system

Abstract

We provide the system. [Solution] A means for receiving information from the user about the medications they are taking, A means for evaluating drug interactions and side effects of medications being taken, A means of generating and notifying users of advice based on evaluation results, A means for generating an optimal medication schedule based on the user's lifestyle, A means of notifying the user's device of the generated medication schedule and setting a reminder, A means of receiving and storing adverse event reports from users, A system that includes a means of notifying experts of adverse event information.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, many people take multiple medications simultaneously, but managing the combination of medications, side effects, and appropriate dosing times is very complicated. As a result, there are problems such as an increased risk of side effects due to forgetting to take medications or having too short intervals between taking medications, and the inability to maximize the effectiveness of the medications. There is a need for a system that solves this problem and supports the safe and effective use of medications.

Means for Solving the Problems

[0005] The present invention is a system that includes means for receiving information from a user about medications they are taking, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting reminders, means for receiving and storing reports of side effects from the user, and means for notifying experts of the side effect information. As a result, users can take their medications appropriately, taking into account drug interactions and timing, thereby reducing the risk of side effects and achieving effective drug therapy.

[0006] A "user" refers to an individual who uses the system to input medication information and receive advice and reminders.

[0007] "Information about medications currently being taken" includes data such as the name, dosage, and timing of administration of the medications the user is currently taking.

[0008] "Drug interactions" refers to information used to evaluate the risks of drug interactions and side effects when taking multiple medications simultaneously.

[0009] "Side effects" refer to undesirable physical reactions or symptoms that occur as a result of taking medication.

[0010] "Evaluation results" refer to diagnoses and recommendations obtained after analyzing the risks of drug interactions and side effects.

[0011] "Advice" refers to information, including recommendations and precautions, provided to users based on evaluation results.

[0012] "Lifestyle rhythm" refers to a pattern of time periods based on the user's usual daily activities and schedule.

[0013] A "medication schedule" refers to a specific time plan set up to ensure that the user takes their medication at the appropriate times.

[0014] "Reminder" refers to the function of notifying the user based on the dosing schedule.

[0015] "Side effect report" refers to the act of collecting information on side effects from the user via the app.

[0016] "Expert" refers to people with specialized medical knowledge such as pharmacists and doctors.

Brief Description of the Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] System Configuration

[0039] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system mainly consists of a server, a terminal, and a user, and each component works in cooperation with the others.

[0040] User medication information input

[0041] Users use a dedicated application to input information about the medications they are taking. They enter information such as the name of the medication, timing of administration, and dosage, and send this information to the server via the application. This allows each user's individual medication information to be stored in a database.

[0042] Evaluation of drug interactions and side effects

[0043] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[0044] Generating and notifying advice

[0045] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0046] Generation and notification of medication schedule

[0047] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[0048] Reporting and feedback on adverse events

[0049] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[0050] Specific example

[0051] 1. User medication information entry

[0052] The user opens the app and enters the following medication information:

[0053] Medication A: After breakfast

[0054] Medication B: After lunch

[0055] Medication C: After dinner

[0056] 2. Evaluation of drug interactions and side effects

[0057] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[0058] 3. Generating and notifying advice

[0059] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[0060] 4. Generation and notification of medication schedule

[0061] The server generates the next medication schedule, taking into account the user's daily routine:

[0062] Medicine A: 8:00

[0063] Medication B: 12:00

[0064] Medication C: 18:00

[0065] The device will periodically display reminders.

[0066] 5. Reporting and feedback on adverse events

[0067] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[0068] This will enable users to manage multiple medications safely and effectively.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[0072] Step 2:

[0073] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[0074] Step 3:

[0075] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[0076] Step 4:

[0077] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0078] Step 5:

[0079] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[0080] Step 6:

[0081] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[0082] Step 7:

[0083] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[0084] Step 8:

[0085] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[0086] Step 9:

[0087] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[0088] Step 10:

[0089] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[0090] Step 11:

[0091] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[0092] (Example 1)

[0093] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Traditional drug administration systems often fail to adequately assess the risks of drug interactions and side effects when multiple medications are taken, potentially posing significant health risks to users. Furthermore, generating optimal dosage schedules based on individual user lifestyles is difficult. Additionally, there is a lack of means for reporting side effects and for prompt notification of appropriate countermeasures by experts. As a result, it is difficult for users to take their medications safely and effectively.

[0095] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0096] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's information processing device of the generated medication schedule and setting reminders, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for receiving feedback from experts and notifying the user, means for evaluating the risks related to the medications being taken using an artificial intelligence algorithm, and means for incorporating new side effect information into the evaluation by referring to information in an expert database. As a result, the user can safely take multiple medications and minimize the risk of side effects.

[0097] A "user" is an individual who uses the system to input medication information, manage their medication use, and monitor for side effects.

[0098] "Information about medications currently being taken" refers to information including details such as the name, dosage, and timing of administration of the medications the user is currently taking.

[0099] "Drug interactions" refer to interactions that occur when multiple medications are taken simultaneously or within a short period of time, and can affect their safety and effectiveness.

[0100] A "side effect" is an undesirable physical or mental reaction that differs from the expected effect of taking a drug.

[0101] "Evaluation results" refer to data indicating the risk of drug interactions and side effects, calculated based on AI algorithms and database information.

[0102] "Advice" refers to specific instructions or warnings provided to the user based on the evaluation results.

[0103] "Lifestyle rhythm" refers to the user's daily activity times and habits, and is taken into consideration in order to optimize the medication schedule.

[0104] A "medication schedule" is a plan that indicates the optimal time and timing for a user to take their medication.

[0105] A "reminder" is an alarm or notification function set to inform the user of the time to take their medication.

[0106] "Reporting side effects" refers to the act of a user entering and submitting information about side effects they have experienced into the system.

[0107] A "specialist" is someone who possesses knowledge of pharmacology or medicine and is qualified to provide users with appropriate advice or prescription changes.

[0108] "Feedback" refers to responses and advice from experts to users, including appropriate measures to address reports of side effects.

[0109] "Artificial intelligence algorithms" refer to AI technology that operates as computer programs and is used for data analysis and risk assessment.

[0110] A "specialist database" is a data storage system that stores the knowledge and latest information on side effects held by experts, and uses it for evaluation.

[0111] An "information processing device" is a device such as a computer or smartphone used by a user, which provides a user interface including notification and reminder functions.

[0112] This invention relates to an interactive concierge system that receives information about medications a user is taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system consists of a server, a terminal, and a user.

[0113] User medication information input

[0114] Users use a dedicated application to enter information about the medications they are taking. This information includes the name of the medication, timing of administration, and dosage. For example, a user might enter the following:

[0115] Medication A: 1 tablet after breakfast

[0116] Medication B: 1 tablet after lunch

[0117] Medication C: 1 tablet after dinner

[0118] Once the user completes the input and presses the "Submit" button, the device sends the information to the server. The server stores the received medication information in its database.

[0119] Evaluation of drug interactions and side effects

[0120] The server compares the received drug information with drug interaction information in its database. Using a generative AI model, the server evaluates the risks of drug interactions and side effects from multiple medications. For example, if the server detects an interaction risk between "drug A" and "drug B," it saves the evaluation results to its internal database. The server periodically updates its expert database and incorporates new side effect information into its evaluation.

[0121] Generating and notifying advice

[0122] Based on the evaluation results, the server generates specific advice for the user. This advice includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is notified from the server to the user's terminal, and the user can check the notification in the application.

[0123] Example of a prompt:

[0124] "I take medication A at 8:00, medication B at 12:00, and medication C at 18:00. I've developed a headache. What should I do?"

[0125] Generation and notification of medication schedule

[0126] The server generates an optimal medication schedule, taking into account the user's daily routine and existing schedule. For example, it sets a medication schedule based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the user's device, and reminders are set. The device displays the set reminders in a timely manner to prompt the user to take their medication.

[0127] Specific example:

[0128] Medicine A: 8:00

[0129] Medication B: 12:00

[0130] Medication C: 18:00

[0131] Reporting and feedback on adverse events

[0132] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might report, "I developed a headache about two hours after taking medication C." The device sends this information to a server, which stores the received side effect information in a database. Experts review the report and provide the server with any necessary prescription changes or additional advice. The server then notifies the user's device of this advice.

[0133] Specific example:

[0134] The user reports within the app that they developed a headache about two hours after taking medication C, and the device sends this information to the server. The server notifies a specialist, who advises the user to "stop taking medication C and be prescribed a new medication." The server then notifies the user's device of this advice, which is displayed to the user.

[0135] This invention enables users to safely take multiple medications and effectively manage their medications while minimizing the risk of side effects. This is expected to further improve users' health management.

[0136] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0137] Step 1: "Enter and submit medication information"

[0138] The user opens a dedicated application. The user enters information about the medication they are currently taking. The input includes the name of the medication, the timing of administration, and the dosage. For example, the user enters "Medication A: After breakfast, 1 tablet" and presses the "Submit" button. The terminal formats the entered information and sends it to the server. The input data is "Medication A, after breakfast, 1 tablet," and the output is the transmission of data to the server. The server saves the received medication information to its database.

[0139] Step 2: "Evaluation of drug interactions and side effects"

[0140] The server compares the received drug information with the drug information in its database. The server then applies a generative AI model to evaluate the risks of drug interactions and side effects from multiple medications. For example, it might detect an interaction risk between drug A and drug B. The evaluation result would be "The interaction risk between drug A and drug B is high." The input is the drug information from the database and the received drug information, and the output is the interaction risk evaluation result. The server saves this evaluation result in its internal database.

[0141] Step 3: "Generating and notifying advice based on evaluation results"

[0142] The server uses the drug interaction and side effect risk assessment results to generate advice for the user. For example, it might generate specific instructions such as, "Take drug A and drug B with a 2-hour interval between doses." The generated advice is sent from the server to the user's device. The input is the assessment results, and the output is the generated advice. The device receives this notification and informs the user via push notification.

[0143] Step 4: "Generating and notifying you of the optimal dosage schedule"

[0144] The server generates an optimal medication schedule based on the user's daily routine and existing schedule. For example, if a user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the medication schedule will be set based on those times. This schedule is notified from the server to the user's terminal. The input is the user's daily routine information, and the output is the generated medication schedule. The terminal sets reminders based on this schedule and displays them in a timely manner.

[0145] Step 5: "Reporting side effects and notifying a professional"

[0146] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might input, "I developed a headache two hours after taking medication C," and submit it. The device formats the side effect information and sends it to the server. The server stores this information in a database and notifies a specialist. The input is the user's side effect report, and the output is a notification to a specialist. The specialist considers necessary countermeasures and sends them to the server as feedback. The server then notifies the user of this feedback.

[0147] Specific examples of operation:

[0148] The prompt message reads: "I took medication A at 8:00, medication B at 12:00, and medication C at 18:00. I have developed a headache. Please tell me what to do."

[0149] (Application Example 1)

[0150] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0151] With the increasing prevalence of autonomous vehicles, concerns are being raised about the impact of medication side effects on driving performance. In particular, for drivers taking multiple medications, there is a need for a system that can pre-evaluate how drug interactions and side effects might affect driving ability and suggest safe medication regimens. However, current systems are not effectively evaluating and notifying drivers, failing to adequately protect their health and safety.

[0152] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0153] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for suggesting a method of taking medication for the driver of an autonomous vehicle to drive safely and displaying a reminder before driving, and means for notifying the risk of side effects while driving. This makes it possible to maintain the driver's health and safety while driving by evaluating drug interactions and side effect risks of the medications the driver is taking in advance and suggesting a safe medication schedule.

[0154] A "user" refers to an individual who uses the system to provide information about the medications they are currently taking.

[0155] An "autonomous vehicle" refers to a vehicle that can operate with minimal driver intervention.

[0156] "Driver" refers to the individual using the self-driving vehicle.

[0157] "Medicine" refers to chemical or biological substances taken for medical purposes.

[0158] A "medication schedule" refers to a timeline set up to ensure that drivers can safely take their medication.

[0159] "Drug interactions" refer to the interactions that occur when taking multiple medications at the same time.

[0160] "Side effects" refer to undesirable effects caused by medication taken.

[0161] A "reminder" refers to a notification used to prompt a user to take a specific action.

[0162] A "specialist" refers to an individual who is well-versed in medicine and pharmacology and can provide appropriate advice to the driver.

[0163] "AI algorithms" refer to artificial intelligence computational methods used to evaluate the risks of drug interactions and side effects.

[0164] A "terminal" refers to an electronic device used by a user to access applications.

[0165] System Configuration

[0166] This system allows users to input information about the medications they are taking, performs a risk assessment based on that information, and provides advice and reminders to autonomous vehicle drivers to ensure safe medication use. The system mainly consists of a server, terminals, and users. The server is central to processing and analyzing the data.

[0167] User medication information input

[0168] Users use an application installed on their smartphones to input information about the medications they are taking. This information includes the name of the medication, when to take it, and the dosage. This information is sent to a server via the application. The server receives this information and stores it in a database.

[0169] Evaluation of drug interactions and side effects

[0170] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. The AI ​​algorithm used is TENSORFLOW®, and a machine learning model has been constructed. For example, if taking drug A and drug B simultaneously carries a risk of side effects, that risk is reflected in the evaluation results. Furthermore, the server refers to the latest information in expert databases and incorporates any new side effect information into the evaluation.

[0171] Generating and notifying advice

[0172] Based on the evaluation results, the server generates specific advice for the user. For example, it may include instructions such as, "Take medication A and medication B at least two hours apart." The generated advice is notified to the user's smartphone, and the user can check it through the application.

[0173] Generation and notification of medication schedule

[0174] The server generates an optimal dosage schedule, taking into account the user's daily rhythm and schedule. For example, if a user typically eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server sets appropriate dosage times. The generated schedule is notified to the smartphone, and a reminder is displayed as the dosage time approaches.

[0175] Uses in the operation of autonomous vehicles

[0176] Before using an autonomous vehicle, the system assesses the impact on driving based on information about the driver's medications and suggests appropriate medication regimens. For example, a reminder such as "Avoid taking medication B before driving" may be displayed. This allows the driver to avoid side effects while driving and drive safely.

[0177] Reporting and feedback on adverse events

[0178] If a user experiences side effects while driving, they can report detailed symptoms through the application. This information is immediately sent to the server and stored in the database. When a specialist is notified, they use this information to provide necessary prescription changes or additional advice. The server then notifies the user of this advice on their smartphone.

[0179] Specific example

[0180] User medication information input

[0181] The user opens the application on their smartphone and enters the following medication information:

[0182] Medication A: After breakfast

[0183] Medication B: After lunch

[0184] Medication C: After dinner

[0185] Evaluation of drug interactions and side effects

[0186] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of side effects between drug A and drug B. The latest information in the database is also included in the evaluation.

[0187] Generating and notifying advice

[0188] The server generates advice stating, "Take medication A and medication B with a two-hour interval between doses," and notifies the user's smartphone. The user then checks this within the app.

[0189] Generation and notification of medication schedule

[0190] The server generates the next medication schedule, taking into account the user's daily routine:

[0191] Medicine A: 8:00

[0192] Medication B: 12:00

[0193] Medication C: 18:00

[0194] Smartphones display reminders periodically.

[0195] Reminders for driving autonomous vehicles

[0196] If the driver starts driving at 12:30, the server will display a reminder at 12:20 stating, "Avoid taking medication B while driving."

[0197] Reporting of side effects

[0198] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The server notifies a specialist of this information, and the specialist instructs the server to "discontinue drug C and prescribe a new medication." The server then notifies the user of this instruction on their smartphone.

[0199] Examples of prompts to input into a generative AI model

[0200] Please provide information on any medications the user is taking before driving. Evaluate the risks of drug interactions and side effects for each medication and suggest a safe medication schedule for when the user is driving.

[0201] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0202] Step 1:

[0203] The user opens a smartphone application and enters information about the medications they are taking (medication name, timing of administration, dosage, etc.). The input data might include, for example, medication A (after breakfast), medication B (after lunch), and medication C (after dinner). The user enters this information into the application's form and presses the "Submit" button. This sends the medication information to the server.

[0204] Step 2:

[0205] The server retrieves medication information received from the user. The server uses a database management system (e.g., SQLAlchemy) to store this information in a database. Specifically, it stores input data in fields such as medication name, timing of administration, and dosage. This centralizes the management of each user's medication information in the database.

[0206] Step 3:

[0207] The server uses stored drug information and an AI algorithm (e.g., TensorFlow) to evaluate the risks of drug interactions and side effects. The input is drug information data, which the AI ​​algorithm analyzes and outputs a risk assessment result. Specifically, it calculates the risk of side effects when drug A and drug B are taken simultaneously and determines the risk level.

[0208] Step 4:

[0209] The server generates specific advice for the user based on the risk assessment results. This advice may include statements such as, "Take medication A and medication B with a two-hour interval between doses." The server generates this advice in text format and notifies the user's device.

[0210] Step 5:

[0211] The server generates an optimal medication schedule considering the user's daily routine and schedule information. The input is the user's daily routine (e.g., breakfast at 8:00 AM, lunch at 12:00 PM, dinner at 6:00 PM). The generated schedule will include medication A at 8:00 AM, medication B at 12:00 PM, and medication C at 6:00 PM. This schedule is then sent to the smartphone as a text message or reminder.

[0212] Step 6:

[0213] Before the driver uses the self-driving vehicle, the server sends a reminder to their smartphone, such as "Avoid taking medication B before driving." The reminder appears 10 minutes before driving (for example, at 12:20). This allows the driver to avoid side effects while driving.

[0214] Step 7:

[0215] If a user experiences side effects (such as a headache) after taking medication, they report the detailed symptoms within the app. This reported information is sent from the smartphone to a server. The server stores this information in a database and immediately notifies a specialist.

[0216] Step 8:

[0217] Experts review side effect information and provide prescription changes or additional advice as needed. Feedback from experts is sent to a server, which then notifies the user's smartphone. For example, if an expert advises discontinuing medication C and prescribing a new medication, the user will be notified.

