

[0783] Suggested dosing schedule

[0784] Step 1:

[0785] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0786] Step 2:

[0787] Terminal: Sends the inputted life rhythm to the server.

[0788] Step 3:

[0789] Server: Based on lifestyle and medication information, the generation AI calculates the optimal dosing schedule.

[0790] Step 4:

[0791] Server: Sends the calculated medication schedule to the terminal.

[0792] Step 5:

[0793] Terminal: Notifies the user of their medication schedule.

[0794] Side effect reporting and feedback

[0795] Step 1:

[0796] User: If you experience any side effects, open the side effect report screen.

[0797] Step 2:

[0798] Device: Displays a screen for selecting the type of side effect and symptoms.

[0799] Step 3:

[0800] User: Enter specific symptoms and submit report.

[0801] Step 4:

[0802] Terminal: Sends the entered side effect information to the server.

[0803] Step 5:

[0804] Server: Stores the received side effect information in a database.

[0805] Step 6:

[0806] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[0807] Step 7:

[0808] Server: Sends the generated solution to the terminal.

[0809] Step 8:

[0810] Terminal: Show the user how to fix the problem.

[0811] Step 9:

[0812] Server: Regularly feeds back accumulated side effect information to relevant institutions.

[0813] Remaining quantity management and automatic replenishment

[0814] Step 1:

[0815] User: Enter remaining amount of medicine or supplement.

[0816] Step 2:

[0817] Terminal: Sends the entered remaining amount information to the server.

[0818] Step 3:

[0819] Server: Saves remaining information in a database.

[0820] Step 4:

[0821] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[0822] Step 5:

[0823] Server: Sends a notification message to the terminal.

[0824] Step 6:

[0825] Terminal: Display a restock notification to the user.

[0826] Step 7:

[0827] User: Approves the replenishment.

[0828] Step 8:

[0829] Terminal: Sends authorization information to the server.

[0830] Step 9:

[0831] Server: After receiving replenishment approval, automatically place an order to the online store.

[0832] Example 1

[0833] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0834] In modern self-medication, users are required to properly manage multiple medications and take them effectively. However, various tasks, such as drug interactions, appropriate dosing schedules, side effect management, and remaining medication amounts, are complex and place a significant burden on users. Furthermore, inadequate medication management, particularly for elderly people and those with chronic diseases, increases health risks. Another issue is the lack of reporting of side effects and appropriate feedback.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0836] In this invention, the server includes a means for evaluating drug interactions using a generative AI, a means for proposing a drug-taking schedule based on the user's lifestyle, and a means for the user to input side effect information. This allows the user to easily manage multiple drugs, take them on an appropriate schedule, and report side effects, thereby reducing health risks.

[0837] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[0838] A "means for evaluating drug interactions" is a system that analyzes the possibility of multiple drugs interacting with each other and displays the results.

[0839] The "means for suggesting a medication schedule" is a system that calculates and suggests appropriate medication times based on the user's lifestyle and medication characteristics.

[0840] The "means for inputting side effect information" is an interface that allows users to input symptoms of side effects experienced after taking a drug into an application or system.

[0841] A "database" is a system that systematically stores information and data and is constructed to make it easy to search and use.

[0842] A "feedback mechanism" is a system that analyzes collected information and sends the results to specific relevant organizations and users.

[0843] The "means for managing remaining amounts" is a system that monitors the remaining amount of medication the user owns and suggests refilling it if necessary.

[0844] "Means for automatic ordering" refers to a system that automatically places orders with online stores and pharmacies based on remaining medication levels and the need for replenishment.

[0845] The "means of generating appropriate answers" is a system in which a generative AI derives and presents the most appropriate answer to a question from a user.

[0846] The "means of generating and notifying appropriate countermeasures" is a system in which a generative AI generates optimal countermeasures based on side effect information reported by users and notifies the user.

[0847] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining medications. The system operates by exchanging information between the server, terminals, and users.

[0848] User registration and initial drug information settings

[0849] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[0850] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[0851] Example: Person A starts the app for the first time and enters their name, age, medications they are taking (drug A, drug B), and any side effects they have had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Person A.

[0852] Conversational Concierge

[0853] User: Enter questions about drug interactions and side effects.

[0854] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[0855] Example: If Person A asks, "Is it okay to take medicine A and medicine B at the same time?", the generated AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[0856] Suggested dosing schedule

[0857] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[0858] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[0859] Example: Based on Mr. A's daily routine, the generating AI proposes a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m.," and notifies Mr. A's device.

[0860] Side effect reporting and feedback

[0861] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[0862] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[0863] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0864] Example: If Person A reports feeling nauseous after taking Medicine B, the generated AI will advise the person on how to deal with the situation, such as "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[0865] Remaining quantity management and automatic replenishment

[0866] User: Enter the remaining amount of medication.

[0867] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[0868] Server: Places automatic orders to the online store as needed.

[0869] Example: When Person A enters into the app that he or she is running low on Medicine A, the server sends a notification saying, "Your remaining supply of Medicine A is low. Would you like to place an order automatically?" If Person A agrees, the server automatically completes the order with the online store.

[0870] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[0871] Example prompts for generative AI models

[0872] 1. "Can I take medication A and medication B at the same time?"

[0873] 2. "I have breakfast at 8:00, lunch at 12:00, and dinner at 19:00. Please tell me the medication schedule that fits this lifestyle."

[0874] 3. "I feel nauseous after taking medicine B. What should I do?"

[0875] The above is an embodiment of the present invention.

[0876] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0877] User registration and initial drug information settings

[0878] Step 1:

[0879] User: Launch the app and the user registration screen will appear.

[0880] What it does: When a user launches the app for the first time, a form to enter personal information is displayed.

[0881] Step 2:

[0882] User: Enter personal information, a list of medications being taken, and a history of side effects.

[0883] Input: Name, age, gender, medications being taken, side effect history.

[0884] Specific behavior: The user fills in each field.

[0885] Step 3:

[0886] Terminal: Sends the entered information to the server.

[0887] Input: Personal and medication information entered by the user.

[0888] Specific operation: The terminal sends the input data to the server.

[0889] Output: Data packets sent to the server.

[0890] Step 4:

[0891] Server: Stores the received information in a database.

[0892] Input: Data packets sent from the terminal.

[0893] Specific operation: The server receives the data packet and stores it in a database.

[0894] Output: User information stored in the database.

[0895] Conversational Concierge

[0896] Step 1:

[0897] User: Enter questions about drug interactions and side effects.

[0898] Input: The question from the user.

[0899] What happens: The user types a question into an input field in the app.

[0900] Step 2:

[0901] Terminal: The generation AI analyzes the input question and generates an appropriate answer.

[0902] Input: The question from the user.

[0903] Specific operation: The generative AI performs natural language processing and analyzes the question content.

[0904] Output: Correct answer.

[0905] Step 3:

[0906] Terminal: Displays the generated answer to the user.

[0907] Input: Answer data from the generation AI.

[0908] What it does: Formats the response data for display to the user.

[0909] Output: The answer displayed on the user's terminal.

[0910] Suggested dosing schedule

[0911] Step 1:

[0912] User: Enter your daily routine.

[0913] Input: User's daily rhythm information (e.g., breakfast, lunch, and dinner times).

[0914] Specific operation: The user inputs information about their daily rhythm.

[0915] Step 2:

[0916] Device: Based on the inputted information on daily rhythms, the AI ​​generates the optimal medication schedule.

[0917] Input: User's lifestyle information.

[0918] Specific operation: The generating AI performs calculations based on the given life rhythm.

[0919] Output: Optimal dosing schedule.

[0920] Step 3:

[0921] Terminal: Notifies the user of the calculated medication schedule.

[0922] Input: Calculated dosing schedule.

[0923] Specific operation: Notification information is sent to the user's device and displayed.

[0924] Output: Medication schedule notification displayed on the user's device.

[0925] Side effect reporting and feedback

[0926] Step 1:

[0927] User: Open the side effect report screen and enter the symptoms.

[0928] Input: The side effect symptoms experienced by the user.

[0929] Specific action: The user enters their symptoms into the input field.

[0930] Step 2:

[0931] Terminal: Sends side effect information to the server.

[0932] Input: The side effect information entered.

[0933] Specific operation: The device sends side effect information to the server.

[0934] Output: Data packets sent to the server.

[0935] Step 3:

[0936] Server: Analyzes the input information and generates appropriate countermeasures.

[0937] Input: Side effect information.

[0938] Specific operation: The generative AI analyzes side effect information and considers appropriate countermeasures.

[0939] Output: Appropriate action.

[0940] Step 4:

[0941] Terminal: Notify the user of the generated solution.

[0942] Input: The generated solution.

[0943] Specific action: Format and send to inform the user of the corrective action.

[0944] Output: Actions displayed on the user's terminal.

[0945] Step 5:

[0946] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[0947] Input: Side effect information.

[0948] Specific operation: The server stores side effect information and periodically creates and sends reports to relevant institutions.

[0949] Output: Side effect information stored in the database and feedback reports.

[0950] Remaining quantity management and automatic replenishment

[0951] Step 1:

[0952] User: Enter the remaining amount of medication.

[0953] Input: remaining amount of medication.

[0954] Specific behavior: The user enters the remaining amount of medication into the input field.

[0955] Step 2:

[0956] Terminal: Sends the entered remaining amount information to the server.

[0957] Input: Remaining medication information.

[0958] Specific operation: The device sends remaining battery information to the server.

[0959] Output: Data packets sent to the server.

[0960] Step 3:

[0961] Server: If the remaining amount falls below a threshold, an automatic replenishment suggestion is made.

[0962] Input: Remaining information.

[0963] What happens: The server checks the remaining amount and generates replenishment suggestions as needed.

[0964] Output: Automatic replenishment suggestion notification.

[0965] Step 4:

[0966] Terminal: If the user accepts automatic ordering, places an order with the online store.

[0967] Enter: Automatic Purchase Order Acceptance.

[0968] Specific action: The user presses the accept button.

[0969] Output: An order request to the online store.

[0970] Step 5:

[0971] Server: Completes the order with the online store.

[0972] Input: Order information accepted by the user.

[0973] Specific operation: The server sends an order request to the online store.

[0974] Output: Order data sent to the online store.

[0975] (Application example 1)

[0976] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0977] In conventional drug management systems, managing drug interactions and side effects is done manually, which is not only inefficient but also increases the risk of misuse. Furthermore, managing security devices requires checking their status and configuring them individually, which is extremely time-consuming. To solve these problems, there is a growing need for a centralized management system that utilizes generative AI.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0979] In this invention, the server includes means for evaluating drug interactions using a generating AI, means for proposing a dosing schedule based on the user's lifestyle, means for the user to input side effect information, means for evaluating the status of security devices and proposing optimal settings and operation schedules, and means for analyzing security-related problem reports and generating and notifying appropriate countermeasures. This enables drug management and security device management to be performed in a unified manner, enabling efficient and safe operation.

[0980] "Generative AI" uses artificial intelligence techniques such as natural language processing to generate appropriate answers to questions from users.

[0981] The "means for evaluating drug interactions" is a function that analyzes the effects of taking multiple drugs simultaneously and instructs the user on the appropriate method of taking them.

[0982] The "means for suggesting a medication schedule" is a function that calculates the optimal timing for taking medication based on the user's lifestyle and notifies the user.

[0983] "Means for inputting side effect information" refers to a data input interface that allows a user to report side effects of a drug.

[0984] The "means for storing in a database" refers to a system that safely stores the entered side effect information and manages it in a manner that allows it to be retrieved as needed.

[0985] "Means of providing feedback to relevant organizations" refers to a function that periodically sends accumulated side effect information to relevant organizations such as medical institutions.

[0986] "Means for managing remaining medication and automatically suggesting replenishment" refers to a function that monitors the remaining amount of medication possessed by the user and automatically suggests replenishment when it falls below a certain threshold.

[0987] "A means for evaluating the status of security equipment and proposing optimal settings and operation schedules" is a means for analyzing the operating status of security equipment such as surveillance cameras and sensors, and proposing optimal settings and schedules to users.

[0988] "Means for analyzing security-related problem reports and generating and notifying appropriate countermeasures" refers to a system that analyzes security device problems reported by users, generates appropriate countermeasures, and notifies the users.

[0989] The "means for managing automatic replenishment" is a function for monitoring the maintenance status of security equipment and the remaining amount of consumables, and for timely replenishment or replacement.

[0990] The system for implementing this invention uses generative AI to manage drug interactions, drug schedules, side effects, monitor the status of security devices, and automatically replenish medications. The system operates by exchanging information between a server, terminals, and users.

[0991] User registration and initial settings

[0992] User: When the application is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, side effect history, and security device information.

[0993] Terminal: Sends the entered information to the server. The user's personal information, medication information, and security device information are stored in the server's database.

[0994] Conversational Concierge

[0995] User: Enter questions about drug interactions and security device settings.

[0996] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[0997] For example, in response to the question, "My surveillance camera isn't recording. What should I do?", the AI ​​generator will answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[0998] Schedule suggestions

[0999] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times) and security device usage schedule.

[1000] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and security device operation schedule and notifies the user.

[1001] Server: Stores schedule information in a database and sends reminders to users when necessary.

[1002] For example, we suggest a schedule such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[1003] Reporting and Feedback on Side Effects and Problems

[1004] Users: If they experience a side effect from a medication or a problem with a security device, they can open a dedicated reporting screen and enter details of the symptoms or problem.

[1005] Device: Sends reported information to the server. The generated AI analyzes the input information and notifies the user of the appropriate countermeasures.

[1006] Server: The reported information is stored in a database and periodically provided to relevant authorities and security companies.

[1007] For example, if you report that "the sensor at the front door is not responding," the generating AI will notify you of the corrective action, such as "check the sensor battery and replace it if necessary," and this information will be regularly fed back to the relevant authorities.

[1008] Remaining quantity management and automatic replenishment

[1009] User: Enters remaining amounts and status of medications and security devices.

[1010] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[1011] Server: Automatically orders consumables and replacement parts as needed.

[1012] Example: "The sensor battery is low. Would you like to order a new battery automatically?" If the user agrees, the server will automatically complete the order with the online store.

[1013] Hardware and Software Used

[1014] Hardware: Smartphones, internet connections, security equipment (surveillance cameras, sensors, etc.)

[1015] software:

[1016] Programming language: Python

[1017] API: REST API for exchanging data related to the security system

[1018] Server: Security database on the cloud (e.g. Amazon AWS, Google Cloud)

[1019] Generative AI models: Natural Language Processing (NLP) models for generating answers to user questions (e.g., OpenAI GPT-4)

[1020] Add specific examples

[1021] Example prompts to input to the generative AI model

[1022] Question: "My security cameras aren't recording, what should I do?"

[1023] Prompt: "What should I do if my security camera isn't recording? Please list possible causes and solutions that apply to your answer."

[1024] My security camera isn't recording, what should I do?

[1025] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1026] Step 1:

[1027] (User registration and initial settings)

[1028] Input: The user enters personal information, medications, and security device information into the app.

[1029] Operation: The terminal sends the entered information to the server, which receives the information and stores it in a database.

[1030] Output: The user's personal information, medication information, and security device information are saved in the database.

[1031] Step 2:

[1032] (Interactive concierge function)

[1033] Input: The user inputs questions about drug interactions and security device settings.

[1034] How it works: The device sends a question to the generative AI model, which analyzes the question and generates an appropriate answer.

[1035] Output: The answer from the generative AI model is sent to the device and displayed to the user.

[1036] Specific operation: In response to the question, "My surveillance camera is not recording. What should I do?", the generative AI model generates the answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[1037] Step 3:

[1038] (Schedule proposal)

[1039] Input: The user inputs their daily routine and schedule for using security devices.

[1040] Operation: The device sends the input information to the server, which uses the generative AI model to calculate the optimal medication schedule and security device operation schedule.

[1041] Output: The generated schedule is notified to the user.

[1042] Specific operation: Based on the user's daily rhythm, the generating AI will make suggestions such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[1043] Step 4:

[1044] (Reporting and Feedback on Side Effects and Problems)

[1045] Input: To report side effects or problems with security devices, users enter details of symptoms or problems into a dedicated reporting screen.

[1046] Operation: The device sends the report information to the server, which uses the generative AI model to analyze the report information and generate appropriate countermeasures.

[1047] Output: The generated solutions are notified to the user. The reported information is also saved in a database and periodically fed back to the relevant organizations.

[1048] Specific operation: When you report that "the front door sensor is not responding," the generating AI will generate and notify you of the solution, such as "check the sensor battery and replace it if necessary."

[1049] Step 5:

[1050] (Remaining amount management and automatic replenishment)

[1051] Input: The user inputs the remaining amount and status of medicines and security devices.

[1052] Operation: The device sends the input information to the server. The server references the database and suggests automatic replenishment if the remaining amount falls below a threshold. If necessary, it automatically places an order with the online store.

[1053] Output: The user is notified of the replenishment proposal, and after user approval, automatic ordering is completed.

[1054] Specific operation: The user is notified, "The sensor battery is low. Would you like to automatically order a new battery?" If the user agrees, the server will automatically complete the order.

[1055] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1056] The following is a detailed description of the mode for carrying out this invention. This system uses generative AI to manage drug interactions, dosage schedules, and side effects, and automatically replenish remaining amounts. In addition, by combining it with an emotion engine, it can also respond to the user's emotional state.

[1057] User registration and initial drug information settings

[1058] User: When the app is first launched, the user is presented with a registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, any side effects they have experienced, and their emotional state.

[1059] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[1060] Example: When Tanaka starts the app for the first time, she answers questions about her name, age, medications she is taking (drug A, drug B), past side effects (skin rash), and initial emotional state. This allows the app to suggest the optimal medication schedule and emotional advice for Tanaka.

[1061] Conversational Concierge

[1062] User: Enter questions about drug interactions and side effects.

[1063] Terminal: Sends the question content to the generation AI and emotion engine.

[1064] Server: The generation AI analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The answer is then displayed to the user.

[1065] Example: When Tanaka asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." The emotion engine recognizes Tanaka's worried expression and advises, "If you are worried, we recommend that you consult a doctor."

[1066] Suggested dosing schedule

[1067] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1068] Terminal: Sends the inputted life rhythm to the server.

[1069] Server: Based on lifestyle and medication information, the generative AI and emotion engine calculate the optimal medication schedule.

[1070] Example: Based on Tanaka's daily rhythm, a schedule is suggested such as "Take medicine A at 8:00 AM and medicine B at 8:30 AM." If Tanaka looks busy, the emotion engine will make a flexible suggestion such as "If these times are difficult, it is also possible to shift the times of each dose by one hour."

[1071] Side effect reporting and feedback

[1072] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[1073] Terminal: Sends side effect information to the server. The generation AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of the appropriate course of action.

[1074] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[1075] Example: If Tanaka reports feeling nauseous after taking drug B, the generative AI will advise him to "drink water and rest," and the emotion engine will recognize Tanaka's anxious feelings and send an additional message saying, "We will inform you of different measures the next time you take the drug."

[1076] Remaining quantity management and automatic replenishment

[1077] User: Enter remaining amount of medicine or supplement.

[1078] Terminal: Sends the entered remaining amount information to the server.

[1079] Server: Saves remaining information in a database. The emotion engine also references this information.

[1080] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[1081] Terminal: Display a restock notification to the user.

[1082] User: Approves replenishment.

[1083] Server: After receiving approval, the order is automatically placed with the online store.

[1084] Example: When Tanaka inputs that he is running low on medicine A, a notification is sent saying, "Your remaining medicine A is low. Would you like us to automatically place an order for you?" Tanaka accepts, and the server automatically completes the order. Furthermore, if the emotion engine recognizes that Tanaka is stressed, it displays the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[1085] This system allows users to manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in a unified manner, according to their emotional state, allowing them to maximize the effectiveness of their medications.

[1086] The processing flow will be explained below.

[1087] User registration and initial drug information settings

[1088] Step 1:

[1089] User: Launches the app for the first time.

[1090] Step 2:

[1091] Terminal: Display the user registration screen.

[1092] Step 3:

[1093] User: Answers initial setup questions to identify personal information (such as name, age, and gender), a list of medications taken, side effects, and emotional state.

[1094] Step 4:

[1095] Terminal: Sends the entered information to the server.

[1096] Step 5:

[1097] Server: Stores the received personal information and medication information in a database.

[1098] Conversational Concierge

[1099] Step 1:

[1100] User: Enter questions about drug interactions and side effects.

[1101] Step 2:

[1102] Terminal: Sends the question content to the generation AI and emotion engine.

[1103] Step 3:

[1104] Server: The generation AI analyzes the question content, and the emotion engine evaluates the user's emotional state.

[1105] Step 4:

[1106] Server: The generation AI generates an appropriate answer based on the analysis results.

[1107] Step 5:

[1108] Server: The emotion engine generates supplementary advice according to the emotional state.

[1109] Step 6:

[1110] Server: Sends the generated answers and supplementary advice to the terminal.

[1111] Step 7:

[1112] Terminal: The answer and supplementary advice are displayed to the user.

[1113] Suggested dosing schedule

[1114] Step 1:

[1115] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1116] Step 2:

[1117] Terminal: Sends the inputted life rhythm to the server.

[1118] Step 3:

[1119] Server: Generative AI calculates the optimal dosing schedule based on lifestyle rhythm information and medication information.

[1120] Step 4:

[1121] Server: The emotion engine evaluates the user's emotional state and checks whether the schedule places a burden on the user.

[1122] Step 5:

[1123] Server: Sends the calculated dosing schedule to the terminal.

[1124] Step 6:

[1125] Terminal: Notifies the user of their medication schedule.

[1126] Side effect reporting and feedback

[1127] Step 1:

[1128] User: If you experience any side effects, open the side effect report screen.

[1129] Step 2:

[1130] Device: Displays a screen for selecting the type of side effect and symptoms.

[1131] Step 3:

[1132] User: Enter specific symptoms and submit report.

[1133] Step 4:

[1134] Terminal: Sends the entered side effect information to the server.

[1135] Step 5:

[1136] Server: Stores the received side effect information in a database.

[1137] Step 6:

[1138] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[1139] Step 7:

[1140] Server: The emotion engine evaluates the user's emotional state and adds additional information to the response.

[1141] Step 8:

[1142] Server: Sends the generated solution and additional information to the terminal.

[1143] Step 9:

[1144] Terminal: Display solutions and additional information to the user.

[1145] Step 10:

[1146] Server: Stores accumulated side effect information in a database and periodically provides feedback to relevant institutions.

[1147] Remaining quantity management and automatic replenishment

[1148] Step 1:

[1149] User: Enter remaining amount of medicine or supplement.

[1150] Step 2:

[1151] Terminal: Sends the entered remaining amount information to the server.

[1152] Step 3:

[1153] Server: Saves remaining information in a database.

[1154] Step 4:

[1155] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[1156] Step 5:

[1157] Server: The emotion engine evaluates the user's emotional state and predicts their reaction to the notification.

[1158] Step 6:

[1159] Server: Sends a notification message to the terminal.

[1160] Step 7:

[1161] Terminal: Display a restock notification to the user.

[1162] Step 8:

[1163] User: Approves replenishment.

[1164] Step 9:

[1165] Terminal: Sends authorization information to the server.

[1166] Step 10:

[1167] Server: After receiving approval for replenishment, the server automatically places an order with the online store.

[1168] In this way, a system is realized that performs integrated medication management while also taking into account the user's emotional state.

[1169] Example 2

[1170] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1171] Conventional medication management systems make it difficult for users to efficiently manage multiple medications, requiring a great deal of effort to manage drug interactions, dosage schedules, side effects, and remaining medication amounts. Furthermore, the system does not respond to the user's emotional state, which can increase anxiety and stress. To solve this problem, a comprehensive medication management system that combines a generative AI model and an emotion engine is needed.

[1172] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for transmitting personal information and drug information entered by the user to the server and storing them in a database; a means for transmitting the user's question to the generation AI model and emotion engine, analyzing the question, evaluating the user's emotional state, and generating an appropriate answer; a means for calculating an optimal drug schedule based on the user's lifestyle information; a means for the user to input side effect information; a means for analyzing the side effect information, evaluating the user's emotional state, and notifying the user of appropriate measures; a means for storing the side effect information in a database and periodically providing feedback to relevant institutions; and a means for managing the user's remaining medication, automatically suggesting replenishment, and placing an order. This allows the user to efficiently manage multiple medications and quickly take appropriate measures. Furthermore, the support provided by the emotion engine minimizes the user's anxiety and stress.

[1173] 1. "Server" refers to a computer system that manages, stores, and processes data over a network.

[1174] 2. "Database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[1175] 3. "Generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to generate appropriate outputs (e.g., answers, suggestions, countermeasures, etc.) from input information.

[1176] 4. "Emotion engine" refers to a computer program that analyzes a user's emotional state based on input, facial expressions, voice, etc., and determines an appropriate response.

[1177] 5. "User" refers to an individual who uses this system.

[1178] 6. "Personal information" refers to information that can identify a specific individual, such as name, age, and gender.

[1179] 7. "Medication information" refers to detailed information about the medications a user is taking, such as the name, dosage, and time of administration.

[1180] 8. "Side effect information" refers to information about undesirable symptoms or reactions that occur as a result of taking a drug.

[1181] 9. "Lifestyle information" refers to information such as meal and activity times in a user's daily life.

[1182] 10. "Dosing schedule" refers to the specific time and order in which a user takes medication.

[1183] 11. "Balance management" refers to the process of monitoring the current level of medications or supplements taken by a user and replenishing them appropriately.

[1184] 12. "Relevant organizations" refers to organizations involved in the management of drugs and the monitoring of side effects, such as medical institutions, pharmacies, and insurance companies.

[1185] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI model and an emotion engine to manage drug interactions, dosage schedules, and side effects, automatically replenish remaining doses, and also responds to the user's emotional state. Hardware used includes a server, cloud storage, and user devices (smartphones, tablets, etc.). Software includes a generative AI, emotion engine, database management system, etc.

[1186] User registration and initial drug information settings

[1187] When a user launches the app, they are presented with a user registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. The device then sends this information to the server, which stores it in a database.

[1188] Examples:

[1189] When a user launches the app for the first time, they answer some initial questions about their name, age, medications they are taking (drug X, drug Y), past side effects (rashes), and emotional state. The device sends this information to the server, which stores it in a database.

[1190] Conversational Concierge

[1191] When a user inputs a question about drug interactions or side effects, the device sends the question to the generation AI and emotion engine. The server then uses the generation AI to analyze the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The device then displays the generated answer to the user.

[1192] Examples:

[1193] When a user asks, "Is it okay to take medication X and medication Y at the same time?", the generative AI responds, "Taking medication X and medication Y at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two," and the emotion engine recognizes the user's anxious expression and advises, "If you are worried, we recommend consulting a doctor." This is displayed to the user on the device.

[1194] Suggested dosing schedule

[1195] When a user inputs their daily rhythm (e.g., breakfast, lunch, and dinner times), the device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule and presents it to the user via the device.

[1196] Examples:

[1197] When a user inputs their daily routine, the system suggests a schedule, such as "Take medication X at 8:00 a.m. and medication Y at 8:30 a.m." If the user looks busy, the emotion engine suggests, "If these times are difficult, you can shift the times of each dose by one hour."

[1198] Side effect reporting and feedback

[1199] When a user experiences a side effect, they enter their symptoms on the side effect reporting screen, and the device sends the information to the server. The server then uses generative AI and an emotion engine to analyze the side effect information, generate appropriate countermeasures, and store them in a database. The appropriate countermeasures are notified to the user and periodically provided as feedback to relevant organizations.

[1200] Examples:

[1201] If a user reports feeling nauseous after taking drug Y, the generation AI will advise them to "drink water and rest," and the emotion engine will recognize the user's anxious feelings and generate an additional message saying, "We will inform you of different measures the next time you take the drug." The device will notify the user of this.

[1202] Remaining quantity management and automatic replenishment

[1203] When a user inputs the remaining amount of medicine or supplement, the device sends the remaining amount information to the server. The server stores the remaining amount information in a database, and when the remaining amount falls below a set threshold, it automatically generates a notification to replenish and displays the notification on the device. If the user approves the replenishment, the server automatically places an order with the online store.

[1204] Examples:

[1205] When the user inputs that they are low on medicine X, a notification is sent saying, "Your remaining medicine X is low. Would you like us to automatically place an order for you?" If the user agrees, the server will automatically complete the order. Also, if the emotion engine recognizes that the user is stressed, it will display the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[1206] Example prompts for generative AI models

[1207] Example prompt 1: Suggested medication schedule

[1208] "Please suggest the best dosing schedule based on the user's daily rhythm of taking medication X at 8:00 AM and medication Y at 8:30 AM."

[1209] Prompt example 2: Conversational concierge function

[1210] "User is asking if it's okay to take medication X and medication Y at the same time. Please provide some advice on how to do this safely. User seems a little nervous."

[1211] This system allows users to efficiently manage multiple medications and take appropriate measures quickly, while its emotional engine supports users in minimizing anxiety and stress.

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Program processing flow

[1214] User registration and initial drug information settings

[1215] Step 1:

[1216] User: Launch the app and open the user registration screen.

[1217] Input: Name, age, gender, list of medications you are taking, side effect history, and default questions about your emotional state.

[1218] What happens: A user fills out a form.

[1219] Output: The entered user information.

[1220] Step 2:

[1221] Terminal: Sends the entered information to the server.

[1222] Input: Personal information and medication information entered by the user.

[1223] How it works: The device sends information over the network to the server.

[1224] Output: The user information sent.

[1225] Step 3:

[1226] Server: Stores the received user information in a database.

[1227] Input: User information sent from the device.

[1228] What it does: Executes a query that stores information in a database.

[1229] Output: User information stored in the database.

[1230] Conversational Concierge

[1231] Step 1:

[1232] User: Enter questions about drug interactions and side effects.

[1233] Input: The user's question.

[1234] What it does: Enter a question into the form.

[1235] Output: The question entered.

[1236] Step 2:

[1237] Terminal: Sends the question content to the generative AI model and emotion engine.

[1238] Input: The user's question.

[1239] Operation: Sends the query to the server.

[1240] Output: The question submitted.