[0218] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0219] System Configuration

[0220] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system is further optimized by incorporating an emotion engine that recognizes the user's emotions. The main components of the system are a server, a terminal, and the user, and each component works in cooperation with the others.

[0221] User medication information input

[0222] Users use a dedicated application to input information about the medications they are taking. By entering information such as the name of the medication, timing of administration, and dosage, and sending it to the server through the application, each user's individual medication information is stored in the database.

[0223] Evaluation of drug interactions and side effects

[0224] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[0225] Generating and notifying advice

[0226] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0227] Generation and notification of medication schedule

[0228] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[0229] Reporting and feedback on adverse events

[0230] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[0231] Embedding an emotion engine

[0232] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's voice and facial expressions to evaluate their current emotional state. For example, it can determine whether the user is stressed or relaxed based on their voice tone and facial expressions.

[0233] Adjusting advice based on emotional state

[0234] Based on the emotional state recognized by the emotion engine, the server adjusts the content of its advice. For example, if the user is feeling stressed, it will use kinder and more relaxing language. It will also suggest a chat function with an expert as needed, allowing the user to directly address the problems and questions they are currently experiencing.

[0235] Specific example

[0236] 1. User medication information entry

[0237] The user opens the app and enters the following medication information:

[0238] Medication A: After breakfast

[0239] Medication B: After lunch

[0240] Medication C: After dinner

[0241] 2. Evaluation of drug interactions and side effects

[0242] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[0243] 3. Generating and notifying advice

[0244] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[0245] 4. Generation and notification of medication schedule

[0246] The server generates the next medication schedule, taking into account the user's daily routine:

[0247] Medicine A: 8:00

[0248] Medication B: 12:00

[0249] Medication C: 18:00

[0250] The device will periodically display reminders.

[0251] 5. Reporting and feedback on adverse events

[0252] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[0253] 6. Example of how the emotion engine works

[0254] When a user submits data entered into the app, an emotion engine analyzes their voice tone and facial expressions. If the server determines that the user is stressed, it sends a notification in gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function where users can consult directly with a specialist if needed, supporting them in taking their medication with peace of mind.

[0255] In this way, by incorporating an emotion engine, a system that can improve the user experience can be realized.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[0259] Step 2:

[0260] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[0261] Step 3:

[0262] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[0263] Step 4:

[0264] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0265] Step 5:

[0266] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[0267] Step 6:

[0268] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[0269] Step 7:

[0270] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[0271] Step 8:

[0272] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[0273] Step 9:

[0274] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[0275] Step 10:

[0276] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[0277] Step 11:

[0278] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[0279] Step 12:

[0280] An emotion engine, which recognizes the user's emotions, analyzes the user's voice and facial expressions. The emotion engine evaluates the user's emotional state (e.g., stress or anxiety) and sends that information to the server.

[0281] Step 13:

[0282] The server adjusts the advice based on the emotional state received from the emotion engine. For example, if the user is feeling stressed, it will use kinder and more relaxing language.

[0283] Step 14:

[0284] The server notifies the terminal of the advice reflecting the emotional state. The terminal receives the advice and displays it to the user.

[0285] Step 15:

[0286] The emotion engine evaluates the user's emotional state. When stress or anxiety is high, the server sends a notification to the terminal proposing a chat function with an expert.

[0287] Step 16:

[0288] The terminal displays the proposal of the chat function with an expert to the user so that the user can start the chat.

[0289] (Example 2)

[0290] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0291] In users taking multiple medications, it is required to accurately evaluate the risks of interactions and side effects due to the combination of medications and provide appropriate advice and dosing schedules. There is also a need for a system that can quickly respond to the side effects and stress felt by the user and provide individually optimized advice. However, it has been difficult for conventional systems to fully meet these needs.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0293] In this invention, the server includes means for receiving information from the user about the medications they are taking, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for recognizing the user's emotions and adjusting advice based on their emotional state, and means for suggesting a chat function with experts according to the emotional state. This not only optimizes the user's medication management but also enables personalized support that takes their emotional state into consideration.

[0294] A "user" is an individual who uses the system to input information about medication use and side effects, and to receive advice and notifications.

[0295] A "server" is a central processing unit that receives data sent from users, stores it in a database, and performs tasks such as evaluating drug pairings, generating advice, and creating schedules.

[0296] A "terminal" is a device that a user directly operates, an electronic device used by the user to input information or receive notifications from a server.

[0297] An "emotion engine" is a software component that analyzes a user's voice and facial expressions to evaluate their current emotional state.

[0298] A "specialist" is a professional who possesses advanced knowledge about the medications and side effects that a user is taking, and who can provide appropriate advice and feedback.

[0299] A "reminder" is a feature used to notify users of specific times or tasks.

[0300] The "AI algorithm" refers to a machine learning model or other advanced computational methods used to analyze users' drug information and evaluate the risks of drug interactions and side effects.

[0301] "Drug interaction" refers to the phenomenon where multiple drugs taken simultaneously affect each other.

[0302] "Side effect" refers to an undesirable physical or mental reaction that occurs as a result of taking a drug.

[0303] The "chat function" refers to a communication function that allows users and experts to interact in real time.

[0304] "Database" refers to an information management system for systematically storing and managing data such as users' drug information, side effect information, and emotional states.

[0305] "Daily rhythm" refers to the temporal patterns such as activity times and meal timings in users' daily lives. <​​​​​​​​​​​​​​​​​As a concrete example, the user enters the following:

[0311] Medication A: After breakfast

[0312] Medication B: After lunch

[0313] Medication C: After dinner

[0314] Drug information transmission and storage

[0315] The terminal sends user input information to the server, and the server stores the received information in a database. During this process, validation is also performed to check the accuracy and consistency of the data.

[0316] Evaluation of drug interactions and side effects

[0317] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information stored in a database. For example, if drug A and drug B may interact when taken simultaneously, that risk is reflected in the evaluation results. New side effect information is obtained from expert databases and added to the evaluation.

[0318] In a specific example, the server evaluates drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B.

[0319] Generating and notifying advice

[0320] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0321] Generation and notification of medication schedule

[0322] The server generates an optimal dosage schedule based on the user's daily routine and schedule information. For example, based on information that the user usually eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the following dosage schedule is generated:

[0323] Medicine A: 8:00

[0324] Medication B: 12:00

[0325] Medication C: 18:00

[0326] The generated schedule will be notified to the device, and a reminder will be set.

[0327] Reporting and feedback on adverse events

[0328] If a user experiences side effects after taking medication, they report the symptoms within the app. This information is sent via the device to a server, which stores it in a database. The server then notifies a specialist of this information and awaits their feedback.

[0329] For example, if a user reports that they developed a headache about two hours after taking drug C, the server will receive instructions from a specialist to "stop taking drug C and prescribe a new drug," and the server will then notify the user's device of these instructions.

[0330] How the emotion engine works

[0331] The device passes data sent when a user uses the app to the emotion engine. The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state, determining whether the user is stressed or relaxed.

[0332] Adjusting advice based on emotional state

[0333] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it will use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function with experts to directly address the user's problems and questions.

[0334] Example of a prompt

[0335] By inputting prompts like the following into the AI ​​model, it can provide advice in a natural conversational format:

[0336] "The user is currently taking the following medications: Medication A (after breakfast), Medication B (after lunch), and Medication C (after dinner). Medications A and B have a risk of interaction and should be taken two hours apart. Please propose an optimal medication schedule that takes the user's lifestyle into consideration. Also, if the user is experiencing stress, please use calming language."

[0337] Using such detailed prompts allows the generative AI model to provide users with appropriate and helpful advice.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] System program processing flow

[0340] Step 1: User enters medication information

[0341] The user opens a dedicated application and enters information about the medication they are taking (such as the name of the medication, timing of administration, and dosage).

[0342] Input: Information such as the name of the drug, timing of administration, and dosage.

[0343] Output: The entered medication information is saved to the terminal.

[0344] Step 2: Send and store medication information

[0345] The terminal sends user input information to the server. The server stores the received information in a database.

[0346] Input: Medication information entered by the user

[0347] Data processing: Data validation and duplicate checking

[0348] Output: Validated drug information is saved to the database.

[0349] Step 3: Evaluation of drug interactions and side effects

[0350] The server uses an AI algorithm to evaluate drug interactions and side effect risks based on drug information stored in a database. It also obtains the latest side effect information from expert databases and incorporates it into the evaluation.

[0351] Input: Drug information stored in the database, side effect information from expert databases.

[0352] Data processing: Risk assessment using AI algorithms

[0353] Output: Drug interaction risk and side effect risk assessment results

[0354] Step 4: Generate and notify advice

[0355] The server generates advice for the user based on the evaluation results. This includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is then notified to the user's device.

[0356] Input: Evaluation results of drug interactions and side effects

[0357] Data processing: Generating advice using AI models.

[0358] Output: The generated advice is notified to the device.

[0359] Step 5: Generate and notify about the medication schedule.

[0360] The server generates an optimal medication schedule based on the user's lifestyle and schedule information. The generated schedule is notified to the user's device, and reminders are set.

[0361] Input: User's daily routine information (breakfast, lunch, dinner times, etc.)

[0362] Data processing: Generating medication schedules

[0363] Output: The generated medication schedule is notified to the device, and a reminder is set.

[0364] Step 6: Reporting and providing feedback on side effects

[0365] If a user experiences side effects after taking medication, they report the symptoms within the app. This report is sent via the device to a server, which stores it in a database and simultaneously notifies a specialist. The specialist's feedback is then provided to the server and notified to the user's device.

[0366] Input: Detailed information on side effects reported by the user

[0367] Data processing: Storage of adverse event information and notification to experts.

[0368] Output: Expert feedback is sent to the user's device via the server.

[0369] Step 7: Operating the Emotion Engine

[0370] When a user uses the app, the emotion engine analyzes their voice tone and facial expressions to assess their current emotional state.

[0371] Input: User's voice tone and facial expression data

[0372] Data processing: Analysis of emotional states using an emotion engine.

[0373] Output: Emotional state evaluation results

[0374] Step 8: Adjusting advice based on emotional state

[0375] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, it might use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It can also provide a chat function with experts.

[0376] Input: Emotional state evaluation result

[0377] Data processing: Adjusting advice from generative AI models

[0378] Output: Adjusted advice is sent to the device.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0381] In modern society, it is crucial for users to manage the proper use of medication and the safe consumption of food to improve their quality of life. However, technology that provides users with optimal advice considering their individual physical condition, emotional state, allergy information, and lifestyle is not yet sufficiently developed. Furthermore, the lack of support that takes into account the user's emotional state can lead to stress and anxiety. This invention is designed to solve these problems.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for receiving information from the user about medications or foods being taken; means for evaluating combinations of medications or foods being taken, as well as side effects and nutritional balance; means for generating and notifying the user of advice based on the evaluation results; means for generating an optimal medication or meal schedule based on the user's lifestyle; means for notifying the user's terminal of the generated medication or meal schedule and setting a reminder; means for receiving and storing reports of side effects or feedback from the user; means for notifying experts of the side effect information or feedback; means for evaluating the user's emotional state using an emotion engine; and means for adjusting the content of advice based on the evaluated emotional state. This enables the user to receive safe and appropriate advice based on information about medication and food intake, and to provide flexible support according to their emotional state.

[0384] A "user" refers to someone who uses this system to input information such as medications they are taking, foods they eat, their lifestyle, and their emotional state, and then receives evaluation results and advice.

[0385] "Medications currently being taken" refers to any medications or supplements that the user is currently using, including information that is entered into the system.

[0386] "Ingredients" refers to the food and ingredients used in dishes that the user is considering consuming, and includes allergy information and nutritional balance.

[0387] "Lifestyle rhythm" refers to the timing and patterns of a user's daily activities, including meal times, sleep times, and medication times.

[0388] "Emotional state" refers to the user's current psychological feelings and mood, and is evaluated based on factors such as voice tone and facial expressions.

[0389] "Evaluation" refers to the process by which the system analyzes and diagnoses drug and food combinations, side effects, nutritional balance, etc., based on information received from the user.

[0390] "Advice" refers to specific instructions and suggestions provided to the user based on the evaluation results, including things like the timing of taking medication or eating.

[0391] A "schedule" refers to the time and order that the system has determined to be optimal for the user to take medication or eat meals, and it is provided along with reminders.

[0392] A "reminder" is a mechanism that notifies the user of medication or meal times, and is set by the system.

[0393] "Feedback" refers to information that users report to the system regarding side effects or impressions they experienced after taking medication or eating.

[0394] "Experts" refer to medical and nutritional professionals who provide additional advice or prescription changes based on user information and feedback regarding side effects.

[0395] An "emotion engine" refers to an algorithm or software that analyzes a user's voice, facial expressions, etc., to evaluate their current emotional state.

[0396] "Adjusting" means modifying and adapting the advice given to the user based on their evaluated emotional state.

[0397] System Configuration

[0398] To implement the present invention, a system comprising the following components is required. The system consists of a server, a terminal, and a user, each working in cooperation with the others.

[0399] Program generation

[0400] The system's main function is to receive information from users about the medications and foods they are currently taking, and then generate and notify them of advice and schedules based on that information. By using an emotion engine, it is possible to provide support tailored to the user's emotional state.

[0401] Hardware to use

[0402] Smartphones: The primary devices on which user interfaces and emotion engines operate.

[0403] Server: Responsible for user data processing, advice generation, and database management.

[0404] Software to use

[0405] AI framework: TensorFlow (for building AI algorithms)

[0406] Database: MySQL (registered trademark) (for managing user information and ingredient data)

[0407] Emotion recognition engine: Amazon Rekognition (face recognition) and Google® Cloud Speech-to-Text (speech recognition)

[0408] Data processing and data calculation

[0409] 1. User Information Input: Users input information about medications they are taking, foods they eat, their lifestyle, and their emotional state through a dedicated app. This information is sent from the device to the server and stored in a database.

[0410] 2. Evaluation and Advice Generation: Based on the received information, the server uses an AI algorithm to evaluate drug and food combinations, side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[0411] 3. Schedule Generation: The server considers the user's daily routine and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[0412] 4. Feedback Management: When a user enters feedback after taking medication or eating, the device sends that information to the server and stores it in the database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[0413] 5. How the Emotion Engine Works: The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[0414] Specific example

[0415] 1. User enters medication information: The user opens the app and enters "Medication A after breakfast," "Medication B after lunch," and "Medication C after dinner." This information is sent to the server.

[0416] 2. Evaluation and advice generation: The server evaluates the information on drug A, drug B, and drug C and generates advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0417] 3. Schedule generation: The server takes the user's daily routine into consideration and generates and notifies the user of a medication schedule, such as taking medication A at 8:00, medication B at 12:00, and medication C at 18:00.

[0418] 4. Feedback Management: A user enters feedback such as "I experienced a headache after taking drug C," and this information is sent to the server. The server notifies a specialist, and the specialist's instructions are then re-notified to the user.

[0419] 5. Emotion Engine Operation: While the user is using the app, the emotion engine will operate and, for example, if it determines that the user is feeling stressed, it will notify the user with a message such as, "Please relax. We recommend taking medication B at 12:00."

[0420] Example of a prompt

[0421] "Since the user is feeling stressed, please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[0422] In this way, the overall system configuration and operation of the invention enable proper management support for medicines and food ingredients for users.

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

[0424] Step 1:

[0425] User input

[0426] Users input information about medications and foods they are currently taking, allergy information, lifestyle, and emotional state through a dedicated application. The entered information is sent from their smartphone to a server and stored in a database.

[0427] Specific actions: The user opens the app and enters information such as "Take medicine A after breakfast," "Take medicine B after lunch," and "Take medicine C after dinner." They also fill in detailed information about their daily routine, such as "I have an egg allergy" and "I usually eat breakfast at 8:00."

[0428] Step 2:

[0429] Evaluation and advice generation

[0430] Based on the received information, the server uses an AI algorithm (TensorFlow) to evaluate combinations of drugs and foods, potential side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[0431] Input: User's medications, food items, and allergy information retrieved from the database by the server.

[0432] Data processing: AI algorithms are used to assess the risk of drug interactions and food allergies.

[0433] Output: Specific advice based on the evaluation results (e.g., "Take drug A and drug B with a 2-hour interval between doses").

[0434] Step 3:

[0435] Schedule generation

[0436] The server takes the user's lifestyle into consideration and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[0437] Input: User's daily routine information (e.g., "Breakfast at 8:00," "Lunch at 12:00")

[0438] Data processing: Calculation of an optimal timetable based on daily routines.

[0439] Output: A schedule such as "Take medicine A at 8:00", "Take medicine B at 12:00", and "Take medicine C at 18:00".

[0440] Step 4:

[0441] Feedback Management

[0442] Users enter feedback after taking medication or eating. This information is sent from the device to the server and stored in a database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[0443] Input: User feedback (e.g., "I experienced a headache after taking drug C")

[0444] Data processing: Feedback information is forwarded to experts, and the user is notified of the experts' responses.

[0445] Output: Instructions from a specialist (e.g., "Discontinue taking drug C and prescribe a new drug")

[0446] Step 5:

[0447] How the emotion engine works

[0448] The emotion engine (Amazon Rekognition and Google Cloud Speech-to-Text) analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[0449] Input: User's voice and facial expression data

[0450] Data processing: Evaluation of emotional states using an emotion engine.

[0451] Output: Adjustment of advice based on emotional state (e.g., "Please relax. We recommend taking medication B at 12:00.")

[0452] Specific operation: While the user is using the app, the camera and microphone are active, and the emotion engine analyzes the user's stress level in real time. For example, if the server determines that the user is feeling stressed, it will send a notification in gentle language such as, "Why not try a relaxing herbal tea today?"

[0453] Example of a prompt

[0454] "The user is feeling stressed, so please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[0455] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0456] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0457] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0458] [Second Embodiment]

[0459] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0460] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0461] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0462] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0463] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0464] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0465] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0466] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0467] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0468] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0469] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0470] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0471] System Configuration

[0472] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system mainly consists of a server, a terminal, and a user, and each component works in cooperation with the others.