[1241] Step 3:

[1242] Server: The generative AI model analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer.

[1243] Input: The user's question.

[1244] How it works: A generative AI model analyzes the question, and an emotion engine assesses the user's emotional state.

[1245] Output: The generated answer.

[1246] Step 4:

[1247] Terminal: Displays the generated answer to the user.

[1248] Input: The answer output from the generative AI model and the emotion engine.

[1249] Behavior: Displays the answer in the user interface.

[1250] Output: The answer shown to the user.

[1251] Suggested dosing schedule

[1252] Step 1:

[1253] User: Enter lifestyle information (breakfast, lunch, dinner times, etc.).

[1254] Input: User's lifestyle information.

[1255] Action: Enter your lifestyle information in the input form.

[1256] Output: Inputted life rhythm information.

[1257] Step 2:

[1258] Terminal: Sends the inputted life rhythm information to the server.

[1259] Input: User's lifestyle information.

[1260] Operation: Sends life rhythm information to the server.

[1261] Output: Transmitted life rhythm information.

[1262] Step 3:

[1263] Server: A generative AI model and emotion engine calculate the optimal medication schedule based on lifestyle information.

[1264] Input: User's lifestyle information.

[1265] How it works: The generative AI model analyzes daily rhythm information, and the emotion engine evaluates the patient's emotional state to create an optimal medication schedule.

[1266] Output: Optimal dosing schedule.

[1267] Step 4:

[1268] Terminal: Displays the generated dosing schedule to the user.

[1269] Input: The dose schedule output from the generative AI model and the emotion engine.

[1270] What it does: Displays the medication schedule in the user interface.

[1271] Output: The dosing schedule displayed to the user.

[1272] Side effect reporting and feedback

[1273] Step 1:

[1274] User: Open the side effect report screen and enter the symptoms.

[1275] Input: Side effect symptoms.

[1276] Action: Enter your symptoms in the form.

[1277] Output: The entered side effect information.

[1278] Step 2:

[1279] Terminal: Sends side effect information to the server.

[1280] Input: Side effect symptoms.

[1281] Action: Sends side effect information to the server.

[1282] Output: The side effect information sent.

[1283] Step 3:

[1284] Server: The generative AI model analyzes side effect information, and the emotion engine evaluates the emotional state and generates appropriate countermeasures.

[1285] Input: Side effect symptoms.

[1286] How it works: A generative AI model analyzes side effect information, and an emotion engine evaluates the user's emotional state.

[1287] Output: The generated solutions.

[1288] Step 4:

[1289] Device: Notify the user of the generated solution.

[1290] Input: The generative AI model and the response output from the emotion engine.

[1291] Action: Notify the user on what to do.

[1292] Output: The action that was notified to the user.

[1293] Step 5:

[1294] Server: Stores side effect information in a database and periodically provides feedback to relevant organizations.

[1295] Input: Side effect information and generated remedies.

[1296] What it does: Runs queries that are saved in a database and fed back to relevant authorities.

[1297] Output: Information stored in database and feedback report.

[1298] Remaining quantity management and automatic replenishment

[1299] Step 1:

[1300] User: Enter remaining amount of medication or supplements.

[1301] Input: Remaining amount of medication or supplements.

[1302] Operation: Enter the remaining amount in the input form.

[1303] Output: The remaining amount information entered.

[1304] Step 2:

[1305] Device: Sends remaining charge information to the server.

[1306] Input: The remaining amount information entered by the user.

[1307] Operation: Sends remaining amount information to the server.

[1308] Output: The remaining information sent.

[1309] Step 3:

[1310] Server: Stores remaining quantity information in a database and generates a replenishment notification when the quantity falls below a set threshold.

[1311] Input: Remaining information.

[1312] What it does: It stores it in a database and runs an algorithm that monitors the remaining amount.

[1313] Output: Replenishment notification.

[1314] Step 4:

[1315] Terminal: Display a restock notification to the user.

[1316] Input: The generated replenishment notice.

[1317] Behavior: Displays a refill notification in the user interface.

[1318] Output: Refill notification displayed to the user.

[1319] Step 5:

[1320] User: Approves replenishment.

[1321] Enter: Refill Notice.

[1322] Action: Enter an acknowledgement for the replenishment notice.

[1323] Output: Replenishment approved.

[1324] Step 6:

[1325] Server: After receiving approval, the order is automatically placed with the online store.

[1326] Input: User's replenishment authorization.

[1327] Operation: Calls the order API to the online store.

[1328] Output: Order completion notification.

[1329] How it works: The user enters the remaining amount of medicine or supplements, and the information is sent to the server. The server stores the information in a database, and if the amount falls below a set threshold, it generates a replenishment notification and displays it to the user. If the user approves the replenishment, the server automatically completes the order with the online store. In addition, an emotion engine evaluates the user's emotions and generates appropriate feedback messages.

[1330] (Application example 2)

[1331] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1332] The present invention focuses on the need for medication management that goes beyond simple schedule management to provide advice tailored to the user's emotional state, manage side effects, and automatically replenish medications in a unified manner. Conventional systems do not take the user's emotional state into account, making it difficult to provide appropriate support and making it difficult for users to continue taking their medication. This can result in medications not being as effective as they could be, potentially hindering the user's health management.

[1333] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for evaluating drug interactions using a generation AI, means for proposing a drug administration schedule based on the user's lifestyle, means for the user to input side effect information, means for storing the side effect information in a database and periodically providing feedback to relevant institutions, means for managing the user's remaining medication and automatically suggesting replenishment, and means for responding to the user's emotional state using an emotion engine. This allows the user to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in accordance with their emotional state.

[1334] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate new information and answers.

[1335] The "means for evaluating drug interactions" is a function that evaluates the interactions when multiple drugs are taken simultaneously and suggests appropriate dosage methods.

[1336] The "means for proposing a medication schedule based on the user's lifestyle" is a function that takes into account the user's daily rhythm and suggests the optimal timing for taking medication.

[1337] "Means for users to input side effect information" is a function that allows users to input information about drug side effects they have experienced into the system.

[1338] "Means for storing side effect information in a database and providing regular feedback to relevant institutions" refers to a function that stores collected side effect information in a database and provides that information to medical institutions and relevant institutions on a regular basis.

[1339] "Means for managing the remaining amount of medication for the user and automatically suggesting refills" refers to a function that monitors the remaining amount of medication for the user and automatically suggests and arranges for refills as necessary.

[1340] "Means for responding to the user's emotional state using an emotion engine" is a function that analyzes the user's emotional state and provides appropriate advice and responses accordingly.

[1341] The following is a detailed description of the mode for carrying out this invention. This system uses a smartphone application to support users in managing their medications, and by combining a generative AI and an emotion engine, it manages drug interactions, dosage schedules, side effects, and remaining amounts, providing support according to the user's emotional state.

[1342] User registration and initial drug information settings

[1343] The server provides a user registration screen where the user enters personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. This information is stored in a database for later use by the generative AI model and emotion engine.

[1344] For example, when a user first launches the app, they answer questions about their name, age, medications they are taking (drug A, drug B), past side effects (skin rashes), and initial emotional state. This allows the app to provide the user with advice based on their optimal medication schedule and emotions.

[1345] Conversational Concierge

[1346] Users ask questions about drug interactions and side effects. The generative AI model analyzes the question, and the emotion engine evaluates the user's facial expressions and emotional state to generate an appropriate answer and display it to the user.

[1347] For example, if a user asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." If the emotion engine recognizes that the user looks worried, it will advise, "If you are concerned, we recommend that you consult a doctor."

[1348] Suggested dosing schedule

[1349] The server uses a generative AI and emotion engine to calculate and propose an optimal medication schedule to the user based on the user's lifestyle information. The user inputs their lifestyle information (e.g., breakfast, lunch, and dinner times).

[1350] For example, based on the user's daily rhythm, the system suggests a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." If the user looks busy, the emotion engine will make a flexible suggestion such as, "If these times are difficult, we can also shift the times of each dose by one hour."

[1351] Side effect reporting and feedback

[1352] If a user experiences a side effect, they open the side effect reporting screen and enter their symptoms. Based on this, the generative AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of appropriate measures. The server stores the reported side effect information in a database and periodically provides feedback to relevant institutions.

[1353] For example, if a user reports feeling nauseous after taking drug B, the generative AI will suggest a course of action such as "drink water and rest," and if the emotion engine recognizes the user's feelings of anxiety, it will send an additional message saying, "We will inform you of different ways to deal with the situation the next time you take the drug."

[1354] Remaining quantity management and automatic replenishment

[1355] When a user enters the remaining amount of medicine or supplement, the server stores the information in a database and automatically generates a notification for refilling when the amount falls below a set threshold. If the user approves the refill, an order is automatically placed with the online store.

[1356] For example, if a user inputs that they are running low on medication A, a notification will be sent saying, "Your remaining medication A is low. Would you like us to automatically place an order for you?" If the user agrees, the server will automatically complete the order. Also, if the emotion engine recognizes that the user is stressed, it will display a message saying, "The order has been completed. Please wait a moment. Thank you for your hard work."

[1357] Hardware and software used

[1358] Hardware: Smartphone (iOS or Android)

[1359] Software: Smartphone apps (e.g., MediGuard Pro), server-side platforms (cloud services such as AWS)

[1360] Generative AI models: GPT-4, etc.

[1361] Emotion engine: EmotionAPI, etc.

[1362] Examples of prompt statements

[1363] I have a question about the functionality of the medication management app "MediGuard Pro." Is it okay to take "Medicine A" and "Medicine B" at the same time?

[1364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1365] Step 1:

[1366] When a user launches the smartphone application for the first time, they enter their personal information (such as name, age, and gender), a list of medications they are taking, their side effect history, and their emotional state. This data is then sent from the device to the server, which then stores the entered information in a database.

[1367] input:

[1368] User personal and medication information

[1369] output:

[1370] Stored user information database

[1371] Specific behavior:

[1372] The user enters information into the application screen and presses the "Submit" button.

[1373] The server receives the data and stores it in a database

[1374] Step 2:

[1375] The user inputs a question. The device sends the user's question to the generative AI model. The generative AI model analyzes the question and generates an answer. This answer is then sent to the device via the server and finally displayed to the user. At the same time, the emotion engine analyzes the user's facial expressions and emotional state and provides appropriate additional advice.

[1376] input:

[1377] User Questions

[1378] output:

[1379] Answers from generative AI models

[1380] Additional advice from the emotion engine

[1381] Specific behavior:

[1382] The user enters a question into a text box within the application and presses the "Ask" button.

[1383] Generative AI model analyzes and generates answers

[1384] The emotion engine analyzes the user's facial expressions and generates advice

[1385] Answers and advice are displayed to the user

[1386] Step 3:

[1387] The user inputs information about their daily rhythm (e.g., breakfast, lunch, and dinner times). The device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule. This schedule is then displayed to the user via the device.

[1388] input:

[1389] User's lifestyle information

[1390] output:

[1391] Optimal dosing schedule

[1392] Specific behavior:

[1393] The user enters their lifestyle information and presses the "Submit" button.

[1394] The server receives the information and calculates the schedule using a generative AI model and emotion engine.

[1395] The calculated schedule is displayed to the user

[1396] Step 4:

[1397] If a user experiences a side effect, they open the side effect report screen and enter their symptoms. The device sends this information to the server, where the generative AI model analyzes it and generates a solution. The emotion engine also evaluates the user's emotional state and generates additional advice. This information is then communicated to the user via the device.

[1398] input:

[1399] User side effects information

[1400] output:

[1401] How to address this with generative AI models

[1402] Additional advice from the emotion engine

[1403] Specific behavior:

[1404] The user enters the side effect information and presses the "Submit" button.

[1405] The server receives the data, and the generative AI model analyzes it and generates a solution.

[1406] The emotion engine analyzes the user's facial expressions and generates advice

[1407] Users are notified of solutions and advice

[1408] Step 5:

[1409] The user enters the remaining amount of medicine or supplement. The device sends this information to the server, which stores it in a database. If the remaining amount falls below a set threshold, the server generates a refill notification. The notification is sent to the device and displayed to the user. If the user approves the refill, the server automatically places an order with the online store.

[1410] input:

[1411] User's remaining medication information

[1412] output:

[1413] Replenishment Notification and Ordering

[1414] Specific behavior:

[1415] The user enters the remaining amount information and presses the "Submit" button.

[1416] The server receives the information and stores it in a database

[1417] Generates a refill notification when the remaining amount falls below a threshold

[1418] When the user approves the replenishment, the server automatically places an order with the online store.

[1419] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1420] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1421] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1422] [Third embodiment]

[1423] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1424] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1425] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1426] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1427] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1428] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1429] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1430] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1431] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1432] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1433] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1434] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1435] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining amounts. The system operates by exchanging information between the server, terminals, and users.

[1436] User registration and initial drug information settings

[1437] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[1438] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[1439] Example: When Tanaka starts the app for the first time, she enters her name, age, medications she is taking (drug A, drug B), and any side effects she has had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Tanaka.

[1440] Conversational Concierge

[1441] User: Enter questions about drug interactions and side effects.

[1442] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[1443] Example: When Tanaka asks, "Can I take medicine A and medicine B at the same time?", the generated AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[1444] Suggested dosing schedule

[1445] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1446] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[1447] Example: Based on Tanaka's daily routine, the AI ​​generator will propose a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." and notify Tanaka's device of this.

[1448] Side effect reporting and feedback

[1449] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[1450] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[1451] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[1452] Example: If Tanaka reports feeling nauseous after taking medicine B, the generated AI will advise him on how to deal with the situation, saying, "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[1453] Remaining quantity management and automatic replenishment

[1454] User: Enter the remaining amount of medication.

[1455] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[1456] Server: Places automatic orders to the online store as needed.

[1457] Example: When Tanaka inputs into the app that he is running low on medicine A, the server sends a notification saying, "You are running low on medicine A. Would you like to place an order automatically?" If Tanaka accepts, the server automatically completes the order with the online store.

[1458] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[1459] The processing flow will be explained below.

[1460] User registration and initial drug information settings

[1461] Step 1:

[1462] User: Launches the app for the first time.

[1463] Step 2:

[1464] Terminal: Display the user registration screen.

[1465] Step 3:

[1466] User: Enter personal information (name, age, gender, etc.), a list of medications being taken, and a history of side effects.

[1467] Step 4:

[1468] Terminal: Sends the entered information to the server.

[1469] Step 5:

[1470] Server: Stores the received personal information and medication information in a database.

[1471] Conversational Concierge

[1472] Step 1:

[1473] User: Enter questions about drug interactions and side effects.

[1474] Step 2:

[1475] Terminal: Sends the question to the generation AI.

[1476] Step 3:

[1477] Server: The generation AI analyzes the question and generates an appropriate answer.

[1478] Step 4:

[1479] Server: Sends the generated answer to the device.

[1480] Step 5:

[1481] Terminal: Display the answer to the user.

[1482] Suggested dosing schedule

[1483] Step 1:

[1484] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1485] Step 2:

[1486] Terminal: Sends the inputted life rhythm to the server.

[1487] Step 3:

[1488] Server: Based on lifestyle and medication information, the generation AI calculates the optimal dosing schedule.

[1489] Step 4:

[1490] Server: Sends the calculated medication schedule to the terminal.

[1491] Step 5:

[1492] Terminal: Notifies the user of their medication schedule.

[1493] Side effect reporting and feedback

[1494] Step 1:

[1495] User: If you experience any side effects, open the side effect report screen.

[1496] Step 2:

[1497] Device: Displays a screen for selecting the type of side effect and symptoms.

[1498] Step 3:

[1499] User: Enter specific symptoms and submit report.

[1500] Step 4:

[1501] Terminal: Sends the entered side effect information to the server.

[1502] Step 5:

[1503] Server: Stores the received side effect information in a database.

[1504] Step 6:

[1505] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[1506] Step 7:

[1507] Server: Sends the generated solution to the terminal.

[1508] Step 8:

[1509] Terminal: Show the user how to fix the problem.

[1510] Step 9:

[1511] Server: Regularly feeds back accumulated side effect information to relevant institutions.

[1512] Remaining quantity management and automatic replenishment

[1513] Step 1:

[1514] User: Enter remaining amount of medicine or supplement.

[1515] Step 2:

[1516] Terminal: Sends the entered remaining amount information to the server.

[1517] Step 3:

[1518] Server: Saves remaining information in a database.

[1519] Step 4:

[1520] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[1521] Step 5:

[1522] Server: Sends a notification message to the terminal.

[1523] Step 6:

[1524] Terminal: Display a restock notification to the user.

[1525] Step 7:

[1526] User: Approves the replenishment.

[1527] Step 8:

[1528] Terminal: Sends authorization information to the server.

[1529] Step 9:

[1530] Server: After receiving replenishment approval, automatically place an order to the online store.

[1531] Example 1

[1532] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1533] In modern self-medication, users are required to properly manage multiple medications and take them effectively. However, various tasks, such as drug interactions, appropriate dosing schedules, side effect management, and remaining medication amounts, are complex and place a significant burden on users. Furthermore, inadequate medication management, particularly for elderly people and those with chronic diseases, increases health risks. Another issue is the lack of reporting of side effects and appropriate feedback.

[1534] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1535] In this invention, the server includes a means for evaluating drug interactions using a generative AI, a means for proposing a drug-taking schedule based on the user's lifestyle, and a means for the user to input side effect information. This allows the user to easily manage multiple drugs, take them on an appropriate schedule, and report side effects, thereby reducing health risks.

[1536] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[1537] A "means for evaluating drug interactions" is a system that analyzes the possibility of multiple drugs interacting with each other and displays the results.

[1538] The "means for suggesting a medication schedule" is a system that calculates and suggests appropriate medication times based on the user's lifestyle and medication characteristics.

[1539] The "means for inputting side effect information" is an interface that allows users to input symptoms of side effects experienced after taking a drug into an application or system.

[1540] A "database" is a system that systematically stores information and data and is constructed to make it easy to search and use.

[1541] A "feedback mechanism" is a system that analyzes collected information and sends the results to specific relevant organizations and users.

[1542] The "means for managing remaining amounts" is a system that monitors the remaining amount of medication the user owns and suggests refilling it if necessary.

[1543] "Means for automatic ordering" refers to a system that automatically places orders with online stores and pharmacies based on remaining medication levels and the need for replenishment.

[1544] The "means of generating appropriate answers" is a system in which a generative AI derives and presents the most appropriate answer to a question from a user.

[1545] The "means of generating and notifying appropriate countermeasures" is a system in which a generative AI generates optimal countermeasures based on side effect information reported by users and notifies the user.

[1546] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining medications. The system operates by exchanging information between the server, terminals, and users.

[1547] User registration and initial drug information settings

[1548] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[1549] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[1550] Example: Person A starts the app for the first time and enters their name, age, medications they are taking (drug A, drug B), and any side effects they have had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Person A.

[1551] Conversational Concierge

[1552] User: Enter questions about drug interactions and side effects.

[1553] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[1554] Example: If Person A asks, "Is it okay to take medicine A and medicine B at the same time?", the generated AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[1555] Suggested dosing schedule

[1556] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1557] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[1558] Example: Based on Mr. A's daily routine, the generating AI proposes a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m.," and notifies Mr. A's device.

[1559] Side effect reporting and feedback

[1560] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[1561] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[1562] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[1563] Example: If Person A reports feeling nauseous after taking Medicine B, the generated AI will advise the person on how to deal with the situation, such as "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[1564] Remaining quantity management and automatic replenishment

[1565] User: Enter the remaining amount of medication.

[1566] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[1567] Server: Places automatic orders to the online store as needed.

[1568] Example: When Person A enters into the app that he or she is running low on Medicine A, the server sends a notification saying, "Your remaining supply of Medicine A is low. Would you like to place an order automatically?" If Person A agrees, the server automatically completes the order with the online store.

[1569] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[1570] Example prompts for generative AI models

[1571] 1. "Can I take medication A and medication B at the same time?"

[1572] 2. "I have breakfast at 8:00, lunch at 12:00, and dinner at 19:00. Please tell me the medication schedule that fits this lifestyle."

[1573] 3. "I feel nauseous after taking medicine B. What should I do?"

[1574] The above is an embodiment of the present invention.

[1575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1576] User registration and initial drug information settings

[1577] Step 1:

[1578] User: Launch the app and the user registration screen will appear.

[1579] What it does: When a user launches the app for the first time, a form to enter personal information is displayed.

[1580] Step 2:

[1581] User: Enter personal information, a list of medications being taken, and a history of side effects.

[1582] Input: Name, age, gender, medications being taken, side effect history.

[1583] Specific behavior: The user fills in each field.

[1584] Step 3:

[1585] Terminal: Sends the entered information to the server.

[1586] Input: Personal and medication information entered by the user.

[1587] Specific operation: The terminal sends the input data to the server.

[1588] Output: Data packets sent to the server.

[1589] Step 4:

[1590] Server: Stores the received information in a database.

[1591] Input: Data packets sent from the terminal.

[1592] Specific operation: The server receives the data packet and stores it in a database.

[1593] Output: User information stored in the database.

[1594] Conversational Concierge

[1595] Step 1:

[1596] User: Enter questions about drug interactions and side effects.

[1597] Input: The question from the user.

[1598] What happens: The user types a question into an input field in the app.

[1599] Step 2:

[1600] Terminal: The generation AI analyzes the input question and generates an appropriate answer.

[1601] Input: The question from the user.

[1602] Specific operation: The generative AI performs natural language processing and analyzes the question content.

[1603] Output: Correct answer.

[1604] Step 3:

[1605] Terminal: Displays the generated answer to the user.

[1606] Input: Answer data from the generation AI.

[1607] What it does: Formats the response data for display to the user.

[1608] Output: The answer displayed on the user's terminal.

[1609] Suggested dosing schedule

[1610] Step 1:

[1611] User: Enter your daily routine.

[1612] Input: User's daily rhythm information (e.g., breakfast, lunch, and dinner times).

[1613] Specific operation: The user inputs information about their daily rhythm.

[1614] Step 2:

[1615] Device: Based on the inputted information on daily rhythms, the AI ​​generates the optimal medication schedule.

[1616] Input: User's lifestyle information.

[1617] Specific operation: The generating AI performs calculations based on the given life rhythm.

[1618] Output: Optimal dosing schedule.

[1619] Step 3:

[1620] Terminal: Notifies the user of the calculated medication schedule.

[1621] Input: Calculated dosing schedule.

[1622] Specific operation: Notification information is sent to the user's device and displayed.

[1623] Output: Medication schedule notification displayed on the user's device.

[1624] Side effect reporting and feedback

[1625] Step 1:

[1626] User: Open the side effect report screen and enter the symptoms.

[1627] Input: The side effect symptoms experienced by the user.

[1628] Specific action: The user enters their symptoms into the input field.

[1629] Step 2:

[1630] Terminal: Sends side effect information to the server.

[1631] Input: The side effect information entered.

[1632] Specific operation: The device sends side effect information to the server.

[1633] Output: Data packets sent to the server.

[1634] Step 3:

[1635] Server: Analyzes the input information and generates appropriate countermeasures.

[1636] Input: Side effect information.

[1637] Specific operation: The generative AI analyzes side effect information and considers appropriate countermeasures.

[1638] Output: Appropriate action.

[1639] Step 4:

[1640] Terminal: Notify the user of the generated solution.

[1641] Input: The generated solution.

[1642] Specific action: Format and send to inform the user of the corrective action.

[1643] Output: Actions displayed on the user's terminal.

[1644] Step 5:

[1645] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[1646] Input: Side effect information.

[1647] Specific operation: The server stores side effect information and periodically creates and sends reports to relevant institutions.

[1648] Output: Side effect information stored in the database and feedback reports.

[1649] Remaining quantity management and automatic replenishment

[1650] Step 1:

[1651] User: Enter the remaining amount of medication.

[1652] Input: remaining amount of medication.

[1653] Specific behavior: The user enters the remaining amount of medication into the input field.

[1654] Step 2:

[1655] Terminal: Sends the entered remaining amount information to the server.

[1656] Input: Remaining medication information.

[1657] Specific operation: The device sends remaining battery information to the server.

[1658] Output: Data packets sent to the server.

[1659] Step 3:

[1660] Server: If the remaining amount falls below a threshold, an automatic replenishment suggestion is made.

[1661] Input: Remaining information.

[1662] What happens: The server checks the remaining amount and generates replenishment suggestions as needed.

[1663] Output: Automatic replenishment suggestion notification.

[1664] Step 4:

[1665] Terminal: If the user accepts automatic ordering, places an order with the online store.

[1666] Enter: Automatic Purchase Order Acceptance.

[1667] Specific action: The user presses the accept button.

[1668] Output: An order request to the online store.

[1669] Step 5:

[1670] Server: Completes the order with the online store.

[1671] Input: Order information accepted by the user.

[1672] Specific operation: The server sends an order request to the online store.

[1673] Output: Order data sent to the online store.

[1674] (Application example 1)

[1675] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1676] In conventional drug management systems, managing drug interactions and side effects is done manually, which is not only inefficient but also increases the risk of misuse. Furthermore, managing security devices requires checking their status and configuring them individually, which is extremely time-consuming. To solve these problems, there is a growing need for a centralized management system that utilizes generative AI.

[1677] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1678] In this invention, the server includes means for evaluating drug interactions using a generating AI, means for proposing a dosing schedule based on the user's lifestyle, means for the user to input side effect information, means for evaluating the status of security devices and proposing optimal settings and operation schedules, and means for analyzing security-related problem reports and generating and notifying appropriate countermeasures. This enables drug management and security device management to be performed in a unified manner, enabling efficient and safe operation.

[1679] "Generative AI" uses artificial intelligence techniques such as natural language processing to generate appropriate answers to questions from users.

[1680] The "means for evaluating drug interactions" is a function that analyzes the effects of taking multiple drugs simultaneously and instructs the user on the appropriate method of taking them.

[1681] The "means for suggesting a medication schedule" is a function that calculates the optimal timing for taking medication based on the user's lifestyle and notifies the user.

[1682] "Means for inputting side effect information" refers to a data input interface that allows a user to report side effects of a drug.

[1683] The "means for storing in a database" refers to a system that safely stores the entered side effect information and manages it in a manner that allows it to be retrieved as needed.

[1684] "Means of providing feedback to relevant organizations" refers to a function that periodically sends accumulated side effect information to relevant organizations such as medical institutions.

[1685] "Means for managing remaining medication and automatically suggesting replenishment" refers to a function that monitors the remaining amount of medication possessed by the user and automatically suggests replenishment when it falls below a certain threshold.

[1686] "A means for evaluating the status of security equipment and proposing optimal settings and operation schedules" is a means for analyzing the operating status of security equipment such as surveillance cameras and sensors, and proposing optimal settings and schedules to users.

[1687] "Means for analyzing security-related problem reports and generating and notifying appropriate countermeasures" refers to a system that analyzes security device problems reported by users, generates appropriate countermeasures, and notifies the users.

[1688] The "means for managing automatic replenishment" is a function for monitoring the maintenance status of security equipment and the remaining amount of consumables, and for timely replenishment or replacement.

[1689] The system for implementing this invention uses generative AI to manage drug interactions, drug schedules, side effects, monitor the status of security devices, and automatically replenish medications. The system operates by exchanging information between a server, terminals, and users.

[1690] User registration and initial settings

[1691] User: When the application is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, side effect history, and security device information.

[1692] Terminal: Sends the entered information to the server. The user's personal information, medication information, and security device information are stored in the server's database.

[1693] Conversational Concierge

[1694] User: Enter questions about drug interactions and security device settings.

[1695] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[1696] For example, in response to the question, "My surveillance camera isn't recording. What should I do?", the AI ​​generator will answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[1697] Schedule suggestions

[1698] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times) and security device usage schedule.

[1699] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and security device operation schedule and notifies the user.

[1700] Server: Stores schedule information in a database and sends reminders to users when necessary.

[1701] For example, we suggest a schedule such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[1702] Reporting and Feedback on Side Effects and Problems

[1703] Users: If they experience a side effect from a medication or a problem with a security device, they can open a dedicated reporting screen and enter details of the symptoms or problem.

[1704] Device: Sends reported information to the server. The generated AI analyzes the input information and notifies the user of the appropriate countermeasures.

[1705] Server: The reported information is stored in a database and periodically provided to relevant authorities and security companies.

[1706] For example, if you report that "the sensor at the front door is not responding," the generating AI will notify you of the corrective action, such as "check the sensor battery and replace it if necessary," and this information will be regularly fed back to the relevant authorities.

[1707] Remaining quantity management and automatic replenishment

[1708] User: Enters remaining amounts and status of medications and security devices.

[1709] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[1710] Server: Automatically orders consumables and replacement parts as needed.

[1711] Example: "The sensor battery is low. Would you like to order a new battery automatically?" If the user agrees, the server will automatically complete the order with the online store.

[1712] Hardware and Software Used

[1713] Hardware: Smartphones, internet connections, security equipment (surveillance cameras, sensors, etc.)

[1714] software:

[1715] Programming language: Python

[1716] API: REST API for exchanging data related to the security system

[1717] Server: Security database on the cloud (e.g. Amazon AWS, Google Cloud)

[1718] Generative AI models: Natural Language Processing (NLP) models for generating answers to user questions (e.g., OpenAI GPT-4)

[1719] Add specific examples

[1720] Example prompts to input to the generative AI model

[1721] Question: "My security cameras aren't recording, what should I do?"

[1722] Prompt: "What should I do if my security camera isn't recording? Please list possible causes and solutions that apply to your answer."

[1723] My security camera isn't recording, what should I do?

[1724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1725] Step 1:

[1726] (User registration and initial settings)

[1727] Input: The user enters personal information, medications, and security device information into the app.

[1728] Operation: The terminal sends the entered information to the server, which receives the information and stores it in a database.

[1729] Output: The user's personal information, medication information, and security device information are saved in the database.

[1730] Step 2:

[1731] (Interactive concierge function)

[1732] Input: The user inputs questions about drug interactions and security device settings.

[1733] How it works: The device sends a question to the generative AI model, which analyzes the question and generates an appropriate answer.

[1734] Output: The answer from the generative AI model is sent to the device and displayed to the user.