[0473] User medication information input

[0474] Users use a dedicated application to input information about the medications they are taking. They enter information such as the name of the medication, timing of administration, and dosage, and send this information to the server via the application. This allows each user's individual medication information to be stored in a database.

[0475] Evaluation of drug interactions and side effects

[0476] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[0477] Generating and notifying advice

[0478] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0479] Generation and notification of medication schedule

[0480] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[0481] Reporting and feedback on adverse events

[0482] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[0483] Specific example

[0484] 1. User medication information entry

[0485] The user opens the app and enters the following medication information:

[0486] Medication A: After breakfast

[0487] Medication B: After lunch

[0488] Medication C: After dinner

[0489] 2. Evaluation of drug interactions and side effects

[0490] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[0491] 3. Generating and notifying advice

[0492] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[0493] 4. Generation and notification of medication schedule

[0494] The server generates the next medication schedule, taking into account the user's daily routine:

[0495] Medicine A: 8:00

[0496] Medication B: 12:00

[0497] Medication C: 18:00

[0498] The device will periodically display reminders.

[0499] 5. Reporting and feedback on adverse events

[0500] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[0501] This will enable users to manage multiple medications safely and effectively.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[0505] Step 2:

[0506] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[0507] Step 3:

[0508] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[0509] Step 4:

[0510] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0511] Step 5:

[0512] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[0513] Step 6:

[0514] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[0515] Step 7:

[0516] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[0517] Step 8:

[0518] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[0519] Step 9:

[0520] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[0521] Step 10:

[0522] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[0523] Step 11:

[0524] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[0525] (Example 1)

[0526] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0527] Traditional drug administration systems often fail to adequately assess the risks of drug interactions and side effects when multiple medications are taken, potentially posing significant health risks to users. Furthermore, generating optimal dosage schedules based on individual user lifestyles is difficult. Additionally, there is a lack of means for reporting side effects and for prompt notification of appropriate countermeasures by experts. As a result, it is difficult for users to take their medications safely and effectively.

[0528] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0529] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's information processing device of the generated medication schedule and setting reminders, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for receiving feedback from experts and notifying the user, means for evaluating the risks related to the medications being taken using an artificial intelligence algorithm, and means for incorporating new side effect information into the evaluation by referring to information in an expert database. As a result, the user can safely take multiple medications and minimize the risk of side effects.

[0530] A "user" is an individual who uses the system to input medication information, manage their medication use, and monitor for side effects.

[0531] "Information about medications currently being taken" refers to information including details such as the name, dosage, and timing of administration of the medications the user is currently taking.

[0532] "Drug interactions" refer to interactions that occur when multiple medications are taken simultaneously or within a short period of time, and can affect their safety and effectiveness.

[0533] A "side effect" is an undesirable physical or mental reaction that differs from the expected effect of taking a drug.

[0534] "Evaluation results" refer to data indicating the risk of drug interactions and side effects, calculated based on AI algorithms and database information.

[0535] "Advice" refers to specific instructions or warnings provided to the user based on the evaluation results.

[0536] "Lifestyle rhythm" refers to the user's daily activity times and habits, and is taken into consideration in order to optimize the medication schedule.

[0537] A "medication schedule" is a plan that indicates the optimal time and timing for a user to take their medication.

[0538] A "reminder" is an alarm or notification function set to inform the user of the time to take their medication.

[0539] "Reporting side effects" refers to the act of a user entering and submitting information about side effects they have experienced into the system.

[0540] A "specialist" is someone who possesses knowledge of pharmacology or medicine and is qualified to provide users with appropriate advice or prescription changes.

[0541] "Feedback" refers to responses and advice from experts to users, including appropriate measures to address reports of side effects.

[0542] "Artificial intelligence algorithms" refer to AI technology that operates as computer programs and is used for data analysis and risk assessment.

[0543] A "specialist database" is a data storage system that stores the knowledge and latest information on side effects held by experts, and uses it for evaluation.

[0544] An "information processing device" is a device such as a computer or smartphone used by a user, which provides a user interface including notification and reminder functions.

[0545] This invention relates to an interactive concierge system that receives information about medications a user is taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system consists of a server, a terminal, and a user.

[0546] User medication information input

[0547] Users use a dedicated application to enter information about the medications they are taking. This information includes the name of the medication, timing of administration, and dosage. For example, a user might enter the following:

[0548] Medication A: 1 tablet after breakfast

[0549] Medication B: 1 tablet after lunch

[0550] Medication C: 1 tablet after dinner

[0551] Once the user completes the input and presses the "Submit" button, the device sends the information to the server. The server stores the received medication information in its database.

[0552] Evaluation of drug interactions and side effects

[0553] The server compares the received drug information with drug interaction information in its database. Using a generative AI model, the server evaluates the risks of drug interactions and side effects from multiple medications. For example, if the server detects an interaction risk between "drug A" and "drug B," it saves the evaluation results to its internal database. The server periodically updates its expert database and incorporates new side effect information into its evaluation.

[0554] Generating and notifying advice

[0555] Based on the evaluation results, the server generates specific advice for the user. This advice includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is notified from the server to the user's terminal, and the user can check the notification in the application.

[0556] Example of a prompt:

[0557] "I take medication A at 8:00, medication B at 12:00, and medication C at 18:00. I've developed a headache. What should I do?"

[0558] Generation and notification of medication schedule

[0559] The server generates an optimal medication schedule, taking into account the user's daily routine and existing schedule. For example, it sets a medication schedule based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the user's device, and reminders are set. The device displays the set reminders in a timely manner to prompt the user to take their medication.

[0560] Specific example:

[0561] Medicine A: 8:00

[0562] Medication B: 12:00

[0563] Medication C: 18:00

[0564] Reporting and feedback on adverse events

[0565] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might report, "I developed a headache about two hours after taking medication C." The device sends this information to a server, which stores the received side effect information in a database. Experts review the report and provide the server with any necessary prescription changes or additional advice. The server then notifies the user's device of this advice.

[0566] Specific example:

[0567] The user reports within the app that they developed a headache about two hours after taking medication C, and the device sends this information to the server. The server notifies a specialist, who advises the user to "stop taking medication C and be prescribed a new medication." The server then notifies the user's device of this advice, which is displayed to the user.

[0568] This invention enables users to safely take multiple medications and effectively manage their medications while minimizing the risk of side effects. This is expected to further improve users' health management.

[0569] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0570] Step 1: "Enter and submit medication information"

[0571] The user opens a dedicated application. The user enters information about the medication they are currently taking. The input includes the name of the medication, the timing of administration, and the dosage. For example, the user enters "Medication A: After breakfast, 1 tablet" and presses the "Submit" button. The terminal formats the entered information and sends it to the server. The input data is "Medication A, after breakfast, 1 tablet," and the output is the transmission of data to the server. The server saves the received medication information to its database.

[0572] Step 2: "Evaluation of drug interactions and side effects"

[0573] The server compares the received drug information with the drug information in its database. The server then applies a generative AI model to evaluate the risks of drug interactions and side effects from multiple medications. For example, it might detect an interaction risk between drug A and drug B. The evaluation result would be "The interaction risk between drug A and drug B is high." The input is the drug information from the database and the received drug information, and the output is the interaction risk evaluation result. The server saves this evaluation result in its internal database.

[0574] Step 3: "Generating and notifying advice based on evaluation results"

[0575] The server uses the drug interaction and side effect risk assessment results to generate advice for the user. For example, it might generate specific instructions such as, "Take drug A and drug B with a 2-hour interval between doses." The generated advice is sent from the server to the user's device. The input is the assessment results, and the output is the generated advice. The device receives this notification and informs the user via push notification.

[0576] Step 4: "Generating and notifying you of the optimal dosage schedule"

[0577] The server generates an optimal medication schedule based on the user's daily routine and existing schedule. For example, if a user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the medication schedule will be set based on those times. This schedule is notified from the server to the user's terminal. The input is the user's daily routine information, and the output is the generated medication schedule. The terminal sets reminders based on this schedule and displays them in a timely manner.

[0578] Step 5: "Reporting side effects and notifying a professional"

[0579] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might input, "I developed a headache two hours after taking medication C," and submit it. The device formats the side effect information and sends it to the server. The server stores this information in a database and notifies a specialist. The input is the user's side effect report, and the output is a notification to a specialist. The specialist considers necessary countermeasures and sends them to the server as feedback. The server then notifies the user of this feedback.

[0580] Specific examples of operation:

[0581] The prompt message reads: "I took medication A at 8:00, medication B at 12:00, and medication C at 18:00. I have developed a headache. Please tell me what to do."

[0582] (Application Example 1)

[0583] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0584] With the increasing prevalence of autonomous vehicles, concerns are being raised about the impact of medication side effects on driving performance. In particular, for drivers taking multiple medications, there is a need for a system that can pre-evaluate how drug interactions and side effects might affect driving ability and suggest safe medication regimens. However, current systems are not effectively evaluating and notifying drivers, failing to adequately protect their health and safety.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0586] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for suggesting a method of taking medication for the driver of an autonomous vehicle to drive safely and displaying a reminder before driving, and means for notifying the risk of side effects while driving. This makes it possible to maintain the driver's health and safety while driving by evaluating drug interactions and side effect risks of the medications the driver is taking in advance and suggesting a safe medication schedule.

[0587] A "user" refers to an individual who uses the system to provide information about the medications they are currently taking.

[0588] An "autonomous vehicle" refers to a vehicle that can operate with minimal driver intervention.

[0589] "Driver" refers to the individual using the self-driving vehicle.

[0590] "Medicine" refers to chemical or biological substances taken for medical purposes.

[0591] A "medication schedule" refers to a timeline set up to ensure that drivers can safely take their medication.

[0592] "Drug interactions" refer to the interactions that occur when taking multiple medications at the same time.

[0593] "Side effects" refer to undesirable effects caused by medication taken.

[0594] A "reminder" refers to a notification used to prompt a user to take a specific action.

[0595] A "specialist" refers to an individual who is well-versed in medicine and pharmacology and can provide appropriate advice to the driver.

[0596] "AI algorithms" refer to artificial intelligence computational methods used to evaluate the risks of drug interactions and side effects.

[0597] A "terminal" refers to an electronic device used by a user to access applications.

[0598] System Configuration

[0599] This system allows users to input information about the medications they are taking, performs a risk assessment based on that information, and provides advice and reminders to autonomous vehicle drivers to ensure safe medication use. The system mainly consists of a server, terminals, and users. The server is central to processing and analyzing the data.

[0600] User medication information input

[0601] Users use an application installed on their smartphones to input information about the medications they are taking. This information includes the name of the medication, when to take it, and the dosage. This information is sent to a server via the application. The server receives this information and stores it in a database.

[0602] Evaluation of drug interactions and side effects

[0603] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. TensorFlow is used as the AI ​​algorithm, and a machine learning model is constructed. For example, if taking drug A and drug B simultaneously carries a risk of side effects, that risk is reflected in the evaluation results. Furthermore, the server refers to the latest information in expert databases, and any new side effect information is added to the evaluation.

[0604] Generating and notifying advice

[0605] Based on the evaluation results, the server generates specific advice for the user. For example, it may include instructions such as, "Take medication A and medication B at least two hours apart." The generated advice is notified to the user's smartphone, and the user can check it through the application.

[0606] Generation and notification of medication schedule

[0607] The server generates an optimal dosage schedule, taking into account the user's daily rhythm and schedule. For example, if a user typically eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server sets appropriate dosage times. The generated schedule is notified to the smartphone, and a reminder is displayed as the dosage time approaches.

[0608] Uses in the operation of autonomous vehicles

[0609] Before using an autonomous vehicle, the system assesses the impact on driving based on information about the driver's medications and suggests appropriate medication regimens. For example, a reminder such as "Avoid taking medication B before driving" may be displayed. This allows the driver to avoid side effects while driving and drive safely.

[0610] Reporting and feedback on adverse events

[0611] If a user experiences side effects while driving, they can report detailed symptoms through the application. This information is immediately sent to the server and stored in the database. When a specialist is notified, they use this information to provide necessary prescription changes or additional advice. The server then notifies the user of this advice on their smartphone.

[0612] Specific example

[0613] User medication information input

[0614] The user opens the application on their smartphone and enters the following medication information:

[0615] Medication A: After breakfast

[0616] Medication B: After lunch

[0617] Medication C: After dinner

[0618] Evaluation of drug interactions and side effects

[0619] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of side effects between drug A and drug B. The latest information in the database is also included in the evaluation.

[0620] Generating and notifying advice

[0621] The server generates advice stating, "Take medication A and medication B with a two-hour interval between doses," and notifies the user's smartphone. The user then checks this within the app.

[0622] Generation and notification of medication schedule

[0623] The server generates the next medication schedule, taking into account the user's daily routine:

[0624] Medicine A: 8:00

[0625] Medication B: 12:00

[0626] Medication C: 18:00

[0627] Smartphones display reminders periodically.

[0628] Reminders for driving autonomous vehicles

[0629] If the driver starts driving at 12:30, the server will display a reminder at 12:20 stating, "Avoid taking medication B while driving."

[0630] Reporting of side effects

[0631] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The server notifies a specialist of this information, and the specialist instructs the server to "discontinue drug C and prescribe a new medication." The server then notifies the user of this instruction on their smartphone.

[0632] Examples of prompts to input into a generative AI model

[0633] Please provide information on any medications the user is taking before driving. Evaluate the risks of drug interactions and side effects for each medication and suggest a safe medication schedule for when the user is driving.

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The user opens a smartphone application and enters information about the medications they are taking (medication name, timing of administration, dosage, etc.). The input data might include, for example, medication A (after breakfast), medication B (after lunch), and medication C (after dinner). The user enters this information into the application's form and presses the "Submit" button. This sends the medication information to the server.

[0637] Step 2:

[0638] The server retrieves medication information received from the user. The server uses a database management system (e.g., SQLAlchemy) to store this information in a database. Specifically, it stores input data in fields such as medication name, timing of administration, and dosage. This centralizes the management of each user's medication information in the database.

[0639] Step 3:

[0640] The server uses stored drug information and an AI algorithm (e.g., TensorFlow) to evaluate the risks of drug interactions and side effects. The input is drug information data, which the AI ​​algorithm analyzes and outputs a risk assessment result. Specifically, it calculates the risk of side effects when drug A and drug B are taken simultaneously and determines the risk level.

[0641] Step 4:

[0642] The server generates specific advice for the user based on the risk assessment results. This advice may include statements such as, "Take medication A and medication B with a two-hour interval between doses." The server generates this advice in text format and notifies the user's device.

[0643] Step 5:

[0644] The server generates an optimal medication schedule considering the user's daily routine and schedule information. The input is the user's daily routine (e.g., breakfast at 8:00 AM, lunch at 12:00 PM, dinner at 6:00 PM). The generated schedule will include medication A at 8:00 AM, medication B at 12:00 PM, and medication C at 6:00 PM. This schedule is then sent to the smartphone as a text message or reminder.

[0645] Step 6:

[0646] Before the driver uses the self-driving vehicle, the server sends a reminder to their smartphone, such as "Avoid taking medication B before driving." The reminder appears 10 minutes before driving (for example, at 12:20). This allows the driver to avoid side effects while driving.

[0647] Step 7:

[0648] If a user experiences side effects (such as a headache) after taking medication, they report the detailed symptoms within the app. This reported information is sent from the smartphone to a server. The server stores this information in a database and immediately notifies a specialist.

[0649] Step 8:

[0650] Experts review side effect information and provide prescription changes or additional advice as needed. Feedback from experts is sent to a server, which then notifies the user's smartphone. For example, if an expert advises discontinuing medication C and prescribing a new medication, the user will be notified.

[0651] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0652] System Configuration

[0653] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system is further optimized by incorporating an emotion engine that recognizes the user's emotions. The main components of the system are a server, a terminal, and the user, and each component works in cooperation with the others.

[0654] User medication information input

[0655] Users use a dedicated application to input information about the medications they are taking. By entering information such as the name of the medication, timing of administration, and dosage, and sending it to the server through the application, each user's individual medication information is stored in the database.

[0656] Evaluation of drug interactions and side effects

[0657] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[0658] Generating and notifying advice

[0659] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0660] Generation and notification of medication schedule

[0661] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[0662] Reporting and feedback on adverse events

[0663] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[0664] Embedding an emotion engine

[0665] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's voice and facial expressions to evaluate their current emotional state. For example, it can determine whether the user is stressed or relaxed based on their voice tone and facial expressions.

[0666] Adjusting advice based on emotional state

[0667] Based on the emotional state recognized by the emotion engine, the server adjusts the content of its advice. For example, if the user is feeling stressed, it will use kinder and more relaxing language. It will also suggest a chat function with an expert as needed, allowing the user to directly address the problems and questions they are currently experiencing.

[0668] Specific example

[0669] 1. User medication information entry

[0670] The user opens the app and enters the following medication information:

[0671] Medication A: After breakfast

[0672] Medication B: After lunch

[0673] Medication C: After dinner

[0674] 2. Evaluation of drug interactions and side effects

[0675] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[0676] 3. Generating and notifying advice

[0677] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[0678] 4. Generation and notification of medication schedule

[0679] The server generates the next medication schedule, taking into account the user's daily routine:

[0680] Medicine A: 8:00

[0681] Medication B: 12:00

[0682] Medication C: 18:00

[0683] The device will periodically display reminders.

[0684] 5. Reporting and feedback on adverse events

[0685] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[0686] 6. Example of how the emotion engine works

[0687] When a user submits data entered into the app, an emotion engine analyzes their voice tone and facial expressions. If the server determines that the user is stressed, it sends a notification in gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function where users can consult directly with a specialist if needed, supporting them in taking their medication with peace of mind.

[0688] In this way, by incorporating an emotion engine, a system that can improve the user experience can be realized.

[0689] The following describes the processing flow.

[0690] Step 1:

[0691] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[0692] Step 2:

[0693] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[0694] Step 3:

[0695] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[0696] Step 4:

[0697] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0698] Step 5:

[0699] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[0700] Step 6:

[0701] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[0702] Step 7:

[0703] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[0704] Step 8:

[0705] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[0706] Step 9:

[0707] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[0708] Step 10:

[0709] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[0710] Step 11:

[0711] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[0712] Step 12:

[0713] An emotion engine, which recognizes the user's emotions, analyzes the user's voice and facial expressions. The emotion engine evaluates the user's emotional state (e.g., stress or anxiety) and sends that information to the server.

[0714] Step 13:

[0715] The server adjusts the advice based on the emotional state received from the emotion engine. For example, if the user is feeling stressed, it will use kinder and more relaxing language.

[0716] Step 14:

[0717] The server notifies the terminal with advice that reflects the user's emotional state. The terminal receives the advice and displays it to the user.

[0718] Step 15:

[0719] The emotion engine evaluates the user's emotional state, and if stress or anxiety levels are high, the server sends a notification to the device suggesting a chat function with an expert.

[0720] Step 16:

[0721] The device will display a suggestion for a chat function with an expert to the user, allowing the user to start a chat.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0724] For users taking multiple medications, there is a need to accurately assess the risks of drug interactions and side effects, and to provide appropriate advice and dosage schedules. Furthermore, there is a need for a system that can quickly respond to side effects and stress experienced by users and provide individually optimized advice. However, conventional systems have struggled to adequately meet these needs.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes means for receiving information from the user about the medications they are taking, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for recognizing the user's emotions and adjusting advice based on their emotional state, and means for suggesting a chat function with experts according to the emotional state. This not only optimizes the user's medication management but also enables personalized support that takes their emotional state into consideration.

[0727] A "user" is an individual who uses the system to input information about medication use and side effects, and to receive advice and notifications.

[0728] A "server" is a central processing unit that receives data sent from users, stores it in a database, and performs tasks such as evaluating drug pairings, generating advice, and creating schedules.

[0729] A "terminal" is a device that a user directly operates, an electronic device used by the user to input information or receive notifications from a server.

[0730] An "emotion engine" is a software component that analyzes a user's voice and facial expressions to evaluate their current emotional state.

[0731] A "specialist" is a professional who possesses advanced knowledge about the medications and side effects that a user is taking, and who can provide appropriate advice and feedback.

[0732] A "reminder" is a feature used to notify users of specific times or tasks.

[0733] "AI algorithms" refer to machine learning models and other advanced computational methods used to analyze a user's medication information and assess the risk of drug interactions and side effects.

[0734] "Drug interactions" refer to the phenomenon where multiple medications taken simultaneously affect each other.

[0735] "Side effects" refer to undesirable physical or mental reactions that occur as a result of taking medication.

[0736] A "chat function" is a communication feature that allows users and experts to interact in real time.

[0737] A "database" is an information management system used to systematically store and manage data such as a user's medication information, side effect information, and emotional state.

[0738] "Lifestyle rhythm" refers to the temporal patterns of a user's daily life, such as their activity times and meal timings.

[0739] Modes for carrying out the invention

[0740] This invention is an interactive concierge system in which the user inputs information about the medications they are taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system incorporates an emotion engine that recognizes the user's emotions for further optimization. The main components of the system are a server, a terminal, and the user, which work together in cooperation with each other.

[0741] User medication information input

[0742] Users use a dedicated application to input information about the medications they are taking. Input fields include the name of the medication, timing of administration, and dosage. Once the input is complete, the device sends the information to the server.

[0743] As a concrete example, the user enters the following:

[0744] Medication A: After breakfast

[0745] Medication B: After lunch

[0746] Medication C: After dinner

[0747] Drug information transmission and storage

[0748] The terminal sends user input information to the server, and the server stores the received information in a database. During this process, validation is also performed to check the accuracy and consistency of the data.

[0749] Evaluation of drug interactions and side effects

[0750] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information stored in a database. For example, if drug A and drug B may interact when taken simultaneously, that risk is reflected in the evaluation results. New side effect information is obtained from expert databases and added to the evaluation.

[0751] In a specific example, the server evaluates drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B.

[0752] Generating and notifying advice

[0753] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0754] Generation and notification of medication schedule

[0755] The server generates an optimal dosage schedule based on the user's daily routine and schedule information. For example, based on information that the user usually eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the following dosage schedule is generated:

[0756] Medicine A: 8:00

[0757] Medication B: 12:00

[0758] Medication C: 18:00

[0759] The generated schedule will be notified to the device, and a reminder will be set.

[0760] Reporting and feedback on adverse events

[0761] If a user experiences side effects after taking medication, they report the symptoms within the app. This information is sent via the device to a server, which stores it in a database. The server then notifies a specialist of this information and awaits their feedback.

[0762] For example, if a user reports that they developed a headache about two hours after taking drug C, the server will receive instructions from a specialist to "stop taking drug C and prescribe a new drug," and the server will then notify the user's device of these instructions.

[0763] How the emotion engine works

[0764] The device passes data sent when a user uses the app to the emotion engine. The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state, determining whether the user is stressed or relaxed.

[0765] Adjusting advice based on emotional state

[0766] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it will use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function with experts to directly address the user's problems and questions.

[0767] Example of a prompt

[0768] By inputting prompts like the following into the AI ​​model, it can provide advice in a natural conversational format:

[0769] "The user is currently taking the following medications: Medication A (after breakfast), Medication B (after lunch), and Medication C (after dinner). Medications A and B have a risk of interaction and should be taken two hours apart. Please propose an optimal medication schedule that takes the user's lifestyle into consideration. Also, if the user is experiencing stress, please use calming language."

[0770] Using such detailed prompts allows the generative AI model to provide users with appropriate and helpful advice.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] System program processing flow

[0773] Step 1: User enters medication information

[0774] The user opens a dedicated application and enters information about the medication they are taking (such as the name of the medication, timing of administration, and dosage).

[0775] Input: Information such as the name of the drug, timing of administration, and dosage.

[0776] Output: The entered medication information is saved to the terminal.

[0777] Step 2: Send and store medication information

[0778] The terminal sends user input information to the server. The server stores the received information in a database.

[0779] Input: Medication information entered by the user

[0780] Data processing: Data validation and duplicate checking

[0781] Output: Validated drug information is saved to the database.

[0782] Step 3: Evaluation of drug interactions and side effects

[0783] The server uses an AI algorithm to evaluate drug interactions and side effect risks based on drug information stored in a database. It also obtains the latest side effect information from expert databases and incorporates it into the evaluation.

[0784] Input: Drug information stored in the database, side effect information from expert databases.

[0785] Data processing: Risk assessment using AI algorithms

[0786] Output: Drug interaction risk and side effect risk assessment results

[0787] Step 4: Generate and notify advice

[0788] The server generates advice for the user based on the evaluation results. This includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is then notified to the user's device.

[0789] Input: Evaluation results of drug interactions and side effects

[0790] Data processing: Generating advice using AI models.

[0791] Output: The generated advice is notified to the device.

[0792] Step 5: Generate and notify about the medication schedule.

[0793] The server generates an optimal medication schedule based on the user's lifestyle and schedule information. The generated schedule is notified to the user's device, and reminders are set.

[0794] Input: User's daily routine information (breakfast, lunch, dinner times, etc.)

[0795] Data processing: Generating medication schedules

[0796] Output: The generated medication schedule is notified to the device, and a reminder is set.

[0797] Step 6: Reporting and providing feedback on side effects

[0798] If a user experiences side effects after taking medication, they report the symptoms within the app. This report is sent via the device to a server, which stores it in a database and simultaneously notifies a specialist. The specialist's feedback is then provided to the server and notified to the user's device.

[0799] Input: Detailed information on side effects reported by the user

[0800] Data processing: Storage of adverse event information and notification to experts.

[0801] Output: Expert feedback is sent to the user's device via the server.

[0802] Step 7: Operating the Emotion Engine

[0803] When a user uses the app, the emotion engine analyzes their voice tone and facial expressions to assess their current emotional state.

[0804] Input: User's voice tone and facial expression data

[0805] Data processing: Analysis of emotional states using an emotion engine.

[0806] Output: Emotional state evaluation results

[0807] Step 8: Adjusting advice based on emotional state

[0808] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, it might use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It can also provide a chat function with experts.

[0809] Input: Emotional state evaluation result

[0810] Data processing: Adjusting advice from generative AI models

[0811] Output: Adjusted advice is sent to the device.

[0812] (Application Example 2)

[0813] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0814] In modern society, it is crucial for users to manage the proper use of medication and the safe consumption of food to improve their quality of life. However, technology that provides users with optimal advice considering their individual physical condition, emotional state, allergy information, and lifestyle is not yet sufficiently developed. Furthermore, the lack of support that takes into account the user's emotional state can lead to stress and anxiety. This invention is designed to solve these problems.

[0815] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0816] In this invention, the server includes means for receiving information from the user about medications or foods being taken; means for evaluating combinations of medications or foods being taken, as well as side effects and nutritional balance; means for generating and notifying the user of advice based on the evaluation results; means for generating an optimal medication or meal schedule based on the user's lifestyle; means for notifying the user's terminal of the generated medication or meal schedule and setting a reminder; means for receiving and storing reports of side effects or feedback from the user; means for notifying experts of the side effect information or feedback; means for evaluating the user's emotional state using an emotion engine; and means for adjusting the content of advice based on the evaluated emotional state. This enables the user to receive safe and appropriate advice based on information about medication and food intake, and to provide flexible support according to their emotional state.

[0817] A "user" refers to someone who uses this system to input information such as medications they are taking, foods they eat, their lifestyle, and their emotional state, and then receives evaluation results and advice.

[0818] "Medications currently being taken" refers to any medications or supplements that the user is currently using, including information that is entered into the system.

[0819] "Ingredients" refers to the food and ingredients used in dishes that the user is considering consuming, and includes allergy information and nutritional balance.

[0820] "Lifestyle rhythm" refers to the timing and patterns of a user's daily activities, including meal times, sleep times, and medication times.

[0821] "Emotional state" refers to the user's current psychological feelings and mood, and is evaluated based on factors such as voice tone and facial expressions.

[0822] "Evaluation" refers to the process by which the system analyzes and diagnoses drug and food combinations, side effects, nutritional balance, etc., based on information received from the user.

[0823] "Advice" refers to specific instructions and suggestions provided to the user based on the evaluation results, including things like the timing of taking medication or eating.

[0824] A "schedule" refers to the time and order that the system has determined to be optimal for the user to take medication or eat meals, and it is provided along with reminders.

[0825] A "reminder" is a mechanism that notifies the user of medication or meal times, and is set by the system.

[0826] "Feedback" refers to information that users report to the system regarding side effects or impressions they experienced after taking medication or eating.

[0827] "Experts" refer to medical and nutritional professionals who provide additional advice or prescription changes based on user information and feedback regarding side effects.

[0828] An "emotion engine" refers to an algorithm or software that analyzes a user's voice, facial expressions, etc., to evaluate their current emotional state.

[0829] "Adjusting" means modifying and adapting the advice given to the user based on their evaluated emotional state.

[0830] System Configuration

[0831] To implement the present invention, a system comprising the following components is required. The system consists of a server, a terminal, and a user, each working in cooperation with the others.

[0832] Program generation

[0833] The system's main function is to receive information from users about the medications and foods they are currently taking, and then generate and notify them of advice and schedules based on that information. By using an emotion engine, it is possible to provide support tailored to the user's emotional state.

[0834] Hardware to use

[0835] Smartphones: The primary devices on which user interfaces and emotion engines operate.

[0836] Server: Responsible for user data processing, advice generation, and database management.

[0837] Software to use

[0838] AI framework: TensorFlow (for building AI algorithms)

[0839] Database: MySQL (for managing user information and ingredient data)

[0840] Emotion recognition engines: Amazon Rekognition (face recognition) and Google Cloud Speech-to-Text (speech recognition)

[0841] Data processing and data calculation

[0842] 1. User Information Input: Users input information about medications they are taking, foods they eat, their lifestyle, and their emotional state through a dedicated app. This information is sent from the device to the server and stored in a database.

[0843] 2. Evaluation and Advice Generation: Based on the received information, the server uses an AI algorithm to evaluate drug and food combinations, side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[0844] 3. Schedule Generation: The server considers the user's daily routine and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[0845] 4. Feedback Management: When a user enters feedback after taking medication or eating, the device sends that information to the server and stores it in the database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[0846] 5. How the Emotion Engine Works: The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[0847] Specific example

[0848] 1. User enters medication information: The user opens the app and enters "Medication A after breakfast," "Medication B after lunch," and "Medication C after dinner." This information is sent to the server.

[0849] 2. Evaluation and advice generation: The server evaluates the information on drug A, drug B, and drug C and generates advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0850] 3. Schedule generation: The server takes the user's daily routine into consideration and generates and notifies the user of a medication schedule, such as taking medication A at 8:00, medication B at 12:00, and medication C at 18:00.

[0851] 4. Feedback Management: A user enters feedback such as "I experienced a headache after taking drug C," and this information is sent to the server. The server notifies a specialist, and the specialist's instructions are then re-notified to the user.

[0852] 5. Emotion Engine Operation: While the user is using the app, the emotion engine will operate and, for example, if it determines that the user is feeling stressed, it will notify the user with a message such as, "Please relax. We recommend taking medication B at 12:00."

[0853] Example of a prompt

[0854] "Since the user is feeling stressed, please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[0855] In this way, the overall system configuration and operation of the invention enable proper management support for medicines and food ingredients for users.

[0856] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0857] Step 1:

[0858] User input

[0859] Users input information about medications and foods they are currently taking, allergy information, lifestyle, and emotional state through a dedicated application. The entered information is sent from their smartphone to a server and stored in a database.

[0860] Specific actions: The user opens the app and enters information such as "Take medicine A after breakfast," "Take medicine B after lunch," and "Take medicine C after dinner." They also fill in detailed information about their daily routine, such as "I have an egg allergy" and "I usually eat breakfast at 8:00."

[0861] Step 2:

[0862] Evaluation and advice generation

[0863] Based on the received information, the server uses an AI algorithm (TensorFlow) to evaluate combinations of drugs and foods, potential side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[0864] Input: User's medications, food items, and allergy information retrieved from the database by the server.

[0865] Data processing: AI algorithms are used to assess the risk of drug interactions and food allergies.

[0866] Output: Specific advice based on the evaluation results (e.g., "Take drug A and drug B with a 2-hour interval between doses").

[0867] Step 3:

[0868] Schedule generation

[0869] The server takes the user's lifestyle into consideration and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[0870] Input: User's daily routine information (e.g., "Breakfast at 8:00," "Lunch at 12:00")

[0871] Data processing: Calculation of an optimal timetable based on daily routines.

[0872] Output: A schedule such as "Take medicine A at 8:00", "Take medicine B at 12:00", and "Take medicine C at 18:00".

[0873] Step 4:

[0874] Feedback Management

[0875] Users enter feedback after taking medication or eating. This information is sent from the device to the server and stored in a database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[0876] Input: User feedback (e.g., "I experienced a headache after taking drug C")

[0877] Data processing: Feedback information is forwarded to experts, and the user is notified of the experts' responses.

[0878] Output: Instructions from a specialist (e.g., "Discontinue taking drug C and prescribe a new drug")

[0879] Step 5:

[0880] How the emotion engine works

[0881] The emotion engine (Amazon Rekognition and Google Cloud Speech-to-Text) analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[0882] Input: User's voice and facial expression data

[0883] Data processing: Evaluation of emotional states using an emotion engine.

[0884] Output: Adjustment of advice based on emotional state (e.g., "Please relax. We recommend taking medication B at 12:00.")

[0885] Specific operation: While the user is using the app, the camera and microphone are active, and the emotion engine analyzes the user's stress level in real time. For example, if the server determines that the user is feeling stressed, it will send a notification in gentle language such as, "Why not try a relaxing herbal tea today?"

[0886] Example of a prompt

[0887] "The user is feeling stressed, so please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[0888] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0889] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0891] [Third Embodiment]

[0892] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0893] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0894] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0895] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0896] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0897] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0898] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0899] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0900] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0901] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0902] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0903] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0904] System Configuration

[0905] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system mainly consists of a server, a terminal, and a user, and each component works in cooperation with the others.

[0906] User medication information input

[0907] Users use a dedicated application to input information about the medications they are taking. They enter information such as the name of the medication, timing of administration, and dosage, and send this information to the server via the application. This allows each user's individual medication information to be stored in a database.

[0908] Evaluation of drug interactions and side effects

[0909] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[0910] Generating and notifying advice

[0911] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[0912] Generation and notification of medication schedule

[0913] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[0914] Reporting and feedback on adverse events

[0915] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[0916] Specific example

[0917] 1. User medication information entry

[0918] The user opens the app and enters the following medication information:

[0919] Medication A: After breakfast

[0920] Medication B: After lunch

[0921] Medication C: After dinner

[0922] 2. Evaluation of drug interactions and side effects

[0923] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[0924] 3. Generating and notifying advice

[0925] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[0926] 4. Generation and notification of medication schedule

[0927] The server generates the next medication schedule, taking into account the user's daily routine:

[0928] Medicine A: 8:00

[0929] Medication B: 12:00

[0930] Medication C: 18:00

[0931] The device will periodically display reminders.

[0932] 5. Reporting and feedback on adverse events

[0933] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[0934] This will enable users to manage multiple medications safely and effectively.

[0935] The following describes the processing flow.

[0936] Step 1:

[0937] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[0938] Step 2:

[0939] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[0940] Step 3:

[0941] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[0942] Step 4:

[0943] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[0944] Step 5:

[0945] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[0946] Step 6:

[0947] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[0948] Step 7:

[0949] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[0950] Step 8:

[0951] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[0952] Step 9:

[0953] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[0954] Step 10:

[0955] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[0956] Step 11:

[0957] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[0958] (Example 1)

[0959] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0960] Traditional drug administration systems often fail to adequately assess the risks of drug interactions and side effects when multiple medications are taken, potentially posing significant health risks to users. Furthermore, generating optimal dosage schedules based on individual user lifestyles is difficult. Additionally, there is a lack of means for reporting side effects and for prompt notification of appropriate countermeasures by experts. As a result, it is difficult for users to take their medications safely and effectively.