[1735] Specific operation: In response to the question, "My surveillance camera is not recording. What should I do?", the generative AI model generates the answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[1736] Step 3:

[1737] (Schedule proposal)

[1738] Input: The user inputs their daily routine and schedule for using security devices.

[1739] Operation: The device sends the input information to the server, which uses the generative AI model to calculate the optimal medication schedule and security device operation schedule.

[1740] Output: The generated schedule is notified to the user.

[1741] Specific operation: Based on the user's daily rhythm, the generating AI will make suggestions such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[1742] Step 4:

[1743] (Reporting and Feedback on Side Effects and Problems)

[1744] Input: To report side effects or problems with security devices, users enter details of symptoms or problems into a dedicated reporting screen.

[1745] Operation: The device sends the report information to the server, which uses the generative AI model to analyze the report information and generate appropriate countermeasures.

[1746] Output: The generated solutions are notified to the user. The reported information is also saved in a database and periodically fed back to the relevant organizations.

[1747] Specific operation: When you report that "the front door sensor is not responding," the generating AI will generate and notify you of the solution, such as "check the sensor battery and replace it if necessary."

[1748] Step 5:

[1749] (Remaining amount management and automatic replenishment)

[1750] Input: The user inputs the remaining amount and status of medicines and security devices.

[1751] Operation: The device sends the input information to the server. The server references the database and suggests automatic replenishment if the remaining amount falls below a threshold. If necessary, it automatically places an order with the online store.

[1752] Output: The user is notified of the replenishment proposal, and after user approval, automatic ordering is completed.

[1753] Specific operation: The user is notified, "The sensor battery is low. Would you like to automatically order a new battery?" If the user agrees, the server will automatically complete the order.

[1754] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1755] The following is a detailed description of the mode for carrying out this invention. This system uses generative AI to manage drug interactions, dosage schedules, and side effects, and automatically replenish remaining amounts. In addition, by combining it with an emotion engine, it can also respond to the user's emotional state.

[1756] User registration and initial drug information settings

[1757] User: When the app is first launched, the user is presented with a registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, any side effects they have experienced, and their emotional state.

[1758] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[1759] Example: When Tanaka starts the app for the first time, she answers questions about her name, age, medications she is taking (drug A, drug B), past side effects (skin rash), and initial emotional state. This allows the app to suggest the optimal medication schedule and emotional advice for Tanaka.

[1760] Conversational Concierge

[1761] User: Enter questions about drug interactions and side effects.

[1762] Terminal: Sends the question content to the generation AI and emotion engine.

[1763] Server: The generation AI analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The answer is then displayed to the user.

[1764] Example: When Tanaka asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." The emotion engine recognizes Tanaka's worried expression and advises, "If you are worried, we recommend that you consult a doctor."

[1765] Suggested dosing schedule

[1766] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1767] Terminal: Sends the inputted life rhythm to the server.

[1768] Server: Based on lifestyle and medication information, the generative AI and emotion engine calculate the optimal medication schedule.

[1769] Example: Based on Tanaka's daily rhythm, a schedule is suggested such as "Take medicine A at 8:00 AM and medicine B at 8:30 AM." If Tanaka looks busy, the emotion engine will make a flexible suggestion such as "If these times are difficult, it is also possible to shift the times of each dose by one hour."

[1770] Side effect reporting and feedback

[1771] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[1772] Terminal: Sends side effect information to the server. The generation AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of the appropriate course of action.

[1773] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[1774] Example: If Tanaka reports feeling nauseous after taking drug B, the generative AI will advise him to "drink water and rest," and the emotion engine will recognize Tanaka's anxious feelings and send an additional message saying, "We will inform you of different measures the next time you take the drug."

[1775] Remaining quantity management and automatic replenishment

[1776] User: Enter remaining amount of medicine or supplement.

[1777] Terminal: Sends the entered remaining amount information to the server.

[1778] Server: Saves remaining information in a database. The emotion engine also references this information.

[1779] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[1780] Terminal: Display a restock notification to the user.

[1781] User: Approves replenishment.

[1782] Server: After receiving approval, the order is automatically placed with the online store.

[1783] Example: When Tanaka inputs that he is running low on medicine A, a notification is sent saying, "Your remaining medicine A is low. Would you like us to automatically place an order for you?" Tanaka accepts, and the server automatically completes the order. Furthermore, if the emotion engine recognizes that Tanaka is stressed, it displays the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[1784] This system allows users to manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in a unified manner, according to their emotional state, allowing them to maximize the effectiveness of their medications.

[1785] The processing flow will be explained below.

[1786] User registration and initial drug information settings

[1787] Step 1:

[1788] User: Launches the app for the first time.

[1789] Step 2:

[1790] Terminal: Display the user registration screen.

[1791] Step 3:

[1792] User: Answers initial setup questions to identify personal information (such as name, age, and gender), a list of medications taken, side effects, and emotional state.

[1793] Step 4:

[1794] Terminal: Sends the entered information to the server.

[1795] Step 5:

[1796] Server: Stores the received personal information and medication information in a database.

[1797] Conversational Concierge

[1798] Step 1:

[1799] User: Enter questions about drug interactions and side effects.

[1800] Step 2:

[1801] Terminal: Sends the question content to the generation AI and emotion engine.

[1802] Step 3:

[1803] Server: The generation AI analyzes the question content, and the emotion engine evaluates the user's emotional state.

[1804] Step 4:

[1805] Server: The generation AI generates an appropriate answer based on the analysis results.

[1806] Step 5:

[1807] Server: The emotion engine generates supplementary advice according to the emotional state.

[1808] Step 6:

[1809] Server: Sends the generated answers and supplementary advice to the terminal.

[1810] Step 7:

[1811] Terminal: The answer and supplementary advice are displayed to the user.

[1812] Suggested dosing schedule

[1813] Step 1:

[1814] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[1815] Step 2:

[1816] Terminal: Sends the inputted life rhythm to the server.

[1817] Step 3:

[1818] Server: Generative AI calculates the optimal dosing schedule based on lifestyle rhythm information and medication information.

[1819] Step 4:

[1820] Server: The emotion engine evaluates the user's emotional state and checks whether the schedule places a burden on the user.

[1821] Step 5:

[1822] Server: Sends the calculated dosing schedule to the terminal.

[1823] Step 6:

[1824] Terminal: Notifies the user of their medication schedule.

[1825] Side effect reporting and feedback

[1826] Step 1:

[1827] User: If you experience any side effects, open the side effect report screen.

[1828] Step 2:

[1829] Device: Displays a screen for selecting the type of side effect and symptoms.

[1830] Step 3:

[1831] User: Enter specific symptoms and submit report.

[1832] Step 4:

[1833] Terminal: Sends the entered side effect information to the server.

[1834] Step 5:

[1835] Server: Stores the received side effect information in a database.

[1836] Step 6:

[1837] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[1838] Step 7:

[1839] Server: The emotion engine evaluates the user's emotional state and adds additional information to the response.

[1840] Step 8:

[1841] Server: Sends the generated solution and additional information to the terminal.

[1842] Step 9:

[1843] Terminal: Display solutions and additional information to the user.

[1844] Step 10:

[1845] Server: Stores accumulated side effect information in a database and periodically provides feedback to relevant institutions.

[1846] Remaining quantity management and automatic replenishment

[1847] Step 1:

[1848] User: Enter remaining amount of medicine or supplement.

[1849] Step 2:

[1850] Terminal: Sends the entered remaining amount information to the server.

[1851] Step 3:

[1852] Server: Saves remaining information in a database.

[1853] Step 4:

[1854] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[1855] Step 5:

[1856] Server: The emotion engine evaluates the user's emotional state and predicts their reaction to the notification.

[1857] Step 6:

[1858] Server: Sends a notification message to the terminal.

[1859] Step 7:

[1860] Terminal: Display a restock notification to the user.

[1861] Step 8:

[1862] User: Approves replenishment.

[1863] Step 9:

[1864] Terminal: Sends authorization information to the server.

[1865] Step 10:

[1866] Server: After receiving approval for replenishment, the server automatically places an order with the online store.

[1867] In this way, a system is realized that performs integrated medication management while also taking into account the user's emotional state.

[1868] Example 2

[1869] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1870] Conventional medication management systems make it difficult for users to efficiently manage multiple medications, requiring a great deal of effort to manage drug interactions, dosage schedules, side effects, and remaining medication amounts. Furthermore, the system does not respond to the user's emotional state, which can increase anxiety and stress. To solve this problem, a comprehensive medication management system that combines a generative AI model and an emotion engine is needed.

[1871] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for transmitting personal information and drug information entered by the user to the server and storing them in a database; a means for transmitting the user's question to the generation AI model and emotion engine, analyzing the question, evaluating the user's emotional state, and generating an appropriate answer; a means for calculating an optimal drug schedule based on the user's lifestyle information; a means for the user to input side effect information; a means for analyzing the side effect information, evaluating the user's emotional state, and notifying the user of appropriate measures; a means for storing the side effect information in a database and periodically providing feedback to relevant institutions; and a means for managing the user's remaining medication, automatically suggesting replenishment, and placing an order. This allows the user to efficiently manage multiple medications and quickly take appropriate measures. Furthermore, the support provided by the emotion engine minimizes the user's anxiety and stress.

[1872] 1. "Server" refers to a computer system that manages, stores, and processes data over a network.

[1873] 2. "Database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[1874] 3. "Generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to generate appropriate outputs (e.g., answers, suggestions, countermeasures, etc.) from input information.

[1875] 4. "Emotion engine" refers to a computer program that analyzes a user's emotional state based on input, facial expressions, voice, etc., and determines an appropriate response.

[1876] 5. "User" refers to an individual who uses this system.

[1877] 6. "Personal information" refers to information that can identify a specific individual, such as name, age, and gender.

[1878] 7. "Medication information" refers to detailed information about the medications a user is taking, such as the name, dosage, and time of administration.

[1879] 8. "Side effect information" refers to information about undesirable symptoms or reactions that occur as a result of taking a drug.

[1880] 9. "Lifestyle information" refers to information such as meal and activity times in a user's daily life.

[1881] 10. "Dosing schedule" refers to the specific time and order in which a user takes medication.

[1882] 11. "Balance management" refers to the process of monitoring the current level of medications or supplements taken by a user and replenishing them appropriately.

[1883] 12. "Relevant organizations" refers to organizations involved in the management of drugs and the monitoring of side effects, such as medical institutions, pharmacies, and insurance companies.

[1884] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI model and an emotion engine to manage drug interactions, dosage schedules, and side effects, automatically replenish remaining doses, and also responds to the user's emotional state. Hardware used includes a server, cloud storage, and user devices (smartphones, tablets, etc.). Software includes a generative AI, emotion engine, database management system, etc.

[1885] User registration and initial drug information settings

[1886] When a user launches the app, they are presented with a user registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. The device then sends this information to the server, which stores it in a database.

[1887] Examples:

[1888] When a user launches the app for the first time, they answer some initial questions about their name, age, medications they are taking (drug X, drug Y), past side effects (rashes), and emotional state. The device sends this information to the server, which stores it in a database.

[1889] Conversational Concierge

[1890] When a user inputs a question about drug interactions or side effects, the device sends the question to the generation AI and emotion engine. The server then uses the generation AI to analyze the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The device then displays the generated answer to the user.

[1891] Examples:

[1892] When a user asks, "Is it okay to take medication X and medication Y at the same time?", the generative AI responds, "Taking medication X and medication Y at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two," and the emotion engine recognizes the user's anxious expression and advises, "If you are worried, we recommend consulting a doctor." This is displayed to the user on the device.

[1893] Suggested dosing schedule

[1894] When a user inputs their daily rhythm (e.g., breakfast, lunch, and dinner times), the device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule and presents it to the user via the device.

[1895] Examples:

[1896] When a user inputs their daily routine, the system suggests a schedule, such as "Take medication X at 8:00 a.m. and medication Y at 8:30 a.m." If the user looks busy, the emotion engine suggests, "If these times are difficult, you can shift the times of each dose by one hour."

[1897] Side effect reporting and feedback

[1898] When a user experiences a side effect, they enter their symptoms on the side effect reporting screen, and the device sends the information to the server. The server then uses generative AI and an emotion engine to analyze the side effect information, generate appropriate countermeasures, and store them in a database. The appropriate countermeasures are notified to the user and periodically provided as feedback to relevant organizations.

[1899] Examples:

[1900] If a user reports feeling nauseous after taking drug Y, the generation AI will advise them to "drink water and rest," and the emotion engine will recognize the user's anxious feelings and generate an additional message saying, "We will inform you of different measures the next time you take the drug." The device will notify the user of this.

[1901] Remaining quantity management and automatic replenishment

[1902] When a user inputs the remaining amount of medicine or supplement, the device sends the remaining amount information to the server. The server stores the remaining amount information in a database, and when the remaining amount falls below a set threshold, it automatically generates a notification to replenish and displays the notification on the device. If the user approves the replenishment, the server automatically places an order with the online store.

[1903] Examples:

[1904] When the user inputs that they are low on medicine X, a notification is sent saying, "Your remaining medicine X is low. Would you like us to automatically place an order for you?" If the user agrees, the server will automatically complete the order. Also, if the emotion engine recognizes that the user is stressed, it will display the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[1905] Example prompts for generative AI models

[1906] Example prompt 1: Suggested medication schedule

[1907] "Please suggest the best dosing schedule based on the user's daily rhythm of taking medication X at 8:00 AM and medication Y at 8:30 AM."

[1908] Prompt example 2: Conversational concierge function

[1909] "User is asking if it's okay to take medication X and medication Y at the same time. Please provide some advice on how to do this safely. User seems a little nervous."

[1910] This system allows users to efficiently manage multiple medications and take appropriate measures quickly, while its emotional engine supports users in minimizing anxiety and stress.

[1911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1912] Program processing flow

[1913] User registration and initial drug information settings

[1914] Step 1:

[1915] User: Launch the app and open the user registration screen.

[1916] Input: Name, age, gender, list of medications you are taking, side effect history, and default questions about your emotional state.

[1917] What happens: A user fills out a form.

[1918] Output: The entered user information.

[1919] Step 2:

[1920] Terminal: Sends the entered information to the server.

[1921] Input: Personal information and medication information entered by the user.

[1922] How it works: The device sends information over the network to the server.

[1923] Output: The user information sent.

[1924] Step 3:

[1925] Server: Stores the received user information in a database.

[1926] Input: User information sent from the device.

[1927] What it does: Executes a query that stores information in a database.

[1928] Output: User information stored in the database.

[1929] Conversational Concierge

[1930] Step 1:

[1931] User: Enter questions about drug interactions and side effects.

[1932] Input: The user's question.

[1933] What it does: Enter a question into the form.

[1934] Output: The question entered.

[1935] Step 2:

[1936] Terminal: Sends the question content to the generative AI model and emotion engine.

[1937] Input: The user's question.

[1938] Operation: Sends the query to the server.

[1939] Output: The question submitted.

[1940] Step 3:

[1941] Server: The generative AI model analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer.

[1942] Input: The user's question.

[1943] How it works: A generative AI model analyzes the question, and an emotion engine assesses the user's emotional state.

[1944] Output: The generated answer.