[0961] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0962] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's information processing device of the generated medication schedule and setting reminders, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for receiving feedback from experts and notifying the user, means for evaluating the risks related to the medications being taken using an artificial intelligence algorithm, and means for incorporating new side effect information into the evaluation by referring to information in an expert database. As a result, the user can safely take multiple medications and minimize the risk of side effects.

[0963] A "user" is an individual who uses the system to input medication information, manage their medication use, and monitor for side effects.

[0964] "Information about medications currently being taken" refers to information including details such as the name, dosage, and timing of administration of the medications the user is currently taking.

[0965] "Drug interactions" refer to interactions that occur when multiple medications are taken simultaneously or within a short period of time, and can affect their safety and effectiveness.

[0966] A "side effect" is an undesirable physical or mental reaction that differs from the expected effect of taking a drug.

[0967] "Evaluation results" refer to data indicating the risk of drug interactions and side effects, calculated based on AI algorithms and database information.

[0968] "Advice" refers to specific instructions or warnings provided to the user based on the evaluation results.

[0969] "Lifestyle rhythm" refers to the user's daily activity times and habits, and is taken into consideration in order to optimize the medication schedule.

[0970] A "medication schedule" is a plan that indicates the optimal time and timing for a user to take their medication.

[0971] A "reminder" is an alarm or notification function set to inform the user of the time to take their medication.

[0972] "Reporting side effects" refers to the act of a user entering and submitting information about side effects they have experienced into the system.

[0973] A "specialist" is someone who possesses knowledge of pharmacology or medicine and is qualified to provide users with appropriate advice or prescription changes.

[0974] "Feedback" refers to responses and advice from experts to users, including appropriate measures to address reports of side effects.

[0975] "Artificial intelligence algorithms" refer to AI technology that operates as computer programs and is used for data analysis and risk assessment.

[0976] A "specialist database" is a data storage system that stores the knowledge and latest information on side effects held by experts, and uses it for evaluation.

[0977] An "information processing device" is a device such as a computer or smartphone used by a user, which provides a user interface including notification and reminder functions.

[0978] This invention relates to an interactive concierge system that receives information about medications a user is taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system consists of a server, a terminal, and a user.

[0979] User medication information input

[0980] Users use a dedicated application to enter information about the medications they are taking. This information includes the name of the medication, timing of administration, and dosage. For example, a user might enter the following:

[0981] Medication A: 1 tablet after breakfast

[0982] Medication B: 1 tablet after lunch

[0983] Medication C: 1 tablet after dinner

[0984] Once the user completes the input and presses the "Submit" button, the device sends the information to the server. The server stores the received medication information in its database.

[0985] Evaluation of drug interactions and side effects

[0986] The server compares the received drug information with drug interaction information in its database. Using a generative AI model, the server evaluates the risks of drug interactions and side effects from multiple medications. For example, if the server detects an interaction risk between "drug A" and "drug B," it saves the evaluation results to its internal database. The server periodically updates its expert database and incorporates new side effect information into its evaluation.

[0987] Generating and notifying advice

[0988] Based on the evaluation results, the server generates specific advice for the user. This advice includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is notified from the server to the user's terminal, and the user can check the notification in the application.

[0989] Example of a prompt:

[0990] "I take medication A at 8:00, medication B at 12:00, and medication C at 18:00. I've developed a headache. What should I do?"

[0991] Generation and notification of medication schedule

[0992] The server generates an optimal medication schedule, taking into account the user's daily routine and existing schedule. For example, it sets a medication schedule based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the user's device, and reminders are set. The device displays the set reminders in a timely manner to prompt the user to take their medication.

[0993] Specific example:

[0994] Medicine A: 8:00

[0995] Medication B: 12:00

[0996] Medication C: 18:00

[0997] Reporting and feedback on adverse events

[0998] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might report, "I developed a headache about two hours after taking medication C." The device sends this information to a server, which stores the received side effect information in a database. Experts review the report and provide the server with any necessary prescription changes or additional advice. The server then notifies the user's device of this advice.

[0999] Specific example:

[1000] The user reports within the app that they developed a headache about two hours after taking medication C, and the device sends this information to the server. The server notifies a specialist, who advises the user to "stop taking medication C and be prescribed a new medication." The server then notifies the user's device of this advice, which is displayed to the user.

[1001] This invention enables users to safely take multiple medications and effectively manage their medications while minimizing the risk of side effects. This is expected to further improve users' health management.

[1002] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1003] Step 1: "Enter and submit medication information"

[1004] The user opens a dedicated application. The user enters information about the medication they are currently taking. The input includes the name of the medication, the timing of administration, and the dosage. For example, the user enters "Medication A: After breakfast, 1 tablet" and presses the "Submit" button. The terminal formats the entered information and sends it to the server. The input data is "Medication A, after breakfast, 1 tablet," and the output is the transmission of data to the server. The server saves the received medication information to its database.

[1005] Step 2: "Evaluation of drug interactions and side effects"

[1006] The server compares the received drug information with the drug information in its database. The server then applies a generative AI model to evaluate the risks of drug interactions and side effects from multiple medications. For example, it might detect an interaction risk between drug A and drug B. The evaluation result would be "The interaction risk between drug A and drug B is high." The input is the drug information from the database and the received drug information, and the output is the interaction risk evaluation result. The server saves this evaluation result in its internal database.

[1007] Step 3: "Generating and notifying advice based on evaluation results"

[1008] The server uses the drug interaction and side effect risk assessment results to generate advice for the user. For example, it might generate specific instructions such as, "Take drug A and drug B with a 2-hour interval between doses." The generated advice is sent from the server to the user's device. The input is the assessment results, and the output is the generated advice. The device receives this notification and informs the user via push notification.

[1009] Step 4: "Generating and notifying you of the optimal dosage schedule"

[1010] The server generates an optimal medication schedule based on the user's daily routine and existing schedule. For example, if a user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the medication schedule will be set based on those times. This schedule is notified from the server to the user's terminal. The input is the user's daily routine information, and the output is the generated medication schedule. The terminal sets reminders based on this schedule and displays them in a timely manner.

[1011] Step 5: "Reporting side effects and notifying a professional"

[1012] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might input, "I developed a headache two hours after taking medication C," and submit it. The device formats the side effect information and sends it to the server. The server stores this information in a database and notifies a specialist. The input is the user's side effect report, and the output is a notification to a specialist. The specialist considers necessary countermeasures and sends them to the server as feedback. The server then notifies the user of this feedback.

[1013] Specific examples of operation:

[1014] The prompt message reads: "I took medication A at 8:00, medication B at 12:00, and medication C at 18:00. I have developed a headache. Please tell me what to do."

[1015] (Application Example 1)

[1016] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1017] With the increasing prevalence of autonomous vehicles, concerns are being raised about the impact of medication side effects on driving performance. In particular, for drivers taking multiple medications, there is a need for a system that can pre-evaluate how drug interactions and side effects might affect driving ability and suggest safe medication regimens. However, current systems are not effectively evaluating and notifying drivers, failing to adequately protect their health and safety.

[1018] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1019] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for suggesting a method of taking medication for the driver of an autonomous vehicle to drive safely and displaying a reminder before driving, and means for notifying the risk of side effects while driving. This makes it possible to maintain the driver's health and safety while driving by evaluating drug interactions and side effect risks of the medications the driver is taking in advance and suggesting a safe medication schedule.

[1020] A "user" refers to an individual who uses the system to provide information about the medications they are currently taking.

[1021] An "autonomous vehicle" refers to a vehicle that can operate with minimal driver intervention.

[1022] "Driver" refers to the individual using the self-driving vehicle.

[1023] "Medicine" refers to chemical or biological substances taken for medical purposes.

[1024] A "medication schedule" refers to a timeline set up to ensure that drivers can safely take their medication.

[1025] "Drug interactions" refer to the interactions that occur when taking multiple medications at the same time.

[1026] "Side effects" refer to undesirable effects caused by medication taken.

[1027] A "reminder" refers to a notification used to prompt a user to take a specific action.

[1028] A "specialist" refers to an individual who is well-versed in medicine and pharmacology and can provide appropriate advice to the driver.

[1029] "AI algorithms" refer to artificial intelligence computational methods used to evaluate the risks of drug interactions and side effects.

[1030] A "terminal" refers to an electronic device used by a user to access applications.

[1031] System Configuration

[1032] This system allows users to input information about the medications they are taking, performs a risk assessment based on that information, and provides advice and reminders to autonomous vehicle drivers to ensure safe medication use. The system mainly consists of a server, terminals, and users. The server is central to processing and analyzing the data.

[1033] User medication information input

[1034] Users use an application installed on their smartphones to input information about the medications they are taking. This information includes the name of the medication, when to take it, and the dosage. This information is sent to a server via the application. The server receives this information and stores it in a database.

[1035] Evaluation of drug interactions and side effects

[1036] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. TensorFlow is used as the AI ​​algorithm, and a machine learning model is constructed. For example, if taking drug A and drug B simultaneously carries a risk of side effects, that risk is reflected in the evaluation results. Furthermore, the server refers to the latest information in expert databases, and any new side effect information is added to the evaluation.

[1037] Generating and notifying advice

[1038] Based on the evaluation results, the server generates specific advice for the user. For example, it may include instructions such as, "Take medication A and medication B at least two hours apart." The generated advice is notified to the user's smartphone, and the user can check it through the application.

[1039] Generation and notification of medication schedule

[1040] The server generates an optimal dosage schedule, taking into account the user's daily rhythm and schedule. For example, if a user typically eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server sets appropriate dosage times. The generated schedule is notified to the smartphone, and a reminder is displayed as the dosage time approaches.

[1041] Uses in the operation of autonomous vehicles

[1042] Before using an autonomous vehicle, the system assesses the impact on driving based on information about the driver's medications and suggests appropriate medication regimens. For example, a reminder such as "Avoid taking medication B before driving" may be displayed. This allows the driver to avoid side effects while driving and drive safely.

[1043] Reporting and feedback on adverse events

[1044] If a user experiences side effects while driving, they can report detailed symptoms through the application. This information is immediately sent to the server and stored in the database. When a specialist is notified, they use this information to provide necessary prescription changes or additional advice. The server then notifies the user of this advice on their smartphone.

[1045] Specific example

[1046] User medication information input

[1047] The user opens the application on their smartphone and enters the following medication information:

[1048] Medication A: After breakfast

[1049] Medication B: After lunch

[1050] Medication C: After dinner

[1051] Evaluation of drug interactions and side effects

[1052] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of side effects between drug A and drug B. The latest information in the database is also included in the evaluation.

[1053] Generating and notifying advice

[1054] The server generates advice stating, "Take medication A and medication B with a two-hour interval between doses," and notifies the user's smartphone. The user then checks this within the app.

[1055] Generation and notification of medication schedule

[1056] The server generates the next medication schedule, taking into account the user's daily routine:

[1057] Medicine A: 8:00

[1058] Medication B: 12:00

[1059] Medication C: 18:00

[1060] Smartphones display reminders periodically.

[1061] Reminders for driving autonomous vehicles

[1062] If the driver starts driving at 12:30, the server will display a reminder at 12:20 stating, "Avoid taking medication B while driving."

[1063] Reporting of side effects

[1064] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The server notifies a specialist of this information, and the specialist instructs the server to "discontinue drug C and prescribe a new medication." The server then notifies the user of this instruction on their smartphone.

[1065] Examples of prompts to input into a generative AI model

[1066] Please provide information on any medications the user is taking before driving. Evaluate the risks of drug interactions and side effects for each medication and suggest a safe medication schedule for when the user is driving.

[1067] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1068] Step 1:

[1069] The user opens a smartphone application and enters information about the medications they are taking (medication name, timing of administration, dosage, etc.). The input data might include, for example, medication A (after breakfast), medication B (after lunch), and medication C (after dinner). The user enters this information into the application's form and presses the "Submit" button. This sends the medication information to the server.

[1070] Step 2:

[1071] The server retrieves medication information received from the user. The server uses a database management system (e.g., SQLAlchemy) to store this information in a database. Specifically, it stores input data in fields such as medication name, timing of administration, and dosage. This centralizes the management of each user's medication information in the database.

[1072] Step 3:

[1073] The server uses stored drug information and an AI algorithm (e.g., TensorFlow) to evaluate the risks of drug interactions and side effects. The input is drug information data, which the AI ​​algorithm analyzes and outputs a risk assessment result. Specifically, it calculates the risk of side effects when drug A and drug B are taken simultaneously and determines the risk level.

[1074] Step 4:

[1075] The server generates specific advice for the user based on the risk assessment results. This advice may include statements such as, "Take medication A and medication B with a two-hour interval between doses." The server generates this advice in text format and notifies the user's device.

[1076] Step 5:

[1077] The server generates an optimal medication schedule considering the user's daily routine and schedule information. The input is the user's daily routine (e.g., breakfast at 8:00 AM, lunch at 12:00 PM, dinner at 6:00 PM). The generated schedule will include medication A at 8:00 AM, medication B at 12:00 PM, and medication C at 6:00 PM. This schedule is then sent to the smartphone as a text message or reminder.

[1078] Step 6:

[1079] Before the driver uses the self-driving vehicle, the server sends a reminder to their smartphone, such as "Avoid taking medication B before driving." The reminder appears 10 minutes before driving (for example, at 12:20). This allows the driver to avoid side effects while driving.

[1080] Step 7:

[1081] If a user experiences side effects (such as a headache) after taking medication, they report the detailed symptoms within the app. This reported information is sent from the smartphone to a server. The server stores this information in a database and immediately notifies a specialist.

[1082] Step 8:

[1083] Experts review side effect information and provide prescription changes or additional advice as needed. Feedback from experts is sent to a server, which then notifies the user's smartphone. For example, if an expert advises discontinuing medication C and prescribing a new medication, the user will be notified.

[1084] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1085] System Configuration

[1086] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system is further optimized by incorporating an emotion engine that recognizes the user's emotions. The main components of the system are a server, a terminal, and the user, and each component works in cooperation with the others.

[1087] User medication information input

[1088] Users use a dedicated application to input information about the medications they are taking. By entering information such as the name of the medication, timing of administration, and dosage, and sending it to the server through the application, each user's individual medication information is stored in the database.

[1089] Evaluation of drug interactions and side effects

[1090] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[1091] Generating and notifying advice

[1092] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[1093] Generation and notification of medication schedule

[1094] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[1095] Reporting and feedback on adverse events

[1096] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[1097] Embedding an emotion engine

[1098] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's voice and facial expressions to evaluate their current emotional state. For example, it can determine whether the user is stressed or relaxed based on their voice tone and facial expressions.

[1099] Adjusting advice based on emotional state

[1100] Based on the emotional state recognized by the emotion engine, the server adjusts the content of its advice. For example, if the user is feeling stressed, it will use kinder and more relaxing language. It will also suggest a chat function with an expert as needed, allowing the user to directly address the problems and questions they are currently experiencing.

[1101] Specific example

[1102] 1. User medication information entry

[1103] The user opens the app and enters the following medication information:

[1104] Medication A: After breakfast

[1105] Medication B: After lunch

[1106] Medication C: After dinner

[1107] 2. Evaluation of drug interactions and side effects

[1108] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[1109] 3. Generating and notifying advice

[1110] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[1111] 4. Generation and notification of medication schedule

[1112] The server generates the next medication schedule, taking into account the user's daily routine:

[1113] Medicine A: 8:00

[1114] Medication B: 12:00

[1115] Medication C: 18:00

[1116] The device will periodically display reminders.

[1117] 5. Reporting and feedback on adverse events

[1118] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[1119] 6. Example of how the emotion engine works

[1120] When a user submits data entered into the app, an emotion engine analyzes their voice tone and facial expressions. If the server determines that the user is stressed, it sends a notification in gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function where users can consult directly with a specialist if needed, supporting them in taking their medication with peace of mind.

[1121] In this way, by incorporating an emotion engine, a system that can improve the user experience can be realized.

[1122] The following describes the processing flow.

[1123] Step 1:

[1124] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[1125] Step 2:

[1126] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[1127] Step 3:

[1128] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[1129] Step 4:

[1130] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[1131] Step 5:

[1132] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[1133] Step 6:

[1134] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[1135] Step 7:

[1136] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[1137] Step 8:

[1138] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[1139] Step 9:

[1140] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[1141] Step 10:

[1142] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[1143] Step 11:

[1144] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[1145] Step 12:

[1146] An emotion engine, which recognizes the user's emotions, analyzes the user's voice and facial expressions. The emotion engine evaluates the user's emotional state (e.g., stress or anxiety) and sends that information to the server.

[1147] Step 13:

[1148] The server adjusts the advice based on the emotional state received from the emotion engine. For example, if the user is feeling stressed, it will use kinder and more relaxing language.

[1149] Step 14:

[1150] The server notifies the terminal with advice that reflects the user's emotional state. The terminal receives the advice and displays it to the user.

[1151] Step 15:

[1152] The emotion engine evaluates the user's emotional state, and if stress or anxiety levels are high, the server sends a notification to the device suggesting a chat function with an expert.

[1153] Step 16:

[1154] The device will display a suggestion for a chat function with an expert to the user, allowing the user to start a chat.

[1155] (Example 2)

[1156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1157] For users taking multiple medications, there is a need to accurately assess the risks of drug interactions and side effects, and to provide appropriate advice and dosage schedules. Furthermore, there is a need for a system that can quickly respond to side effects and stress experienced by users and provide individually optimized advice. However, conventional systems have struggled to adequately meet these needs.

[1158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1159] In this invention, the server includes means for receiving information from the user about the medications they are taking, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for recognizing the user's emotions and adjusting advice based on their emotional state, and means for suggesting a chat function with experts according to the emotional state. This not only optimizes the user's medication management but also enables personalized support that takes their emotional state into consideration.

[1160] A "user" is an individual who uses the system to input information about medication use and side effects, and to receive advice and notifications.

[1161] A "server" is a central processing unit that receives data sent from users, stores it in a database, and performs tasks such as evaluating drug pairings, generating advice, and creating schedules.

[1162] A "terminal" is a device that a user directly operates, an electronic device used by the user to input information or receive notifications from a server.

[1163] An "emotion engine" is a software component that analyzes a user's voice and facial expressions to evaluate their current emotional state.

[1164] A "specialist" is a professional who possesses advanced knowledge about the medications and side effects that a user is taking, and who can provide appropriate advice and feedback.

[1165] A "reminder" is a feature used to notify users of specific times or tasks.

[1166] "AI algorithms" refer to machine learning models and other advanced computational methods used to analyze a user's medication information and assess the risk of drug interactions and side effects.

[1167] "Drug interactions" refer to the phenomenon where multiple medications taken simultaneously affect each other.

[1168] "Side effects" refer to undesirable physical or mental reactions that occur as a result of taking medication.

[1169] A "chat function" is a communication feature that allows users and experts to interact in real time.

[1170] A "database" is an information management system used to systematically store and manage data such as a user's medication information, side effect information, and emotional state.

[1171] "Lifestyle rhythm" refers to the temporal patterns of a user's daily life, such as their activity times and meal timings.

[1172] Modes for carrying out the invention

[1173] This invention is an interactive concierge system in which the user inputs information about the medications they are taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system incorporates an emotion engine that recognizes the user's emotions for further optimization. The main components of the system are a server, a terminal, and the user, which work together in cooperation with each other.

[1174] User medication information input

[1175] Users use a dedicated application to input information about the medications they are taking. Input fields include the name of the medication, timing of administration, and dosage. Once the input is complete, the device sends the information to the server.

[1176] As a concrete example, the user enters the following:

[1177] Medication A: After breakfast

[1178] Medication B: After lunch

[1179] Medication C: After dinner

[1180] Drug information transmission and storage

[1181] The terminal sends user input information to the server, and the server stores the received information in a database. During this process, validation is also performed to check the accuracy and consistency of the data.

[1182] Evaluation of drug interactions and side effects

[1183] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information stored in a database. For example, if drug A and drug B may interact when taken simultaneously, that risk is reflected in the evaluation results. New side effect information is obtained from expert databases and added to the evaluation.

[1184] In a specific example, the server evaluates drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B.

[1185] Generating and notifying advice

[1186] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[1187] Generation and notification of medication schedule

[1188] The server generates an optimal dosage schedule based on the user's daily routine and schedule information. For example, based on information that the user usually eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the following dosage schedule is generated:

[1189] Medicine A: 8:00

[1190] Medication B: 12:00

[1191] Medication C: 18:00

[1192] The generated schedule will be notified to the device, and a reminder will be set.

[1193] Reporting and feedback on adverse events

[1194] If a user experiences side effects after taking medication, they report the symptoms within the app. This information is sent via the device to a server, which stores it in a database. The server then notifies a specialist of this information and awaits their feedback.

[1195] For example, if a user reports that they developed a headache about two hours after taking drug C, the server will receive instructions from a specialist to "stop taking drug C and prescribe a new drug," and the server will then notify the user's device of these instructions.

[1196] How the emotion engine works

[1197] The device passes data sent when a user uses the app to the emotion engine. The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state, determining whether the user is stressed or relaxed.

[1198] Adjusting advice based on emotional state

[1199] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it will use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function with experts to directly address the user's problems and questions.

[1200] Example of a prompt

[1201] By inputting prompts like the following into the AI ​​model, it can provide advice in a natural conversational format:

[1202] "The user is currently taking the following medications: Medication A (after breakfast), Medication B (after lunch), and Medication C (after dinner). Medications A and B have a risk of interaction and should be taken two hours apart. Please propose an optimal medication schedule that takes the user's lifestyle into consideration. Also, if the user is experiencing stress, please use calming language."

[1203] Using such detailed prompts allows the generative AI model to provide users with appropriate and helpful advice.

[1204] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1205] System program processing flow

[1206] Step 1: User enters medication information

[1207] The user opens a dedicated application and enters information about the medication they are taking (such as the name of the medication, timing of administration, and dosage).

[1208] Input: Information such as the name of the drug, timing of administration, and dosage.

[1209] Output: The entered medication information is saved to the terminal.

[1210] Step 2: Send and store medication information

[1211] The terminal sends user input information to the server. The server stores the received information in a database.

[1212] Input: Medication information entered by the user

[1213] Data processing: Data validation and duplicate checking

[1214] Output: Validated drug information is saved to the database.

[1215] Step 3: Evaluation of drug interactions and side effects

[1216] The server uses an AI algorithm to evaluate drug interactions and side effect risks based on drug information stored in a database. It also obtains the latest side effect information from expert databases and incorporates it into the evaluation.

[1217] Input: Drug information stored in the database, side effect information from expert databases.

[1218] Data processing: Risk assessment using AI algorithms

[1219] Output: Drug interaction risk and side effect risk assessment results

[1220] Step 4: Generate and notify advice

[1221] The server generates advice for the user based on the evaluation results. This includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is then notified to the user's device.

[1222] Input: Evaluation results of drug interactions and side effects

[1223] Data processing: Generating advice using AI models.

[1224] Output: The generated advice is notified to the device.

[1225] Step 5: Generate and notify about the medication schedule.

[1226] The server generates an optimal medication schedule based on the user's lifestyle and schedule information. The generated schedule is notified to the user's device, and reminders are set.

[1227] Input: User's daily routine information (breakfast, lunch, dinner times, etc.)

[1228] Data processing: Generating medication schedules

[1229] Output: The generated medication schedule is notified to the device, and a reminder is set.

[1230] Step 6: Reporting and providing feedback on side effects

[1231] If a user experiences side effects after taking medication, they report the symptoms within the app. This report is sent via the device to a server, which stores it in a database and simultaneously notifies a specialist. The specialist's feedback is then provided to the server and notified to the user's device.

[1232] Input: Detailed information on side effects reported by the user

[1233] Data processing: Storage of adverse event information and notification to experts.

[1234] Output: Expert feedback is sent to the user's device via the server.

[1235] Step 7: Operating the Emotion Engine

[1236] When a user uses the app, the emotion engine analyzes their voice tone and facial expressions to assess their current emotional state.

[1237] Input: User's voice tone and facial expression data

[1238] Data processing: Analysis of emotional states using an emotion engine.

[1239] Output: Emotional state evaluation results

[1240] Step 8: Adjusting advice based on emotional state

[1241] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, it might use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It can also provide a chat function with experts.

[1242] Input: Emotional state evaluation result

[1243] Data processing: Adjusting advice from generative AI models

[1244] Output: Adjusted advice is sent to the device.

[1245] (Application Example 2)

[1246] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1247] In modern society, it is crucial for users to manage the proper use of medication and the safe consumption of food to improve their quality of life. However, technology that provides users with optimal advice considering their individual physical condition, emotional state, allergy information, and lifestyle is not yet sufficiently developed. Furthermore, the lack of support that takes into account the user's emotional state can lead to stress and anxiety. This invention is designed to solve these problems.

[1248] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1249] In this invention, the server includes means for receiving information from the user about medications or foods being taken; means for evaluating combinations of medications or foods being taken, as well as side effects and nutritional balance; means for generating and notifying the user of advice based on the evaluation results; means for generating an optimal medication or meal schedule based on the user's lifestyle; means for notifying the user's terminal of the generated medication or meal schedule and setting a reminder; means for receiving and storing reports of side effects or feedback from the user; means for notifying experts of the side effect information or feedback; means for evaluating the user's emotional state using an emotion engine; and means for adjusting the content of advice based on the evaluated emotional state. This enables the user to receive safe and appropriate advice based on information about medication and food intake, and to provide flexible support according to their emotional state.

[1250] A "user" refers to someone who uses this system to input information such as medications they are taking, foods they eat, their lifestyle, and their emotional state, and then receives evaluation results and advice.

[1251] "Medications currently being taken" refers to any medications or supplements that the user is currently using, including information that is entered into the system.

[1252] "Ingredients" refers to the food and ingredients used in dishes that the user is considering consuming, and includes allergy information and nutritional balance.

[1253] "Lifestyle rhythm" refers to the timing and patterns of a user's daily activities, including meal times, sleep times, and medication times.

[1254] "Emotional state" refers to the user's current psychological feelings and mood, and is evaluated based on factors such as voice tone and facial expressions.

[1255] "Evaluation" refers to the process by which the system analyzes and diagnoses drug and food combinations, side effects, nutritional balance, etc., based on information received from the user.

[1256] "Advice" refers to specific instructions and suggestions provided to the user based on the evaluation results, including things like the timing of taking medication or eating.

[1257] A "schedule" refers to the time and order that the system has determined to be optimal for the user to take medication or eat meals, and it is provided along with reminders.

[1258] A "reminder" is a mechanism that notifies the user of medication or meal times, and is set by the system.

[1259] "Feedback" refers to information that users report to the system regarding side effects or impressions they experienced after taking medication or eating.

[1260] "Experts" refer to medical and nutritional professionals who provide additional advice or prescription changes based on user information and feedback regarding side effects.

[1261] An "emotion engine" refers to an algorithm or software that analyzes a user's voice, facial expressions, etc., to evaluate their current emotional state.

[1262] "Adjusting" means modifying and adapting the advice given to the user based on their evaluated emotional state.

[1263] System Configuration

[1264] To implement the present invention, a system comprising the following components is required. The system consists of a server, a terminal, and a user, each working in cooperation with the others.

[1265] Program generation

[1266] The system's main function is to receive information from users about the medications and foods they are currently taking, and then generate and notify them of advice and schedules based on that information. By using an emotion engine, it is possible to provide support tailored to the user's emotional state.

[1267] Hardware to use

[1268] Smartphones: The primary devices on which user interfaces and emotion engines operate.

[1269] Server: Responsible for user data processing, advice generation, and database management.

[1270] Software to use

[1271] AI framework: TensorFlow (for building AI algorithms)

[1272] Database: MySQL (for managing user information and ingredient data)

[1273] Emotion recognition engines: Amazon Rekognition (face recognition) and Google Cloud Speech-to-Text (speech recognition)

[1274] Data processing and data calculation

[1275] 1. User Information Input: Users input information about medications they are taking, foods they eat, their lifestyle, and their emotional state through a dedicated app. This information is sent from the device to the server and stored in a database.

[1276] 2. Evaluation and Advice Generation: Based on the received information, the server uses an AI algorithm to evaluate drug and food combinations, side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[1277] 3. Schedule Generation: The server considers the user's daily routine and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[1278] 4. Feedback Management: When a user enters feedback after taking medication or eating, the device sends that information to the server and stores it in the database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[1279] 5. How the Emotion Engine Works: The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[1280] Specific example

[1281] 1. User enters medication information: The user opens the app and enters "Medication A after breakfast," "Medication B after lunch," and "Medication C after dinner." This information is sent to the server.

[1282] 2. Evaluation and advice generation: The server evaluates the information on drug A, drug B, and drug C and generates advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[1283] 3. Schedule generation: The server takes the user's daily routine into consideration and generates and notifies the user of a medication schedule, such as taking medication A at 8:00, medication B at 12:00, and medication C at 18:00.

[1284] 4. Feedback Management: A user enters feedback such as "I experienced a headache after taking drug C," and this information is sent to the server. The server notifies a specialist, and the specialist's instructions are then re-notified to the user.

[1285] 5. Emotion Engine Operation: While the user is using the app, the emotion engine will operate and, for example, if it determines that the user is feeling stressed, it will notify the user with a message such as, "Please relax. We recommend taking medication B at 12:00."

[1286] Example of a prompt

[1287] "Since the user is feeling stressed, please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[1288] In this way, the overall system configuration and operation of the invention enable proper management support for medicines and food ingredients for users.

[1289] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1290] Step 1:

[1291] User input

[1292] Users input information about medications and foods they are currently taking, allergy information, lifestyle, and emotional state through a dedicated application. The entered information is sent from their smartphone to a server and stored in a database.

[1293] Specific actions: The user opens the app and enters information such as "Take medicine A after breakfast," "Take medicine B after lunch," and "Take medicine C after dinner." They also fill in detailed information about their daily routine, such as "I have an egg allergy" and "I usually eat breakfast at 8:00."

[1294] Step 2:

[1295] Evaluation and advice generation

[1296] Based on the received information, the server uses an AI algorithm (TensorFlow) to evaluate combinations of drugs and foods, potential side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[1297] Input: User's medications, food items, and allergy information retrieved from the database by the server.

[1298] Data processing: AI algorithms are used to assess the risk of drug interactions and food allergies.

[1299] Output: Specific advice based on the evaluation results (e.g., "Take drug A and drug B with a 2-hour interval between doses").

[1300] Step 3:

[1301] Schedule generation

[1302] The server takes the user's lifestyle into consideration and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[1303] Input: User's daily routine information (e.g., "Breakfast at 8:00," "Lunch at 12:00")

[1304] Data processing: Calculation of an optimal timetable based on daily routines.

[1305] Output: A schedule such as "Take medicine A at 8:00", "Take medicine B at 12:00", and "Take medicine C at 18:00".

[1306] Step 4:

[1307] Feedback Management

[1308] Users enter feedback after taking medication or eating. This information is sent from the device to the server and stored in a database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[1309] Input: User feedback (e.g., "I experienced a headache after taking drug C")

[1310] Data processing: Feedback information is forwarded to experts, and the user is notified of the experts' responses.

[1311] Output: Instructions from a specialist (e.g., "Discontinue taking drug C and prescribe a new drug")

[1312] Step 5:

[1313] How the emotion engine works

[1314] The emotion engine (Amazon Rekognition and Google Cloud Speech-to-Text) analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[1315] Input: User's voice and facial expression data

[1316] Data processing: Evaluation of emotional states using an emotion engine.

[1317] Output: Adjustment of advice based on emotional state (e.g., "Please relax. We recommend taking medication B at 12:00.")

[1318] Specific operation: While the user is using the app, the camera and microphone are active, and the emotion engine analyzes the user's stress level in real time. For example, if the server determines that the user is feeling stressed, it will send a notification in gentle language such as, "Why not try a relaxing herbal tea today?"

[1319] Example of a prompt

[1320] "The user is feeling stressed, so please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[1321] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1322] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1323] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1324] [Fourth Embodiment]

[1325] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1326] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1327] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1328] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1329] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1330] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1331] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1332] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1333] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1334] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1335] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1336] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1337] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1338] System Configuration

[1339] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system mainly consists of a server, a terminal, and a user, and each component works in cooperation with the others.

[1340] User medication information input

[1341] Users use a dedicated application to input information about the medications they are taking. They enter information such as the name of the medication, timing of administration, and dosage, and send this information to the server via the application. This allows each user's individual medication information to be stored in a database.

[1342] Evaluation of drug interactions and side effects

[1343] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[1344] Generating and notifying advice

[1345] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[1346] Generation and notification of medication schedule

[1347] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[1348] Reporting and feedback on adverse events

[1349] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[1350] Specific example

[1351] 1. User medication information entry

[1352] The user opens the app and enters the following medication information:

[1353] Medication A: After breakfast

[1354] Medication B: After lunch

[1355] Medication C: After dinner

[1356] 2. Evaluation of drug interactions and side effects

[1357] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[1358] 3. Generating and notifying advice

[1359] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[1360] 4. Generation and notification of medication schedule

[1361] The server generates the next medication schedule, taking into account the user's daily routine:

[1362] Medicine A: 8:00

[1363] Medication B: 12:00

[1364] Medication C: 18:00

[1365] The device will periodically display reminders.

[1366] 5. Reporting and feedback on adverse events

[1367] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[1368] This will enable users to manage multiple medications safely and effectively.

[1369] The following describes the processing flow.

[1370] Step 1:

[1371] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[1372] Step 2:

[1373] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[1374] Step 3:

[1375] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[1376] Step 4:

[1377] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[1378] Step 5:

[1379] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[1380] Step 6:

[1381] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[1382] Step 7:

[1383] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[1384] Step 8:

[1385] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[1386] Step 9:

[1387] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[1388] Step 10:

[1389] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[1390] Step 11:

[1391] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[1392] (Example 1)

[1393] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1394] Traditional drug administration systems often fail to adequately assess the risks of drug interactions and side effects when multiple medications are taken, potentially posing significant health risks to users. Furthermore, generating optimal dosage schedules based on individual user lifestyles is difficult. Additionally, there is a lack of means for reporting side effects and for prompt notification of appropriate countermeasures by experts. As a result, it is difficult for users to take their medications safely and effectively.

[1395] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1396] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's information processing device of the generated medication schedule and setting reminders, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for receiving feedback from experts and notifying the user, means for evaluating the risks related to the medications being taken using an artificial intelligence algorithm, and means for incorporating new side effect information into the evaluation by referring to information in an expert database. As a result, the user can safely take multiple medications and minimize the risk of side effects.

[1397] A "user" is an individual who uses the system to input medication information, manage their medication use, and monitor for side effects.

[1398] "Information about medications currently being taken" refers to information including details such as the name, dosage, and timing of administration of the medications the user is currently taking.

[1399] "Drug interactions" refer to interactions that occur when multiple medications are taken simultaneously or within a short period of time, and can affect their safety and effectiveness.

[1400] A "side effect" is an undesirable physical or mental reaction that differs from the expected effect of taking a drug.

[1401] "Evaluation results" refer to data indicating the risk of drug interactions and side effects, calculated based on AI algorithms and database information.

[1402] "Advice" refers to specific instructions or warnings provided to the user based on the evaluation results.

[1403] "Lifestyle rhythm" refers to the user's daily activity times and habits, and is taken into consideration in order to optimize the medication schedule.

[1404] A "medication schedule" is a plan that indicates the optimal time and timing for a user to take their medication.