[1945] Step 4:

[1946] Terminal: Displays the generated answer to the user.

[1947] Input: The answer output from the generative AI model and the emotion engine.

[1948] Behavior: Displays the answer in the user interface.

[1949] Output: The answer shown to the user.

[1950] Suggested dosing schedule

[1951] Step 1:

[1952] User: Enter lifestyle information (breakfast, lunch, dinner times, etc.).

[1953] Input: User's lifestyle information.

[1954] Action: Enter your lifestyle information in the input form.

[1955] Output: Inputted life rhythm information.

[1956] Step 2:

[1957] Terminal: Sends the inputted life rhythm information to the server.

[1958] Input: User's lifestyle information.

[1959] Operation: Sends life rhythm information to the server.

[1960] Output: Transmitted life rhythm information.

[1961] Step 3:

[1962] Server: A generative AI model and emotion engine calculate the optimal medication schedule based on lifestyle information.

[1963] Input: User's lifestyle information.

[1964] How it works: The generative AI model analyzes daily rhythm information, and the emotion engine evaluates the patient's emotional state to create an optimal medication schedule.

[1965] Output: Optimal dosing schedule.

[1966] Step 4:

[1967] Terminal: Displays the generated dosing schedule to the user.

[1968] Input: The dose schedule output from the generative AI model and the emotion engine.

[1969] What it does: Displays the medication schedule in the user interface.

[1970] Output: The dosing schedule displayed to the user.

[1971] Side effect reporting and feedback

[1972] Step 1:

[1973] User: Open the side effect report screen and enter the symptoms.

[1974] Input: Side effect symptoms.

[1975] Action: Enter your symptoms in the form.

[1976] Output: The entered side effect information.

[1977] Step 2:

[1978] Terminal: Sends side effect information to the server.

[1979] Input: Side effect symptoms.

[1980] Action: Sends side effect information to the server.

[1981] Output: The side effect information sent.

[1982] Step 3:

[1983] Server: The generative AI model analyzes side effect information, and the emotion engine evaluates the emotional state and generates appropriate countermeasures.

[1984] Input: Side effect symptoms.

[1985] How it works: A generative AI model analyzes side effect information, and an emotion engine evaluates the user's emotional state.

[1986] Output: The generated solutions.

[1987] Step 4:

[1988] Device: Notify the user of the generated solution.

[1989] Input: The generative AI model and the response output from the emotion engine.

[1990] Action: Notify the user on what to do.

[1991] Output: The action that was notified to the user.

[1992] Step 5:

[1993] Server: Stores side effect information in a database and periodically provides feedback to relevant organizations.

[1994] Input: Side effect information and generated remedies.

[1995] What it does: Runs queries that are saved in a database and fed back to relevant authorities.

[1996] Output: Information stored in database and feedback report.

[1997] Remaining quantity management and automatic replenishment

[1998] Step 1:

[1999] User: Enter remaining amount of medication or supplements.

[2000] Input: Remaining amount of medication or supplements.

[2001] Operation: Enter the remaining amount in the input form.

[2002] Output: The remaining amount information entered.

[2003] Step 2:

[2004] Device: Sends remaining charge information to the server.

[2005] Input: The remaining amount information entered by the user.

[2006] Operation: Sends remaining amount information to the server.

[2007] Output: The remaining information sent.

[2008] Step 3:

[2009] Server: Stores remaining quantity information in a database and generates a replenishment notification when the quantity falls below a set threshold.

[2010] Input: Remaining information.

[2011] What it does: It stores it in a database and runs an algorithm that monitors the remaining amount.

[2012] Output: Replenishment notification.

[2013] Step 4:

[2014] Terminal: Display a restock notification to the user.

[2015] Input: The generated replenishment notice.

[2016] Behavior: Displays a refill notification in the user interface.

[2017] Output: Refill notification displayed to the user.

[2018] Step 5:

[2019] User: Approves replenishment.

[2020] Enter: Refill Notice.

[2021] Action: Enter an acknowledgement for the replenishment notice.

[2022] Output: Replenishment approved.

[2023] Step 6:

[2024] Server: After receiving approval, the order is automatically placed with the online store.

[2025] Input: User's replenishment authorization.

[2026] Operation: Calls the order API to the online store.

[2027] Output: Order completion notification.

[2028] How it works: The user enters the remaining amount of medicine or supplements, and the information is sent to the server. The server stores the information in a database, and if the amount falls below a set threshold, it generates a replenishment notification and displays it to the user. If the user approves the replenishment, the server automatically completes the order with the online store. In addition, an emotion engine evaluates the user's emotions and generates appropriate feedback messages.

[2029] (Application example 2)

[2030] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2031] The present invention focuses on the need for medication management that goes beyond simple schedule management to provide advice tailored to the user's emotional state, manage side effects, and automatically replenish medications in a unified manner. Conventional systems do not take the user's emotional state into account, making it difficult to provide appropriate support and making it difficult for users to continue taking their medication. This can result in medications not being as effective as they could be, potentially hindering the user's health management.

[2032] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for evaluating drug interactions using a generation AI, means for proposing a drug administration schedule based on the user's lifestyle, means for the user to input side effect information, means for storing the side effect information in a database and periodically providing feedback to relevant institutions, means for managing the user's remaining medication and automatically suggesting replenishment, and means for responding to the user's emotional state using an emotion engine. This allows the user to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in accordance with their emotional state.

[2033] "Generative AI" is a technology that uses artificial intelligence to analyze data and generate new information and answers.

[2034] The "means for evaluating drug interactions" is a function that evaluates the interactions when multiple drugs are taken simultaneously and suggests appropriate dosage methods.

[2035] The "means for proposing a medication schedule based on the user's lifestyle" is a function that takes into account the user's daily rhythm and suggests the optimal timing for taking medication.

[2036] "Means for users to input side effect information" is a function that allows users to input information about drug side effects they have experienced into the system.

[2037] "Means for storing side effect information in a database and providing regular feedback to relevant institutions" refers to a function that stores collected side effect information in a database and provides that information to medical institutions and relevant institutions on a regular basis.

[2038] "Means for managing the remaining amount of medication for the user and automatically suggesting refills" refers to a function that monitors the remaining amount of medication for the user and automatically suggests and arranges for refills as necessary.

[2039] "Means for responding to the user's emotional state using an emotion engine" is a function that analyzes the user's emotional state and provides appropriate advice and responses accordingly.

[2040] The following is a detailed description of the mode for carrying out this invention. This system uses a smartphone application to support users in managing their medications, and by combining a generative AI and an emotion engine, it manages drug interactions, dosage schedules, side effects, and remaining amounts, providing support according to the user's emotional state.

[2041] User registration and initial drug information settings

[2042] The server provides a user registration screen where the user enters personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. This information is stored in a database for later use by the generative AI model and emotion engine.

[2043] For example, when a user first launches the app, they answer questions about their name, age, medications they are taking (drug A, drug B), past side effects (skin rashes), and initial emotional state. This allows the app to provide the user with advice based on their optimal medication schedule and emotions.

[2044] Conversational Concierge

[2045] Users ask questions about drug interactions and side effects. The generative AI model analyzes the question, and the emotion engine evaluates the user's facial expressions and emotional state to generate an appropriate answer and display it to the user.

[2046] For example, if a user asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." If the emotion engine recognizes that the user looks worried, it will advise, "If you are concerned, we recommend that you consult a doctor."

[2047] Suggested dosing schedule

[2048] The server uses a generative AI and emotion engine to calculate and propose an optimal medication schedule to the user based on the user's lifestyle information. The user inputs their lifestyle information (e.g., breakfast, lunch, and dinner times).

[2049] For example, based on the user's daily rhythm, the system suggests a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." If the user looks busy, the emotion engine will make a flexible suggestion such as, "If these times are difficult, we can also shift the times of each dose by one hour."

[2050] Side effect reporting and feedback

[2051] If a user experiences a side effect, they open the side effect reporting screen and enter their symptoms. Based on this, the generative AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of appropriate measures. The server stores the reported side effect information in a database and periodically provides feedback to relevant institutions.

[2052] For example, if a user reports feeling nauseous after taking drug B, the generative AI will suggest a course of action such as "drink water and rest," and if the emotion engine recognizes the user's feelings of anxiety, it will send an additional message saying, "We will inform you of different ways to deal with the situation the next time you take the drug."

[2053] Remaining quantity management and automatic replenishment

[2054] When a user enters the remaining amount of medicine or supplement, the server stores the information in a database and automatically generates a notification for refilling when the amount falls below a set threshold. If the user approves the refill, an order is automatically placed with the online store.

[2055] For example, if a user inputs that they are running low on medication A, a notification will be sent saying, "Your remaining medication A is low. Would you like us to automatically place an order for you?" If the user agrees, the server will automatically complete the order. Also, if the emotion engine recognizes that the user is stressed, it will display a message saying, "The order has been completed. Please wait a moment. Thank you for your hard work."

[2056] Hardware and software used

[2057] Hardware: Smartphone (iOS or Android)

[2058] Software: Smartphone apps (e.g., MediGuard Pro), server-side platforms (cloud services such as AWS)

[2059] Generative AI models: GPT-4, etc.

[2060] Emotion engine: EmotionAPI, etc.

[2061] Examples of prompt statements

[2062] I have a question about the functionality of the medication management app "MediGuard Pro." Is it okay to take "Medicine A" and "Medicine B" at the same time?

[2063] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2064] Step 1:

[2065] When a user launches the smartphone application for the first time, they enter their personal information (such as name, age, and gender), a list of medications they are taking, their side effect history, and their emotional state. This data is then sent from the device to the server, which then stores the entered information in a database.

[2066] input:

[2067] User personal and medication information

[2068] output:

[2069] Stored user information database

[2070] Specific behavior:

[2071] The user enters information into the application screen and presses the "Submit" button.

[2072] The server receives the data and stores it in a database

[2073] Step 2:

[2074] The user inputs a question. The device sends the user's question to the generative AI model. The generative AI model analyzes the question and generates an answer. This answer is then sent to the device via the server and finally displayed to the user. At the same time, the emotion engine analyzes the user's facial expressions and emotional state and provides appropriate additional advice.

[2075] input:

[2076] User Questions

[2077] output:

[2078] Answers from generative AI models

[2079] Additional advice from the emotion engine

[2080] Specific behavior:

[2081] The user enters a question into a text box within the application and presses the "Ask" button.

[2082] Generative AI model analyzes and generates answers

[2083] The emotion engine analyzes the user's facial expressions and generates advice

[2084] Answers and advice are displayed to the user

[2085] Step 3:

[2086] The user inputs information about their daily rhythm (e.g., breakfast, lunch, and dinner times). The device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule. This schedule is then displayed to the user via the device.

[2087] input:

[2088] User's lifestyle information

[2089] output:

[2090] Optimal dosing schedule

[2091] Specific behavior:

[2092] The user enters their lifestyle information and presses the "Submit" button.

[2093] The server receives the information and calculates the schedule using a generative AI model and emotion engine.

[2094] The calculated schedule is displayed to the user

[2095] Step 4:

[2096] If a user experiences a side effect, they open the side effect report screen and enter their symptoms. The device sends this information to the server, where the generative AI model analyzes it and generates a solution. The emotion engine also evaluates the user's emotional state and generates additional advice. This information is then communicated to the user via the device.

[2097] input:

[2098] User side effects information

[2099] output:

[2100] How to address this with generative AI models

[2101] Additional advice from the emotion engine

[2102] Specific behavior:

[2103] The user enters the side effect information and presses the "Submit" button.

[2104] The server receives the data, and the generative AI model analyzes it and generates a solution.

[2105] The emotion engine analyzes the user's facial expressions and generates advice

[2106] Users are notified of solutions and advice

[2107] Step 5:

[2108] The user enters the remaining amount of medicine or supplement. The device sends this information to the server, which stores it in a database. If the remaining amount falls below a set threshold, the server generates a refill notification. The notification is sent to the device and displayed to the user. If the user approves the refill, the server automatically places an order with the online store.

[2109] input:

[2110] User's remaining medication information

[2111] output:

[2112] Replenishment Notification and Ordering

[2113] Specific behavior:

[2114] The user enters the remaining amount information and presses the "Submit" button.

[2115] The server receives the information and stores it in a database

[2116] Generates a refill notification when the remaining amount falls below a threshold

[2117] When the user approves the replenishment, the server automatically places an order with the online store.

[2118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2120] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2121] [Fourth embodiment]

[2122] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2123] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2126] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2129] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2131] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2133] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2134] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2135] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining amounts. The system operates by exchanging information between the server, terminals, and users.

[2136] User registration and initial drug information settings

[2137] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[2138] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[2139] Example: When Tanaka starts the app for the first time, she enters her name, age, medications she is taking (drug A, drug B), and any side effects she has had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Tanaka.

[2140] Conversational Concierge

[2141] User: Enter questions about drug interactions and side effects.

[2142] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[2143] Example: When Tanaka asks, "Can I take medicine A and medicine B at the same time?", the generated AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[2144] Suggested dosing schedule

[2145] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[2146] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[2147] Example: Based on Tanaka's daily routine, the AI ​​generator will propose a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m." and notify Tanaka's device of this.

[2148] Side effect reporting and feedback

[2149] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[2150] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[2151] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[2152] Example: If Tanaka reports feeling nauseous after taking medicine B, the generated AI will advise him on how to deal with the situation, saying, "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[2153] Remaining quantity management and automatic replenishment

[2154] User: Enter the remaining amount of medication.

[2155] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[2156] Server: Places automatic orders to the online store as needed.

[2157] Example: When Tanaka inputs into the app that he is running low on medicine A, the server sends a notification saying, "You are running low on medicine A. Would you like to place an order automatically?" If Tanaka accepts, the server automatically completes the order with the online store.

[2158] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[2159] The processing flow will be explained below.

[2160] User registration and initial drug information settings

[2161] Step 1:

[2162] User: Launches the app for the first time.

[2163] Step 2:

[2164] Terminal: Display the user registration screen.

[2165] Step 3:

[2166] User: Enter personal information (name, age, gender, etc.), a list of medications being taken, and a history of side effects.

[2167] Step 4:

[2168] Terminal: Sends the entered information to the server.

[2169] Step 5:

[2170] Server: Stores the received personal information and medication information in a database.

[2171] Conversational Concierge

[2172] Step 1:

[2173] User: Enter questions about drug interactions and side effects.

[2174] Step 2:

[2175] Terminal: Sends the question to the generation AI.

[2176] Step 3:

[2177] Server: The generation AI analyzes the question and generates an appropriate answer.

[2178] Step 4:

[2179] Server: Sends the generated answer to the device.

[2180] Step 5:

[2181] Terminal: Display the answer to the user.

[2182] Suggested dosing schedule

[2183] Step 1:

[2184] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[2185] Step 2:

[2186] Terminal: Sends the inputted life rhythm to the server.

[2187] Step 3:

[2188] Server: Based on lifestyle and medication information, the generation AI calculates the optimal dosing schedule.

[2189] Step 4:

[2190] Server: Sends the calculated medication schedule to the terminal.

[2191] Step 5:

[2192] Terminal: Notifies the user of their medication schedule.

[2193] Side effect reporting and feedback

[2194] Step 1:

[2195] User: If you experience any side effects, open the side effect report screen.