[1405] A "reminder" is an alarm or notification function set to inform the user of the time to take their medication.

[1406] "Reporting side effects" refers to the act of a user entering and submitting information about side effects they have experienced into the system.

[1407] A "specialist" is someone who possesses knowledge of pharmacology or medicine and is qualified to provide users with appropriate advice or prescription changes.

[1408] "Feedback" refers to responses and advice from experts to users, including appropriate measures to address reports of side effects.

[1409] "Artificial intelligence algorithms" refer to AI technology that operates as computer programs and is used for data analysis and risk assessment.

[1410] A "specialist database" is a data storage system that stores the knowledge and latest information on side effects held by experts, and uses it for evaluation.

[1411] An "information processing device" is a device such as a computer or smartphone used by a user, which provides a user interface including notification and reminder functions.

[1412] This invention relates to an interactive concierge system that receives information about medications a user is taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system consists of a server, a terminal, and a user.

[1413] User medication information input

[1414] Users use a dedicated application to enter information about the medications they are taking. This information includes the name of the medication, timing of administration, and dosage. For example, a user might enter the following:

[1415] Medication A: 1 tablet after breakfast

[1416] Medication B: 1 tablet after lunch

[1417] Medication C: 1 tablet after dinner

[1418] Once the user completes the input and presses the "Submit" button, the device sends the information to the server. The server stores the received medication information in its database.

[1419] Evaluation of drug interactions and side effects

[1420] The server compares the received drug information with drug interaction information in its database. Using a generative AI model, the server evaluates the risks of drug interactions and side effects from multiple medications. For example, if the server detects an interaction risk between "drug A" and "drug B," it saves the evaluation results to its internal database. The server periodically updates its expert database and incorporates new side effect information into its evaluation.

[1421] Generating and notifying advice

[1422] Based on the evaluation results, the server generates specific advice for the user. This advice includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is notified from the server to the user's terminal, and the user can check the notification in the application.

[1423] Example of a prompt:

[1424] "I take medication A at 8:00, medication B at 12:00, and medication C at 18:00. I've developed a headache. What should I do?"

[1425] Generation and notification of medication schedule

[1426] The server generates an optimal medication schedule, taking into account the user's daily routine and existing schedule. For example, it sets a medication schedule based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the user's device, and reminders are set. The device displays the set reminders in a timely manner to prompt the user to take their medication.

[1427] Specific example:

[1428] Medicine A: 8:00

[1429] Medication B: 12:00

[1430] Medication C: 18:00

[1431] Reporting and feedback on adverse events

[1432] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might report, "I developed a headache about two hours after taking medication C." The device sends this information to a server, which stores the received side effect information in a database. Experts review the report and provide the server with any necessary prescription changes or additional advice. The server then notifies the user's device of this advice.

[1433] Specific example:

[1434] The user reports within the app that they developed a headache about two hours after taking medication C, and the device sends this information to the server. The server notifies a specialist, who advises the user to "stop taking medication C and be prescribed a new medication." The server then notifies the user's device of this advice, which is displayed to the user.

[1435] This invention enables users to safely take multiple medications and effectively manage their medications while minimizing the risk of side effects. This is expected to further improve users' health management.

[1436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1437] Step 1: "Enter and submit medication information"

[1438] The user opens a dedicated application. The user enters information about the medication they are currently taking. The input includes the name of the medication, the timing of administration, and the dosage. For example, the user enters "Medication A: After breakfast, 1 tablet" and presses the "Submit" button. The terminal formats the entered information and sends it to the server. The input data is "Medication A, after breakfast, 1 tablet," and the output is the transmission of data to the server. The server saves the received medication information to its database.

[1439] Step 2: "Evaluation of drug interactions and side effects"

[1440] The server compares the received drug information with the drug information in its database. The server then applies a generative AI model to evaluate the risks of drug interactions and side effects from multiple medications. For example, it might detect an interaction risk between drug A and drug B. The evaluation result would be "The interaction risk between drug A and drug B is high." The input is the drug information from the database and the received drug information, and the output is the interaction risk evaluation result. The server saves this evaluation result in its internal database.

[1441] Step 3: "Generating and notifying advice based on evaluation results"

[1442] The server uses the drug interaction and side effect risk assessment results to generate advice for the user. For example, it might generate specific instructions such as, "Take drug A and drug B with a 2-hour interval between doses." The generated advice is sent from the server to the user's device. The input is the assessment results, and the output is the generated advice. The device receives this notification and informs the user via push notification.

[1443] Step 4: "Generating and notifying you of the optimal dosage schedule"

[1444] The server generates an optimal medication schedule based on the user's daily routine and existing schedule. For example, if a user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the medication schedule will be set based on those times. This schedule is notified from the server to the user's terminal. The input is the user's daily routine information, and the output is the generated medication schedule. The terminal sets reminders based on this schedule and displays them in a timely manner.

[1445] Step 5: "Reporting side effects and notifying a professional"

[1446] If a user experiences side effects after taking medication, they report the detailed symptoms within the app. For example, they might input, "I developed a headache two hours after taking medication C," and submit it. The device formats the side effect information and sends it to the server. The server stores this information in a database and notifies a specialist. The input is the user's side effect report, and the output is a notification to a specialist. The specialist considers necessary countermeasures and sends them to the server as feedback. The server then notifies the user of this feedback.

[1447] Specific examples of operation:

[1448] The prompt message reads: "I took medication A at 8:00, medication B at 12:00, and medication C at 18:00. I have developed a headache. Please tell me what to do."

[1449] (Application Example 1)

[1450] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1451] With the increasing prevalence of autonomous vehicles, concerns are being raised about the impact of medication side effects on driving performance. In particular, for drivers taking multiple medications, there is a need for a system that can pre-evaluate how drug interactions and side effects might affect driving ability and suggest safe medication regimens. However, current systems are not effectively evaluating and notifying drivers, failing to adequately protect their health and safety.

[1452] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1453] In this invention, the server includes means for receiving information from the user about medications being taken, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for suggesting a method of taking medication for the driver of an autonomous vehicle to drive safely and displaying a reminder before driving, and means for notifying the risk of side effects while driving. This makes it possible to maintain the driver's health and safety while driving by evaluating drug interactions and side effect risks of the medications the driver is taking in advance and suggesting a safe medication schedule.

[1454] A "user" refers to an individual who uses the system to provide information about the medications they are currently taking.

[1455] An "autonomous vehicle" refers to a vehicle that can operate with minimal driver intervention.

[1456] "Driver" refers to the individual using the self-driving vehicle.

[1457] "Medicine" refers to chemical or biological substances taken for medical purposes.

[1458] A "medication schedule" refers to a timeline set up to ensure that drivers can safely take their medication.

[1459] "Drug interactions" refer to the interactions that occur when taking multiple medications at the same time.

[1460] "Side effects" refer to undesirable effects caused by medication taken.

[1461] A "reminder" refers to a notification used to prompt a user to take a specific action.

[1462] A "specialist" refers to an individual who is well-versed in medicine and pharmacology and can provide appropriate advice to the driver.

[1463] "AI algorithms" refer to artificial intelligence computational methods used to evaluate the risks of drug interactions and side effects.

[1464] A "terminal" refers to an electronic device used by a user to access applications.

[1465] System Configuration

[1466] This system allows users to input information about the medications they are taking, performs a risk assessment based on that information, and provides advice and reminders to autonomous vehicle drivers to ensure safe medication use. The system mainly consists of a server, terminals, and users. The server is central to processing and analyzing the data.

[1467] User medication information input

[1468] Users use an application installed on their smartphones to input information about the medications they are taking. This information includes the name of the medication, when to take it, and the dosage. This information is sent to a server via the application. The server receives this information and stores it in a database.

[1469] Evaluation of drug interactions and side effects

[1470] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. TensorFlow is used as the AI ​​algorithm, and a machine learning model is constructed. For example, if taking drug A and drug B simultaneously carries a risk of side effects, that risk is reflected in the evaluation results. Furthermore, the server refers to the latest information in expert databases, and any new side effect information is added to the evaluation.

[1471] Generating and notifying advice

[1472] Based on the evaluation results, the server generates specific advice for the user. For example, it may include instructions such as, "Take medication A and medication B at least two hours apart." The generated advice is notified to the user's smartphone, and the user can check it through the application.

[1473] Generation and notification of medication schedule

[1474] The server generates an optimal dosage schedule, taking into account the user's daily rhythm and schedule. For example, if a user typically eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server sets appropriate dosage times. The generated schedule is notified to the smartphone, and a reminder is displayed as the dosage time approaches.

[1475] Uses in the operation of autonomous vehicles

[1476] Before using an autonomous vehicle, the system assesses the impact on driving based on information about the driver's medications and suggests appropriate medication regimens. For example, a reminder such as "Avoid taking medication B before driving" may be displayed. This allows the driver to avoid side effects while driving and drive safely.

[1477] Reporting and feedback on adverse events

[1478] If a user experiences side effects while driving, they can report detailed symptoms through the application. This information is immediately sent to the server and stored in the database. When a specialist is notified, they use this information to provide necessary prescription changes or additional advice. The server then notifies the user of this advice on their smartphone.

[1479] Specific example

[1480] User medication information input

[1481] The user opens the application on their smartphone and enters the following medication information:

[1482] Medication A: After breakfast

[1483] Medication B: After lunch

[1484] Medication C: After dinner

[1485] Evaluation of drug interactions and side effects

[1486] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of side effects between drug A and drug B. The latest information in the database is also included in the evaluation.

[1487] Generating and notifying advice

[1488] The server generates advice stating, "Take medication A and medication B with a two-hour interval between doses," and notifies the user's smartphone. The user then checks this within the app.

[1489] Generation and notification of medication schedule

[1490] The server generates the next medication schedule, taking into account the user's daily routine:

[1491] Medicine A: 8:00

[1492] Medication B: 12:00

[1493] Medication C: 18:00

[1494] Smartphones display reminders periodically.

[1495] Reminders for driving autonomous vehicles

[1496] If the driver starts driving at 12:30, the server will display a reminder at 12:20 stating, "Avoid taking medication B while driving."

[1497] Reporting of side effects

[1498] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The server notifies a specialist of this information, and the specialist instructs the server to "discontinue drug C and prescribe a new medication." The server then notifies the user of this instruction on their smartphone.

[1499] Examples of prompts to input into a generative AI model

[1500] Please provide information on any medications the user is taking before driving. Evaluate the risks of drug interactions and side effects for each medication and suggest a safe medication schedule for when the user is driving.

[1501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1502] Step 1:

[1503] The user opens a smartphone application and enters information about the medications they are taking (medication name, timing of administration, dosage, etc.). The input data might include, for example, medication A (after breakfast), medication B (after lunch), and medication C (after dinner). The user enters this information into the application's form and presses the "Submit" button. This sends the medication information to the server.

[1504] Step 2:

[1505] The server retrieves medication information received from the user. The server uses a database management system (e.g., SQLAlchemy) to store this information in a database. Specifically, it stores input data in fields such as medication name, timing of administration, and dosage. This centralizes the management of each user's medication information in the database.

[1506] Step 3:

[1507] The server uses stored drug information and an AI algorithm (e.g., TensorFlow) to evaluate the risks of drug interactions and side effects. The input is drug information data, which the AI ​​algorithm analyzes and outputs a risk assessment result. Specifically, it calculates the risk of side effects when drug A and drug B are taken simultaneously and determines the risk level.

[1508] Step 4:

[1509] The server generates specific advice for the user based on the risk assessment results. This advice may include statements such as, "Take medication A and medication B with a two-hour interval between doses." The server generates this advice in text format and notifies the user's device.

[1510] Step 5:

[1511] The server generates an optimal medication schedule considering the user's daily routine and schedule information. The input is the user's daily routine (e.g., breakfast at 8:00 AM, lunch at 12:00 PM, dinner at 6:00 PM). The generated schedule will include medication A at 8:00 AM, medication B at 12:00 PM, and medication C at 6:00 PM. This schedule is then sent to the smartphone as a text message or reminder.

[1512] Step 6:

[1513] Before the driver uses the self-driving vehicle, the server sends a reminder to their smartphone, such as "Avoid taking medication B before driving." The reminder appears 10 minutes before driving (for example, at 12:20). This allows the driver to avoid side effects while driving.

[1514] Step 7:

[1515] If a user experiences side effects (such as a headache) after taking medication, they report the detailed symptoms within the app. This reported information is sent from the smartphone to a server. The server stores this information in a database and immediately notifies a specialist.

[1516] Step 8:

[1517] Experts review side effect information and provide prescription changes or additional advice as needed. Feedback from experts is sent to a server, which then notifies the user's smartphone. For example, if an expert advises discontinuing medication C and prescribing a new medication, the user will be notified.

[1518] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1519] System Configuration

[1520] One embodiment of the present invention is an interactive concierge system that receives information about medications being taken by the user, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. This system is further optimized by incorporating an emotion engine that recognizes the user's emotions. The main components of the system are a server, a terminal, and the user, and each component works in cooperation with the others.

[1521] User medication information input

[1522] Users use a dedicated application to input information about the medications they are taking. By entering information such as the name of the medication, timing of administration, and dosage, and sending it to the server through the application, each user's individual medication information is stored in the database.

[1523] Evaluation of drug interactions and side effects

[1524] The server uses an AI algorithm to evaluate the risks of drug interactions and side effects based on the received drug information. For example, if it is determined that taking drug A and drug B simultaneously may cause an interaction, that risk will be reflected in the evaluation results. It also refers to expert databases and incorporates any new side effect information into the evaluation.

[1525] Generating and notifying advice

[1526] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as, "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[1527] Generation and notification of medication schedule

[1528] The server generates an optimal medication schedule, taking into account the user's daily routine and schedule. For example, it sets medication times based on information such as the user typically having breakfast at 8:00, lunch at 12:00, and dinner at 18:00. The generated schedule is notified to the device, and a reminder is displayed on the device as the medication time approaches.

[1529] Reporting and feedback on adverse events

[1530] If a user experiences side effects after taking medication, they can report detailed symptoms within the app. The side effect information entered by the user is sent to a server via their device and stored in a database. This information is then forwarded to a medical professional who reviews it and provides the server with necessary prescription changes or additional advice. The server then notifies the user of this advice on their device.

[1531] Embedding an emotion engine

[1532] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's voice and facial expressions to evaluate their current emotional state. For example, it can determine whether the user is stressed or relaxed based on their voice tone and facial expressions.

[1533] Adjusting advice based on emotional state

[1534] Based on the emotional state recognized by the emotion engine, the server adjusts the content of its advice. For example, if the user is feeling stressed, it will use kinder and more relaxing language. It will also suggest a chat function with an expert as needed, allowing the user to directly address the problems and questions they are currently experiencing.

[1535] Specific example

[1536] 1. User medication information entry

[1537] The user opens the app and enters the following medication information:

[1538] Medication A: After breakfast

[1539] Medication B: After lunch

[1540] Medication C: After dinner

[1541] 2. Evaluation of drug interactions and side effects

[1542] The server uses AI to evaluate drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B. It also refers to information in the database and adds any new side effect information to the evaluation.

[1543] 3. Generating and notifying advice

[1544] The server generates advice stating, "Take medication A and medication B with a 2-hour interval between doses," and notifies the user's device. The user then checks this information within the app.

[1545] 4. Generation and notification of medication schedule

[1546] The server generates the next medication schedule, taking into account the user's daily routine:

[1547] Medicine A: 8:00

[1548] Medication B: 12:00

[1549] Medication C: 18:00

[1550] The device will periodically display reminders.

[1551] 5. Reporting and feedback on adverse events

[1552] If a user experiences a headache after taking drug C, they report it within the app as "Headache occurred about 2 hours after taking drug C." The device sends this information to the server, which notifies a specialist of the side effect information. The specialist then provides the server with an instruction to "discontinue drug C and prescribe a new medication," and the server notifies the user's device of this instruction.

[1553] 6. Example of how the emotion engine works

[1554] When a user submits data entered into the app, an emotion engine analyzes their voice tone and facial expressions. If the server determines that the user is stressed, it sends a notification in gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function where users can consult directly with a specialist if needed, supporting them in taking their medication with peace of mind.

[1555] In this way, by incorporating an emotion engine, a system that can improve the user experience can be realized.

[1556] The following describes the processing flow.

[1557] Step 1:

[1558] The user launches the app and enters information about the medications they are currently taking. The user enters the name of the medication, the timing of administration, and the dosage, and then presses the save button.

[1559] Step 2:

[1560] The terminal sends the medication information entered by the user to the server using a RESTful API. The server stores the received information in a database.

[1561] Step 3:

[1562] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information in the database. For example, it checks whether there is a risk of interaction between drug A and drug B.

[1563] Step 4:

[1564] The server generates advice for the user based on the evaluation results. For example, it might create specific advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[1565] Step 5:

[1566] The server generates advice and notifies the terminal. The terminal receives the advice and displays it to the user.

[1567] Step 6:

[1568] The server generates an optimal medication schedule based on the user's daily routine and schedule information. For example, if the user eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the server will set the timing for taking each medication accordingly.

[1569] Step 7:

[1570] The server generates a medication schedule and notifies the device. The device sets a reminder and sends a notification when it is time to take the medication.

[1571] Step 8:

[1572] If a user experiences side effects after taking medication, they can enter details into the side effect report form within the app. For example, they might write, "I developed a headache about two hours after taking medication C."

[1573] Step 9:

[1574] The device sends side effect information to the server. The server stores the received side effect information in a database and notifies a specialist.

[1575] Step 10:

[1576] Experts review the side effect information and input necessary instructions and advice into the system. For example, they might input, "Discontinue taking drug C and prescribe a new drug."

[1577] Step 11:

[1578] The server receives expert feedback and notifies the user's device. The device then displays the feedback to the user.

[1579] Step 12:

[1580] An emotion engine, which recognizes the user's emotions, analyzes the user's voice and facial expressions. The emotion engine evaluates the user's emotional state (e.g., stress or anxiety) and sends that information to the server.

[1581] Step 13:

[1582] The server adjusts the advice based on the emotional state received from the emotion engine. For example, if the user is feeling stressed, it will use kinder and more relaxing language.

[1583] Step 14:

[1584] The server notifies the terminal with advice that reflects the user's emotional state. The terminal receives the advice and displays it to the user.

[1585] Step 15:

[1586] The emotion engine evaluates the user's emotional state, and if stress or anxiety levels are high, the server sends a notification to the device suggesting a chat function with an expert.

[1587] Step 16:

[1588] The device will display a suggestion for a chat function with an expert to the user, allowing the user to start a chat.

[1589] (Example 2)

[1590] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1591] For users taking multiple medications, there is a need to accurately assess the risks of drug interactions and side effects, and to provide appropriate advice and dosage schedules. Furthermore, there is a need for a system that can quickly respond to side effects and stress experienced by users and provide individually optimized advice. However, conventional systems have struggled to adequately meet these needs.

[1592] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1593] In this invention, the server includes means for receiving information from the user about the medications they are taking, means for evaluating drug interactions and side effects of the medications being taken, means for generating and notifying the user of advice based on the evaluation results, means for generating an optimal medication schedule based on the user's lifestyle, means for notifying the user's terminal of the generated medication schedule and setting a reminder, means for receiving and storing reports of side effects from the user, means for notifying experts of the side effect information, means for recognizing the user's emotions and adjusting advice based on their emotional state, and means for suggesting a chat function with experts according to the emotional state. This not only optimizes the user's medication management but also enables personalized support that takes their emotional state into consideration.

[1594] A "user" is an individual who uses the system to input information about medication use and side effects, and to receive advice and notifications.

[1595] A "server" is a central processing unit that receives data sent from users, stores it in a database, and performs tasks such as evaluating drug pairings, generating advice, and creating schedules.

[1596] A "terminal" is a device that a user directly operates, an electronic device used by the user to input information or receive notifications from a server.

[1597] An "emotion engine" is a software component that analyzes a user's voice and facial expressions to evaluate their current emotional state.

[1598] A "specialist" is a professional who possesses advanced knowledge about the medications and side effects that a user is taking, and who can provide appropriate advice and feedback.

[1599] A "reminder" is a feature used to notify users of specific times or tasks.

[1600] "AI algorithms" refer to machine learning models and other advanced computational methods used to analyze a user's medication information and assess the risk of drug interactions and side effects.

[1601] "Drug interactions" refer to the phenomenon where multiple medications taken simultaneously affect each other.

[1602] "Side effects" refer to undesirable physical or mental reactions that occur as a result of taking medication.

[1603] A "chat function" is a communication feature that allows users and experts to interact in real time.

[1604] A "database" is an information management system used to systematically store and manage data such as a user's medication information, side effect information, and emotional state.

[1605] "Lifestyle rhythm" refers to the temporal patterns of a user's daily life, such as their activity times and meal timings.

[1606] Modes for carrying out the invention

[1607] This invention is an interactive concierge system in which the user inputs information about the medications they are taking, evaluates the risks of drug interactions and side effects, and proposes an optimal medication schedule. The system incorporates an emotion engine that recognizes the user's emotions for further optimization. The main components of the system are a server, a terminal, and the user, which work together in cooperation with each other.

[1608] User medication information input

[1609] Users use a dedicated application to input information about the medications they are taking. Input fields include the name of the medication, timing of administration, and dosage. Once the input is complete, the device sends the information to the server.

[1610] As a concrete example, the user enters the following:

[1611] Medication A: After breakfast

[1612] Medication B: After lunch

[1613] Medication C: After dinner

[1614] Drug information transmission and storage

[1615] The terminal sends user input information to the server, and the server stores the received information in a database. During this process, validation is also performed to check the accuracy and consistency of the data.

[1616] Evaluation of drug interactions and side effects

[1617] The server uses AI algorithms to evaluate drug interactions and side effect risks based on drug information stored in a database. For example, if drug A and drug B may interact when taken simultaneously, that risk is reflected in the evaluation results. New side effect information is obtained from expert databases and added to the evaluation.

[1618] In a specific example, the server evaluates drug interactions between drugs A, B, and C, and confirms that there is a risk of interaction between drug A and drug B.

[1619] Generating and notifying advice

[1620] Based on the evaluation results, the server generates advice for the user. This advice includes specific instructions, such as "Take medication A and medication B with a two-hour interval between them." The generated advice is notified to the user's device, and the user can check it through the application.

[1621] Generation and notification of medication schedule

[1622] The server generates an optimal dosage schedule based on the user's daily routine and schedule information. For example, based on information that the user usually eats breakfast at 8:00, lunch at 12:00, and dinner at 18:00, the following dosage schedule is generated:

[1623] Medicine A: 8:00

[1624] Medication B: 12:00

[1625] Medication C: 18:00

[1626] The generated schedule will be notified to the device, and a reminder will be set.

[1627] Reporting and feedback on adverse events

[1628] If a user experiences side effects after taking medication, they report the symptoms within the app. This information is sent via the device to a server, which stores it in a database. The server then notifies a specialist of this information and awaits their feedback.

[1629] For example, if a user reports that they developed a headache about two hours after taking drug C, the server will receive instructions from a specialist to "stop taking drug C and prescribe a new drug," and the server will then notify the user's device of these instructions.

[1630] How the emotion engine works

[1631] The device passes data sent when a user uses the app to the emotion engine. The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state, determining whether the user is stressed or relaxed.

[1632] Adjusting advice based on emotional state

[1633] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, if the user is feeling stressed, it will use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It also offers a chat function with experts to directly address the user's problems and questions.

[1634] Example of a prompt

[1635] By inputting prompts like the following into the AI ​​model, it can provide advice in a natural conversational format:

[1636] "The user is currently taking the following medications: Medication A (after breakfast), Medication B (after lunch), and Medication C (after dinner). Medications A and B have a risk of interaction and should be taken two hours apart. Please propose an optimal medication schedule that takes the user's lifestyle into consideration. Also, if the user is experiencing stress, please use calming language."

[1637] Using such detailed prompts allows the generative AI model to provide users with appropriate and helpful advice.

[1638] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1639] System program processing flow

[1640] Step 1: User enters medication information

[1641] The user opens a dedicated application and enters information about the medication they are taking (such as the name of the medication, timing of administration, and dosage).

[1642] Input: Information such as the name of the drug, timing of administration, and dosage.

[1643] Output: The entered medication information is saved to the terminal.

[1644] Step 2: Send and store medication information

[1645] The terminal sends user input information to the server. The server stores the received information in a database.

[1646] Input: Medication information entered by the user

[1647] Data processing: Data validation and duplicate checking

[1648] Output: Validated drug information is saved to the database.

[1649] Step 3: Evaluation of drug interactions and side effects

[1650] The server uses an AI algorithm to evaluate drug interactions and side effect risks based on drug information stored in a database. It also obtains the latest side effect information from expert databases and incorporates it into the evaluation.

[1651] Input: Drug information stored in the database, side effect information from expert databases.

[1652] Data processing: Risk assessment using AI algorithms

[1653] Output: Drug interaction risk and side effect risk assessment results

[1654] Step 4: Generate and notify advice

[1655] The server generates advice for the user based on the evaluation results. This includes specific instructions such as, "Take medication A and medication B with a two-hour interval between doses." The generated advice is then notified to the user's device.

[1656] Input: Evaluation results of drug interactions and side effects

[1657] Data processing: Generating advice using AI models.

[1658] Output: The generated advice is notified to the device.

[1659] Step 5: Generate and notify about the medication schedule.

[1660] The server generates an optimal medication schedule based on the user's lifestyle and schedule information. The generated schedule is notified to the user's device, and reminders are set.

[1661] Input: User's daily routine information (breakfast, lunch, dinner times, etc.)

[1662] Data processing: Generating medication schedules

[1663] Output: The generated medication schedule is notified to the device, and a reminder is set.

[1664] Step 6: Reporting and providing feedback on side effects

[1665] If a user experiences side effects after taking medication, they report the symptoms within the app. This report is sent via the device to a server, which stores it in a database and simultaneously notifies a specialist. The specialist's feedback is then provided to the server and notified to the user's device.

[1666] Input: Detailed information on side effects reported by the user

[1667] Data processing: Storage of adverse event information and notification to experts.

[1668] Output: Expert feedback is sent to the user's device via the server.

[1669] Step 7: Operating the Emotion Engine

[1670] When a user uses the app, the emotion engine analyzes their voice tone and facial expressions to assess their current emotional state.

[1671] Input: User's voice tone and facial expression data

[1672] Data processing: Analysis of emotional states using an emotion engine.

[1673] Output: Emotional state evaluation results

[1674] Step 8: Adjusting advice based on emotional state

[1675] The server adjusts the advice given to the user based on the evaluation results of the emotion engine. For example, it might use gentle language such as, "Please relax. We recommend taking medication B at 12:00." It can also provide a chat function with experts.

[1676] Input: Emotional state evaluation result

[1677] Data processing: Adjusting advice from generative AI models

[1678] Output: Adjusted advice is sent to the device.

[1679] (Application Example 2)

[1680] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1681] In modern society, it is crucial for users to manage the proper use of medication and the safe consumption of food to improve their quality of life. However, technology that provides users with optimal advice considering their individual physical condition, emotional state, allergy information, and lifestyle is not yet sufficiently developed. Furthermore, the lack of support that takes into account the user's emotional state can lead to stress and anxiety. This invention is designed to solve these problems.

[1682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1683] In this invention, the server includes means for receiving information from the user about medications or foods being taken; means for evaluating combinations of medications or foods being taken, as well as side effects and nutritional balance; means for generating and notifying the user of advice based on the evaluation results; means for generating an optimal medication or meal schedule based on the user's lifestyle; means for notifying the user's terminal of the generated medication or meal schedule and setting a reminder; means for receiving and storing reports of side effects or feedback from the user; means for notifying experts of the side effect information or feedback; means for evaluating the user's emotional state using an emotion engine; and means for adjusting the content of advice based on the evaluated emotional state. This enables the user to receive safe and appropriate advice based on information about medication and food intake, and to provide flexible support according to their emotional state.

[1684] A "user" refers to someone who uses this system to input information such as medications they are taking, foods they eat, their lifestyle, and their emotional state, and then receives evaluation results and advice.

[1685] "Medications currently being taken" refers to any medications or supplements that the user is currently using, including information that is entered into the system.

[1686] "Ingredients" refers to the food and ingredients used in dishes that the user is considering consuming, and includes allergy information and nutritional balance.

[1687] "Lifestyle rhythm" refers to the timing and patterns of a user's daily activities, including meal times, sleep times, and medication times.

[1688] "Emotional state" refers to the user's current psychological feelings and mood, and is evaluated based on factors such as voice tone and facial expressions.

[1689] "Evaluation" refers to the process by which the system analyzes and diagnoses drug and food combinations, side effects, nutritional balance, etc., based on information received from the user.

[1690] "Advice" refers to specific instructions and suggestions provided to the user based on the evaluation results, including things like the timing of taking medication or eating.

[1691] A "schedule" refers to the time and order that the system has determined to be optimal for the user to take medication or eat meals, and it is provided along with reminders.

[1692] A "reminder" is a mechanism that notifies the user of medication or meal times, and is set by the system.

[1693] "Feedback" refers to information that users report to the system regarding side effects or impressions they experienced after taking medication or eating.

[1694] "Experts" refer to medical and nutritional professionals who provide additional advice or prescription changes based on user information and feedback regarding side effects.

[1695] An "emotion engine" refers to an algorithm or software that analyzes a user's voice, facial expressions, etc., to evaluate their current emotional state.

[1696] "Adjusting" means modifying and adapting the advice given to the user based on their evaluated emotional state.

[1697] System Configuration

[1698] To implement the present invention, a system comprising the following components is required. The system consists of a server, a terminal, and a user, each working in cooperation with the others.

[1699] Program generation

[1700] The system's main function is to receive information from users about the medications and foods they are currently taking, and then generate and notify them of advice and schedules based on that information. By using an emotion engine, it is possible to provide support tailored to the user's emotional state.

[1701] Hardware to use

[1702] Smartphones: The primary devices on which user interfaces and emotion engines operate.

[1703] Server: Responsible for user data processing, advice generation, and database management.

[1704] Software to use

[1705] AI framework: TensorFlow (for building AI algorithms)

[1706] Database: MySQL (for managing user information and ingredient data)

[1707] Emotion recognition engines: Amazon Rekognition (face recognition) and Google Cloud Speech-to-Text (speech recognition)

[1708] Data processing and data calculation

[1709] 1. User Information Input: Users input information about medications they are taking, foods they eat, their lifestyle, and their emotional state through a dedicated app. This information is sent from the device to the server and stored in a database.

[1710] 2. Evaluation and Advice Generation: Based on the received information, the server uses an AI algorithm to evaluate drug and food combinations, side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[1711] 3. Schedule Generation: The server considers the user's daily routine and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[1712] 4. Feedback Management: When a user enters feedback after taking medication or eating, the device sends that information to the server and stores it in the database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[1713] 5. How the Emotion Engine Works: The emotion engine analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[1714] Specific example

[1715] 1. User enters medication information: The user opens the app and enters "Medication A after breakfast," "Medication B after lunch," and "Medication C after dinner." This information is sent to the server.

[1716] 2. Evaluation and advice generation: The server evaluates the information on drug A, drug B, and drug C and generates advice such as, "Take drug A and drug B with a 2-hour interval between doses."

[1717] 3. Schedule generation: The server takes the user's daily routine into consideration and generates and notifies the user of a medication schedule, such as taking medication A at 8:00, medication B at 12:00, and medication C at 18:00.

[1718] 4. Feedback Management: A user enters feedback such as "I experienced a headache after taking drug C," and this information is sent to the server. The server notifies a specialist, and the specialist's instructions are then re-notified to the user.

[1719] 5. Emotion Engine Operation: While the user is using the app, the emotion engine will operate and, for example, if it determines that the user is feeling stressed, it will notify the user with a message such as, "Please relax. We recommend taking medication B at 12:00."

[1720] Example of a prompt

[1721] "Since the user is feeling stressed, please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[1722] In this way, the overall system configuration and operation of the invention enable proper management support for medicines and food ingredients for users.

[1723] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1724] Step 1:

[1725] User input

[1726] Users input information about medications and foods they are currently taking, allergy information, lifestyle, and emotional state through a dedicated application. The entered information is sent from their smartphone to a server and stored in a database.

[1727] Specific actions: The user opens the app and enters information such as "Take medicine A after breakfast," "Take medicine B after lunch," and "Take medicine C after dinner." They also fill in detailed information about their daily routine, such as "I have an egg allergy" and "I usually eat breakfast at 8:00."

[1728] Step 2:

[1729] Evaluation and advice generation

[1730] Based on the received information, the server uses an AI algorithm (TensorFlow) to evaluate combinations of drugs and foods, potential side effects, and nutritional balance. Based on the evaluation results, it generates specific advice and notifies the user's device.

[1731] Input: User's medications, food items, and allergy information retrieved from the database by the server.

[1732] Data processing: AI algorithms are used to assess the risk of drug interactions and food allergies.

[1733] Output: Specific advice based on the evaluation results (e.g., "Take drug A and drug B with a 2-hour interval between doses").

[1734] Step 3:

[1735] Schedule generation

[1736] The server takes the user's lifestyle into consideration and generates an optimal medication or meal schedule. The generated schedule is notified to the user's device and displayed as a reminder.

[1737] Input: User's daily routine information (e.g., "Breakfast at 8:00," "Lunch at 12:00")

[1738] Data processing: Calculation of an optimal timetable based on daily routines.

[1739] Output: A schedule such as "Take medicine A at 8:00", "Take medicine B at 12:00", and "Take medicine C at 18:00".

[1740] Step 4:

[1741] Feedback Management

[1742] Users enter feedback after taking medication or eating. This information is sent from the device to the server and stored in a database. The server notifies experts of the side effect information and feedback, and then re-notifies the user of the expert's feedback.

[1743] Input: User feedback (e.g., "I experienced a headache after taking drug C")

[1744] Data processing: Feedback information is forwarded to experts, and the user is notified of the experts' responses.

[1745] Output: Instructions from a specialist (e.g., "Discontinue taking drug C and prescribe a new drug")

[1746] Step 5:

[1747] How the emotion engine works

[1748] The emotion engine (Amazon Rekognition and Google Cloud Speech-to-Text) analyzes the user's voice tone and facial expressions to assess their current emotional state. Based on the assessment, it adjusts the advice provided and, if necessary, suggests a chat function with an expert.

[1749] Input: User's voice and facial expression data

[1750] Data processing: Evaluation of emotional states using an emotion engine.

[1751] Output: Adjustment of advice based on emotional state (e.g., "Please relax. We recommend taking medication B at 12:00.")

[1752] Specific operation: While the user is using the app, the camera and microphone are active, and the emotion engine analyzes the user's stress level in real time. For example, if the server determines that the user is feeling stressed, it will send a notification in gentle language such as, "Why not try a relaxing herbal tea today?"

[1753] Example of a prompt

[1754] "The user is feeling stressed, so please suggest relaxing foods and drinks. For example, chamomile tea or a light salad."

[1755] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1756] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1757] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1758] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1759] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1760] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1761] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1762] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1763] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1764] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1765] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1766] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1767] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1768] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1769] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing pr...

Claims

1. A means of receiving information from the user about the medications they are currently taking, A means for evaluating drug interactions and side effects of medications being taken, A means of generating and notifying users of advice based on evaluation results, A means for generating an optimal medication schedule based on the user's lifestyle, A means of notifying the user's device of the generated medication schedule and setting a reminder, A means of receiving and storing adverse event reports from users, A system that includes a means of notifying experts of adverse event information.

2. A means of generating and notifying users of advice based on evaluation results is, The system according to claim 1, which includes a warning about drug interactions with specific medications.

3. A means of notifying experts of side effect information is The system according to claim 1, comprising means for notifying the user of feedback from experts.

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