[2196] Step 2:

[2197] Device: Displays a screen for selecting the type of side effect and symptoms.

[2198] Step 3:

[2199] User: Enter specific symptoms and submit report.

[2200] Step 4:

[2201] Terminal: Sends the entered side effect information to the server.

[2202] Step 5:

[2203] Server: Stores the received side effect information in a database.

[2204] Step 6:

[2205] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[2206] Step 7:

[2207] Server: Sends the generated solution to the terminal.

[2208] Step 8:

[2209] Terminal: Show the user how to fix the problem.

[2210] Step 9:

[2211] Server: Regularly feeds back accumulated side effect information to relevant institutions.

[2212] Remaining quantity management and automatic replenishment

[2213] Step 1:

[2214] User: Enter remaining amount of medicine or supplement.

[2215] Step 2:

[2216] Terminal: Sends the entered remaining amount information to the server.

[2217] Step 3:

[2218] Server: Saves remaining information in a database.

[2219] Step 4:

[2220] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[2221] Step 5:

[2222] Server: Sends a notification message to the terminal.

[2223] Step 6:

[2224] Terminal: Display a restock notification to the user.

[2225] Step 7:

[2226] User: Approves the replenishment.

[2227] Step 8:

[2228] Terminal: Sends authorization information to the server.

[2229] Step 9:

[2230] Server: After receiving replenishment approval, automatically place an order to the online store.

[2231] Example 1

[2232] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2233] In modern self-medication, users are required to properly manage multiple medications and take them effectively. However, various tasks, such as drug interactions, appropriate dosing schedules, side effect management, and remaining medication amounts, are complex and place a significant burden on users. Furthermore, inadequate medication management, particularly for elderly people and those with chronic diseases, increases health risks. Another issue is the lack of reporting of side effects and appropriate feedback.

[2234] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2235] In this invention, the server includes a means for evaluating drug interactions using a generative AI, a means for proposing a drug-taking schedule based on the user's lifestyle, and a means for the user to input side effect information. This allows the user to easily manage multiple drugs, take them on an appropriate schedule, and report side effects, thereby reducing health risks.

[2236] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data.

[2237] A "means for evaluating drug interactions" is a system that analyzes the possibility of multiple drugs interacting with each other and displays the results.

[2238] The "means for suggesting a medication schedule" is a system that calculates and suggests appropriate medication times based on the user's lifestyle and medication characteristics.

[2239] The "means for inputting side effect information" is an interface that allows users to input symptoms of side effects experienced after taking a drug into an application or system.

[2240] A "database" is a system that systematically stores information and data and is constructed to make it easy to search and use.

[2241] A "feedback mechanism" is a system that analyzes collected information and sends the results to specific relevant organizations and users.

[2242] The "means for managing remaining amounts" is a system that monitors the remaining amount of medication the user owns and suggests refilling it if necessary.

[2243] "Means for automatic ordering" refers to a system that automatically places orders with online stores and pharmacies based on remaining medication levels and the need for replenishment.

[2244] The "means of generating appropriate answers" is a system in which a generative AI derives and presents the most appropriate answer to a question from a user.

[2245] The "means of generating and notifying appropriate countermeasures" is a system in which a generative AI generates optimal countermeasures based on side effect information reported by users and notifies the user.

[2246] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI to manage drug interactions, drug schedules, and side effects, and automatically replenish remaining medications. The system operates by exchanging information between the server, terminals, and users.

[2247] User registration and initial drug information settings

[2248] User: When the app is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, and any side effects.

[2249] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[2250] Example: Person A starts the app for the first time and enters their name, age, medications they are taking (drug A, drug B), and any side effects they have had in the past (skin rashes). This allows the app to suggest the optimal medication schedule for Person A.

[2251] Conversational Concierge

[2252] User: Enter questions about drug interactions and side effects.

[2253] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[2254] Example: If Person A asks, "Is it okay to take medicine A and medicine B at the same time?", the generated AI will respond, "Taking medicine A and medicine B at the same time may reduce their effectiveness. We recommend taking them at least 30 minutes apart."

[2255] Suggested dosing schedule

[2256] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[2257] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and notifies the user.

[2258] Example: Based on Mr. A's daily routine, the generating AI proposes a schedule such as "Take medicine A at 8:00 a.m. and medicine B at 8:30 a.m.," and notifies Mr. A's device.

[2259] Side effect reporting and feedback

[2260] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[2261] Terminal: Sends side effect information to the server. The generation AI analyzes the input information and notifies the user of the appropriate countermeasures.

[2262] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[2263] Example: If Person A reports feeling nauseous after taking Medicine B, the generated AI will advise the person on how to deal with the situation, such as "If you feel nauseous, drink water and rest." This information will also be periodically fed back to the medical institution.

[2264] Remaining quantity management and automatic replenishment

[2265] User: Enter the remaining amount of medication.

[2266] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[2267] Server: Places automatic orders to the online store as needed.

[2268] Example: When Person A enters into the app that he or she is running low on Medicine A, the server sends a notification saying, "Your remaining supply of Medicine A is low. Would you like to place an order automatically?" If Person A agrees, the server automatically completes the order with the online store.

[2269] This system allows users to centrally manage multiple medication schedules, deal with side effects, and manage remaining medication amounts, allowing them to maximize the effectiveness of their medications.

[2270] Example prompts for generative AI models

[2271] 1. "Can I take medication A and medication B at the same time?"

[2272] 2. "I have breakfast at 8:00, lunch at 12:00, and dinner at 19:00. Please tell me the medication schedule that fits this lifestyle."

[2273] 3. "I feel nauseous after taking medicine B. What should I do?"

[2274] The above is an embodiment of the present invention.

[2275] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2276] User registration and initial drug information settings

[2277] Step 1:

[2278] User: Launch the app and the user registration screen will appear.

[2279] What it does: When a user launches the app for the first time, a form to enter personal information is displayed.

[2280] Step 2:

[2281] User: Enter personal information, a list of medications being taken, and a history of side effects.

[2282] Input: Name, age, gender, medications being taken, side effect history.

[2283] Specific behavior: The user fills in each field.

[2284] Step 3:

[2285] Terminal: Sends the entered information to the server.

[2286] Input: Personal and medication information entered by the user.

[2287] Specific operation: The terminal sends the input data to the server.

[2288] Output: Data packets sent to the server.

[2289] Step 4:

[2290] Server: Stores the received information in a database.

[2291] Input: Data packets sent from the terminal.

[2292] Specific operation: The server receives the data packet and stores it in a database.

[2293] Output: User information stored in the database.

[2294] Conversational Concierge

[2295] Step 1:

[2296] User: Enter questions about drug interactions and side effects.

[2297] Input: The question from the user.

[2298] What happens: The user types a question into an input field in the app.

[2299] Step 2:

[2300] Terminal: The generation AI analyzes the input question and generates an appropriate answer.

[2301] Input: The question from the user.

[2302] Specific operation: The generative AI performs natural language processing and analyzes the question content.

[2303] Output: Correct answer.

[2304] Step 3:

[2305] Terminal: Displays the generated answer to the user.

[2306] Input: Answer data from the generation AI.

[2307] What it does: Formats the response data for display to the user.

[2308] Output: The answer displayed on the user's terminal.

[2309] Suggested dosing schedule

[2310] Step 1:

[2311] User: Enter your daily routine.

[2312] Input: User's daily rhythm information (e.g., breakfast, lunch, and dinner times).

[2313] Specific operation: The user inputs information about their daily rhythm.

[2314] Step 2:

[2315] Device: Based on the inputted information on daily rhythms, the AI ​​generates the optimal medication schedule.

[2316] Input: User's lifestyle information.

[2317] Specific operation: The generating AI performs calculations based on the given life rhythm.

[2318] Output: Optimal dosing schedule.

[2319] Step 3:

[2320] Terminal: Notifies the user of the calculated medication schedule.

[2321] Input: Calculated dosing schedule.

[2322] Specific operation: Notification information is sent to the user's device and displayed.

[2323] Output: Medication schedule notification displayed on the user's device.

[2324] Side effect reporting and feedback

[2325] Step 1:

[2326] User: Open the side effect report screen and enter the symptoms.

[2327] Input: The side effect symptoms experienced by the user.

[2328] Specific action: The user enters their symptoms into the input field.

[2329] Step 2:

[2330] Terminal: Sends side effect information to the server.

[2331] Input: The side effect information entered.

[2332] Specific operation: The device sends side effect information to the server.

[2333] Output: Data packets sent to the server.

[2334] Step 3:

[2335] Server: Analyzes the input information and generates appropriate countermeasures.

[2336] Input: Side effect information.

[2337] Specific operation: The generative AI analyzes side effect information and considers appropriate countermeasures.

[2338] Output: Appropriate action.

[2339] Step 4:

[2340] Terminal: Notify the user of the generated solution.

[2341] Input: The generated solution.

[2342] Specific action: Format and send to inform the user of the corrective action.

[2343] Output: Actions displayed on the user's terminal.

[2344] Step 5:

[2345] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[2346] Input: Side effect information.

[2347] Specific operation: The server stores side effect information and periodically creates and sends reports to relevant institutions.

[2348] Output: Side effect information stored in the database and feedback reports.

[2349] Remaining quantity management and automatic replenishment

[2350] Step 1:

[2351] User: Enter the remaining amount of medication.

[2352] Input: remaining amount of medication.

[2353] Specific behavior: The user enters the remaining amount of medication into the input field.

[2354] Step 2:

[2355] Terminal: Sends the entered remaining amount information to the server.

[2356] Input: Remaining medication information.

[2357] Specific operation: The device sends remaining battery information to the server.

[2358] Output: Data packets sent to the server.

[2359] Step 3:

[2360] Server: If the remaining amount falls below a threshold, an automatic replenishment suggestion is made.

[2361] Input: Remaining information.

[2362] What happens: The server checks the remaining amount and generates replenishment suggestions as needed.

[2363] Output: Automatic replenishment suggestion notification.

[2364] Step 4:

[2365] Terminal: If the user accepts automatic ordering, places an order with the online store.

[2366] Enter: Automatic Purchase Order Acceptance.

[2367] Specific action: The user presses the accept button.

[2368] Output: An order request to the online store.

[2369] Step 5:

[2370] Server: Completes the order with the online store.

[2371] Input: Order information accepted by the user.

[2372] Specific operation: The server sends an order request to the online store.

[2373] Output: Order data sent to the online store.

[2374] (Application example 1)

[2375] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2376] In conventional drug management systems, managing drug interactions and side effects is done manually, which is not only inefficient but also increases the risk of misuse. Furthermore, managing security devices requires checking their status and configuring them individually, which is extremely time-consuming. To solve these problems, there is a growing need for a centralized management system that utilizes generative AI.

[2377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2378] In this invention, the server includes means for evaluating drug interactions using a generating AI, means for proposing a dosing schedule based on the user's lifestyle, means for the user to input side effect information, means for evaluating the status of security devices and proposing optimal settings and operation schedules, and means for analyzing security-related problem reports and generating and notifying appropriate countermeasures. This enables drug management and security device management to be performed in a unified manner, enabling efficient and safe operation.

[2379] "Generative AI" uses artificial intelligence techniques such as natural language processing to generate appropriate answers to questions from users.

[2380] The "means for evaluating drug interactions" is a function that analyzes the effects of taking multiple drugs simultaneously and instructs the user on the appropriate method of taking them.

[2381] The "means for suggesting a medication schedule" is a function that calculates the optimal timing for taking medication based on the user's lifestyle and notifies the user.

[2382] "Means for inputting side effect information" refers to a data input interface that allows a user to report side effects of a drug.

[2383] The "means for storing in a database" refers to a system that safely stores the entered side effect information and manages it in a manner that allows it to be retrieved as needed.

[2384] "Means of providing feedback to relevant organizations" refers to a function that periodically sends accumulated side effect information to relevant organizations such as medical institutions.

[2385] "Means for managing remaining medication and automatically suggesting replenishment" refers to a function that monitors the remaining amount of medication possessed by the user and automatically suggests replenishment when it falls below a certain threshold.

[2386] "A means for evaluating the status of security equipment and proposing optimal settings and operation schedules" is a means for analyzing the operating status of security equipment such as surveillance cameras and sensors, and proposing optimal settings and schedules to users.

[2387] "Means for analyzing security-related problem reports and generating and notifying appropriate countermeasures" refers to a system that analyzes security device problems reported by users, generates appropriate countermeasures, and notifies the users.

[2388] The "means for managing automatic replenishment" is a function for monitoring the maintenance status of security equipment and the remaining amount of consumables, and for timely replenishment or replacement.

[2389] The system for implementing this invention uses generative AI to manage drug interactions, drug schedules, side effects, monitor the status of security devices, and automatically replenish medications. The system operates by exchanging information between a server, terminals, and users.

[2390] User registration and initial settings

[2391] User: When the application is first launched, a user registration screen appears, where the user enters personal information (such as name, age, and gender), a list of medications currently being taken, side effect history, and security device information.

[2392] Terminal: Sends the entered information to the server. The user's personal information, medication information, and security device information are stored in the server's database.

[2393] Conversational Concierge

[2394] User: Enter questions about drug interactions and security device settings.

[2395] Terminal: The generation AI analyzes the input question and generates an appropriate answer, which is then displayed to the user.

[2396] For example, in response to the question, "My surveillance camera isn't recording. What should I do?", the AI ​​generator will answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[2397] Schedule suggestions

[2398] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times) and security device usage schedule.

[2399] Terminal: Based on the input information, the generation AI calculates the optimal medication schedule and security device operation schedule and notifies the user.

[2400] Server: Stores schedule information in a database and sends reminders to users when necessary.

[2401] For example, we suggest a schedule such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[2402] Reporting and Feedback on Side Effects and Problems

[2403] Users: If they experience a side effect from a medication or a problem with a security device, they can open a dedicated reporting screen and enter details of the symptoms or problem.

[2404] Device: Sends reported information to the server. The generated AI analyzes the input information and notifies the user of the appropriate countermeasures.

[2405] Server: The reported information is stored in a database and periodically provided to relevant authorities and security companies.

[2406] For example, if you report that "the sensor at the front door is not responding," the generating AI will notify you of the corrective action, such as "check the sensor battery and replace it if necessary," and this information will be regularly fed back to the relevant authorities.

[2407] Remaining quantity management and automatic replenishment

[2408] User: Enters remaining amounts and status of medications and security devices.

[2409] Device: Sends the entered remaining amount information to the server. If the remaining amount falls below the threshold, the server automatically suggests refilling.

[2410] Server: Automatically orders consumables and replacement parts as needed.

[2411] Example: "The sensor battery is low. Would you like to order a new battery automatically?" If the user agrees, the server will automatically complete the order with the online store.

[2412] Hardware and Software Used

[2413] Hardware: Smartphones, internet connections, security equipment (surveillance cameras, sensors, etc.)

[2414] software:

[2415] Programming language: Python

[2416] API: REST API for exchanging data related to the security system

[2417] Server: Security database on the cloud (e.g. Amazon AWS, Google Cloud)

[2418] Generative AI models: Natural Language Processing (NLP) models for generating answers to user questions (e.g., OpenAI GPT-4)

[2419] Add specific examples

[2420] Example prompts to input to the generative AI model

[2421] Question: "My security cameras aren't recording, what should I do?"

[2422] Prompt: "What should I do if my security camera isn't recording? Please list possible causes and solutions that apply to your answer."

[2423] My security camera isn't recording, what should I do?

[2424] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2425] Step 1:

[2426] (User registration and initial settings)

[2427] Input: The user enters personal information, medications, and security device information into the app.

[2428] Operation: The terminal sends the entered information to the server, which receives the information and stores it in a database.

[2429] Output: The user's personal information, medication information, and security device information are saved in the database.

[2430] Step 2:

[2431] (Interactive concierge function)

[2432] Input: The user inputs questions about drug interactions and security device settings.

[2433] How it works: The device sends a question to the generative AI model, which analyzes the question and generates an appropriate answer.

[2434] Output: The answer from the generative AI model is sent to the device and displayed to the user.

[2435] Specific operation: In response to the question, "My surveillance camera is not recording. What should I do?", the generative AI model generates the answer, "You may be running low on free space for recording. Please check your storage and delete unnecessary data."

[2436] Step 3:

[2437] (Schedule proposal)

[2438] Input: The user inputs their daily routine and schedule for using security devices.

[2439] Operation: The device sends the input information to the server, which uses the generative AI model to calculate the optimal medication schedule and security device operation schedule.

[2440] Output: The generated schedule is notified to the user.

[2441] Specific operation: Based on the user's daily rhythm, the generating AI will make suggestions such as "Turn on the sensor at 7am every day and set the surveillance camera to enhanced mode at 8pm."

[2442] Step 4:

[2443] (Reporting and Feedback on Side Effects and Problems)

[2444] Input: To report side effects or problems with security devices, users enter details of symptoms or problems into a dedicated reporting screen.

[2445] Operation: The device sends the report information to the server, which uses the generative AI model to analyze the report information and generate appropriate countermeasures.

[2446] Output: The generated solutions are notified to the user. The reported information is also saved in a database and periodically fed back to the relevant organizations.

[2447] Specific operation: When you report that "the front door sensor is not responding," the generating AI will generate and notify you of the solution, such as "check the sensor battery and replace it if necessary."

[2448] Step 5:

[2449] (Remaining amount management and automatic replenishment)

[2450] Input: The user inputs the remaining amount and status of medicines and security devices.

[2451] Operation: The device sends the input information to the server. The server references the database and suggests automatic replenishment if the remaining amount falls below a threshold. If necessary, it automatically places an order with the online store.

[2452] Output: The user is notified of the replenishment proposal, and after user approval, automatic ordering is completed.

[2453] Specific operation: The user is notified, "The sensor battery is low. Would you like to automatically order a new battery?" If the user agrees, the server will automatically complete the order.

[2454] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2455] The following is a detailed description of the mode for carrying out this invention. This system uses generative AI to manage drug interactions, dosage schedules, and side effects, and automatically replenish remaining amounts. In addition, by combining it with an emotion engine, it can also respond to the user's emotional state.

[2456] User registration and initial drug information settings

[2457] User: When the app is first launched, the user is presented with a registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, any side effects they have experienced, and their emotional state.

[2458] Terminal: Sends the entered information to the server. The user's personal information and medication information are stored in the server's database.

[2459] Example: When Tanaka starts the app for the first time, she answers questions about her name, age, medications she is taking (drug A, drug B), past side effects (skin rash), and initial emotional state. This allows the app to suggest the optimal medication schedule and emotional advice for Tanaka.

[2460] Conversational Concierge

[2461] User: Enter questions about drug interactions and side effects.

[2462] Terminal: Sends the question content to the generation AI and emotion engine.

[2463] Server: The generation AI analyzes the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The answer is then displayed to the user.

[2464] Example: When Tanaka asks, "Is it okay to take medicine A and medicine B at the same time?" the generative AI responds, "Taking medicine A and medicine B at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two." The emotion engine recognizes Tanaka's worried expression and advises, "If you are worried, we recommend that you consult a doctor."

[2465] Suggested dosing schedule

[2466] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[2467] Terminal: Sends the inputted life rhythm to the server.

[2468] Server: Based on lifestyle and medication information, the generative AI and emotion engine calculate the optimal medication schedule.

[2469] Example: Based on Tanaka's daily rhythm, a schedule is suggested such as "Take medicine A at 8:00 AM and medicine B at 8:30 AM." If Tanaka looks busy, the emotion engine will make a flexible suggestion such as "If these times are difficult, it is also possible to shift the times of each dose by one hour."

[2470] Side effect reporting and feedback

[2471] User: If you experience any side effects, open the side effect report screen and enter your symptoms.

[2472] Terminal: Sends side effect information to the server. The generation AI analyzes the input information, and the emotion engine evaluates the user's emotional state and notifies the user of the appropriate course of action.

[2473] Server: Stores reported adverse reaction information in a database and periodically provides feedback to relevant organizations.

[2474] Example: If Tanaka reports feeling nauseous after taking drug B, the generative AI will advise him to "drink water and rest," and the emotion engine will recognize Tanaka's anxious feelings and send an additional message saying, "We will inform you of different measures the next time you take the drug."

[2475] Remaining quantity management and automatic replenishment

[2476] User: Enter remaining amount of medicine or supplement.

[2477] Terminal: Sends the entered remaining amount information to the server.

[2478] Server: Saves remaining information in a database. The emotion engine also references this information.

[2479] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[2480] Terminal: Display a restock notification to the user.

[2481] User: Approves replenishment.

[2482] Server: After receiving approval, the order is automatically placed with the online store.

[2483] Example: When Tanaka inputs that he is running low on medicine A, a notification is sent saying, "Your remaining medicine A is low. Would you like us to automatically place an order for you?" Tanaka accepts, and the server automatically completes the order. Furthermore, if the emotion engine recognizes that Tanaka is stressed, it displays the message, "The order has been completed. Please wait a moment. Thank you for your hard work."

[2484] This system allows users to manage multiple medication schedules, deal with side effects, and manage remaining medication amounts in a unified manner, according to their emotional state, allowing them to maximize the effectiveness of their medications.

[2485] The processing flow will be explained below.

[2486] User registration and initial drug information settings

[2487] Step 1:

[2488] User: Launches the app for the first time.

[2489] Step 2:

[2490] Terminal: Display the user registration screen.

[2491] Step 3:

[2492] User: Answers initial setup questions to identify personal information (such as name, age, and gender), a list of medications taken, side effects, and emotional state.

[2493] Step 4:

[2494] Terminal: Sends the entered information to the server.

[2495] Step 5:

[2496] Server: Stores the received personal information and medication information in a database.

[2497] Conversational Concierge

[2498] Step 1:

[2499] User: Enter questions about drug interactions and side effects.

[2500] Step 2:

[2501] Terminal: Sends the question content to the generation AI and emotion engine.

[2502] Step 3:

[2503] Server: The generation AI analyzes the question content, and the emotion engine evaluates the user's emotional state.

[2504] Step 4:

[2505] Server: The generation AI generates an appropriate answer based on the analysis results.

[2506] Step 5:

[2507] Server: The emotion engine generates supplementary advice according to the emotional state.

[2508] Step 6:

[2509] Server: Sends the generated answers and supplementary advice to the terminal.

[2510] Step 7:

[2511] Terminal: The answer and supplementary advice are displayed to the user.

[2512] Suggested dosing schedule

[2513] Step 1:

[2514] User: Enter your daily routine (e.g., breakfast, lunch, and dinner times).

[2515] Step 2:

[2516] Terminal: Sends the inputted life rhythm to the server.

[2517] Step 3:

[2518] Server: Generative AI calculates the optimal dosing schedule based on lifestyle rhythm information and medication information.

[2519] Step 4:

[2520] Server: The emotion engine evaluates the user's emotional state and checks whether the schedule places a burden on the user.

[2521] Step 5:

[2522] Server: Sends the calculated dosing schedule to the terminal.

[2523] Step 6:

[2524] Terminal: Notifies the user of their medication schedule.

[2525] Side effect reporting and feedback

[2526] Step 1:

[2527] User: If you experience any side effects, open the side effect report screen.

[2528] Step 2:

[2529] Device: Displays a screen for selecting the type of side effect and symptoms.

[2530] Step 3:

[2531] User: Enter specific symptoms and submit report.

[2532] Step 4:

[2533] Terminal: Sends the entered side effect information to the server.

[2534] Step 5:

[2535] Server: Stores the received side effect information in a database.

[2536] Step 6:

[2537] Server: The generation AI analyzes side effect information and generates appropriate countermeasures.

[2538] Step 7:

[2539] Server: The emotion engine evaluates the user's emotional state and adds additional information to the response.

[2540] Step 8:

[2541] Server: Sends the generated solution and additional information to the terminal.

[2542] Step 9:

[2543] Terminal: Display solutions and additional information to the user.

[2544] Step 10:

[2545] Server: Stores accumulated side effect information in a database and periodically provides feedback to relevant institutions.

[2546] Remaining quantity management and automatic replenishment

[2547] Step 1:

[2548] User: Enter remaining amount of medicine or supplement.

[2549] Step 2:

[2550] Terminal: Sends the entered remaining amount information to the server.

[2551] Step 3:

[2552] Server: Saves remaining information in a database.

[2553] Step 4:

[2554] Server: Generates a replenishment notification when the remaining amount falls below a set threshold.

[2555] Step 5:

[2556] Server: The emotion engine evaluates the user's emotional state and predicts their reaction to the notification.

[2557] Step 6:

[2558] Server: Sends a notification message to the terminal.

[2559] Step 7:

[2560] Terminal: Display a restock notification to the user.

[2561] Step 8:

[2562] User: Approves replenishment.

[2563] Step 9:

[2564] Terminal: Sends authorization information to the server.

[2565] Step 10:

[2566] Server: After receiving approval for replenishment, the server automatically places an order with the online store.

[2567] In this way, a system is realized that performs integrated medication management while also taking into account the user's emotional state.

[2568] Example 2

[2569] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2570] Conventional medication management systems make it difficult for users to efficiently manage multiple medications, requiring a great deal of effort to manage drug interactions, dosage schedules, side effects, and remaining medication amounts. Furthermore, the system does not respond to the user's emotional state, which can increase anxiety and stress. To solve this problem, a comprehensive medication management system that combines a generative AI model and an emotion engine is needed.

[2571] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for transmitting personal information and drug information entered by the user to the server and storing them in a database; a means for transmitting the user's question to the generation AI model and emotion engine, analyzing the question, evaluating the user's emotional state, and generating an appropriate answer; a means for calculating an optimal drug schedule based on the user's lifestyle information; a means for the user to input side effect information; a means for analyzing the side effect information, evaluating the user's emotional state, and notifying the user of appropriate measures; a means for storing the side effect information in a database and periodically providing feedback to relevant institutions; and a means for managing the user's remaining medication, automatically suggesting replenishment, and placing an order. This allows the user to efficiently manage multiple medications and quickly take appropriate measures. Furthermore, the support provided by the emotion engine minimizes the user's anxiety and stress.

[2572] 1. "Server" refers to a computer system that manages, stores, and processes data over a network.

[2573] 2. "Database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[2574] 3. "Generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to generate appropriate outputs (e.g., answers, suggestions, countermeasures, etc.) from input information.

[2575] 4. "Emotion engine" refers to a computer program that analyzes a user's emotional state based on input, facial expressions, voice, etc., and determines an appropriate response.

[2576] 5. "User" refers to an individual who uses this system.

[2577] 6. "Personal information" refers to information that can identify a specific individual, such as name, age, and gender.

[2578] 7. "Medication information" refers to detailed information about the medications a user is taking, such as the name, dosage, and time of administration.

[2579] 8. "Side effect information" refers to information about undesirable symptoms or reactions that occur as a result of taking a drug.

[2580] 9. "Lifestyle information" refers to information such as meal and activity times in a user's daily life.

[2581] 10. "Dosing schedule" refers to the specific time and order in which a user takes medication.

[2582] 11. "Balance management" refers to the process of monitoring the current level of medications or supplements taken by a user and replenishing them appropriately.

[2583] 12. "Relevant organizations" refers to organizations involved in the management of drugs and the monitoring of side effects, such as medical institutions, pharmacies, and insurance companies.

[2584] The following is a detailed description of the embodiments of the present invention. This system uses a generative AI model and an emotion engine to manage drug interactions, dosage schedules, and side effects, automatically replenish remaining doses, and also responds to the user's emotional state. Hardware used includes a server, cloud storage, and user devices (smartphones, tablets, etc.). Software includes a generative AI, emotion engine, database management system, etc.

[2585] User registration and initial drug information settings

[2586] When a user launches the app, they are presented with a user registration screen where they answer initial setup questions to identify their personal information (such as name, age, and gender), a list of medications they are taking, side effects, and emotional state. The device then sends this information to the server, which stores it in a database.

[2587] Examples:

[2588] When a user launches the app for the first time, they answer some initial questions about their name, age, medications they are taking (drug X, drug Y), past side effects (rashes), and emotional state. The device sends this information to the server, which stores it in a database.

[2589] Conversational Concierge

[2590] When a user inputs a question about drug interactions or side effects, the device sends the question to the generation AI and emotion engine. The server then uses the generation AI to analyze the question, and the emotion engine evaluates the user's emotional state to generate an appropriate answer. The device then displays the generated answer to the user.

[2591] Examples:

[2592] When a user asks, "Is it okay to take medication X and medication Y at the same time?", the generative AI responds, "Taking medication X and medication Y at the same time may reduce their effectiveness. Please leave a gap of at least 30 minutes between the two," and the emotion engine recognizes the user's anxious expression and advises, "If you are worried, we recommend consulting a doctor." This is displayed to the user on the device.

[2593] Suggested dosing schedule

[2594] When a user inputs their daily rhythm (e.g., breakfast, lunch, and dinner times), the device sends this information to the server, which then uses generative AI and an emotion engine to calculate an optimal medication schedule and presents it to the user via the device.

[2595] Examples:

[2596] When a user inputs their daily routine, the system suggests a schedule, such as "Take medication X at 8:00 a.m. and medication Y at 8:30 a.m." If the user looks busy, the emotion engine suggests, "If these times are difficult, you can shift the times of each dose by one hour."

[2597] Side effect reporting and feedback

[2598] When a user experiences a side effect, they enter their symptoms on the side effect reporting screen, and the device sends the information to the server. The server then uses generative AI and an emotion engine to analyze the side effect information, generate appropriate countermeasures, and store them in a database. The appropriate countermeasures are notified to the user and periodically provided as feedback to relevant organizations.

[2599] Examples:

[2600] If a user reports feeling nauseous after taking drug Y, the generation AI will advise them to "drink water...

Claims

1. A means of evaluating drug interactions using generative AI, A means of suggesting a dosing schedule based on the user's lifestyle; a means for users to input side effect information; A means of storing side effect information in a database and periodically providing feedback to relevant institutions; A means to manage the user's remaining medication and automatically suggest refills, A system including:

2. The system of claim 1 , further comprising means for the generating AI to generate an appropriate answer to a user's question.

3. 10. The system of claim 1, further comprising means for generating an appropriate response to a user reporting an adverse drug reaction.

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