System

Abstract

An object of a system according to an embodiment is to assist a user in taking medicine appropriately.SOLUTION: A system according to an embodiment includes a medication information input unit, a reminder providing unit, and a health data tracking unit. The drug information input unit inputs drug information and a dosing schedule from a user. The reminder providing unit provides a reminder and a warning on the basis of the information input by the medication information input unit. The health data tracker tracks changes in the user's health data and symptoms.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that managing medication intake is complicated, making it difficult for users to take their medication appropriately.

[0005] The system according to the embodiment aims to support users in taking their medicines appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a medication information input unit, a reminder providing unit, and a health data tracking unit. The medication information input unit receives medication information and a dosing schedule from a user. The reminder providing unit provides reminders and alerts based on the information input by the medication information input unit. The health data tracking unit tracks changes in the user's health data and symptoms. [Effects of the Invention]

[0007] The system according to the embodiment can assist the user in taking medicines appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A medication management system according to an embodiment of the present invention allows a user to input information about prescribed medications and their dosage schedules, and a generating AI provides reminders and warnings about medication dosage, timing, interactions, etc., and tracks changes in the user's health data and symptoms to provide information about the appropriateness and effectiveness of medications. This allows the medication management system to enable users to properly manage medication information and maintain their health.

[0029] A medication management system according to an embodiment includes a medication information input unit, a reminder providing unit, and a health data tracking unit. The medication information input unit receives medication information and a medication schedule from a user. For example, the user inputs the medication name, dosage, and dosage method. The user can also input a medication schedule. For example, the user inputs a specific medication schedule, such as "Take one tablet after breakfast and one tablet after dinner." The reminder providing unit provides reminders and alerts based on the information input by the medication information input unit. For example, the reminder providing unit sends a notification such as "It's time to take your medication after breakfast." The reminder providing unit also provides alerts regarding drug interactions. For example, the reminder providing unit displays a warning such as "Taking this medication with other medications may cause side effects." The health data tracking unit tracks changes in the user's health data and symptoms. For example, the user inputs health data such as body temperature, blood pressure, and heart rate. The AI ​​then analyzes the data and provides information regarding the appropriateness and effectiveness of the medication. For example, the AI ​​provides information such as "Your body temperature has dropped since you started taking the medication." This allows the medication management system according to an embodiment to properly manage medication information and maintain a healthy state.

[0030] When a user enters drug information, the drug information input unit provides an auto-completion function using the generation AI, which can predict and suggest drug names and dosing schedules. For example, when a user enters part of a drug name, the generation AI automatically completes candidate drug names based on that information. For example, if a user enters "aspi," candidates such as "aspirin" and "aspiromycin" are displayed. Furthermore, when a user enters part of a dosing schedule, the generation AI automatically completes candidate schedules based on that information. For example, if a user enters "after breakfast," candidates such as "one tablet after breakfast, one tablet after dinner" are displayed. This reduces the effort required for users to enter drug information and provides accurate information.

[0031] When a user inputs drug information, the drug information input unit allows the generation AI to propose an optimal drug schedule based on past drug-taking history and health data. For example, when a user inputs drug information, the drug information input unit allows the generation AI to analyze the past drug-taking history and propose an optimal drug schedule. For example, it may propose the optimal timing of taking the drug based on data from when the same drug was taken in the past. The drug information input unit also proposes an optimal drug schedule based on the user's health data. For example, it may analyze the user's body temperature and blood pressure data and propose the optimal timing of taking the drug. This makes it possible to propose an optimal drug schedule by utilizing the user's past data.

[0032] The drug information input unit uses voice recognition technology to input drug information, allowing the user to input drug information by voice. The drug information input unit allows the user to input drug names and dosing schedules by voice, for example. For example, if the user inputs "one aspirin after breakfast and one after dinner" by voice, the app converts the information into text and saves it. In addition, when the user inputs drug information by voice, the drug information input unit uses speech recognition technology to analyze the input content and provide accurate information. For example, if the user pronounces "aspirin," the generation AI analyzes the voice and inputs the correct drug name. This allows the user to input drug information by voice.

[0033] The drug information input unit inputs drug information by scanning QR codes and barcodes, and can obtain drug information directly from the drug package. The drug information input unit automatically obtains drug information, for example, when a user scans a QR code or barcode on a drug package. For example, when a QR code is scanned using a smartphone camera, the drug name and dosing schedule are automatically entered. The drug information input unit also automatically obtains drug information when a user scans a barcode on a drug package. For example, when a barcode is scanned using a barcode reader, the drug name and dosing schedule are automatically entered. This allows the user to obtain information directly from the drug package.

[0034] The reminder providing unit optimizes reminder notifications to match the user's lifestyle rhythm, and the generation AI can send notifications at the optimal timing. The reminder providing unit, for example, analyzes the user's lifestyle rhythm and sends reminders at the optimal timing. For example, based on the time the user takes breakfast, it sends a notification such as "It's time to take your medicine after breakfast." The reminder providing unit also analyzes the user's lifestyle rhythm and sends reminders at the optimal timing. For example, based on the time the user takes their medicine before going to bed, it sends a notification such as "It's time to take your medicine before going to bed." This allows reminders to be provided at the optimal timing to match the user's lifestyle rhythm.

[0035] The reminder providing unit can include detailed information about the effects and side effects of the medication in the reminder notification and provide the information in a form that is easy for the user to understand. For example, the reminder providing unit includes detailed information about the effects and side effects of the medication in the reminder notification. For example, it provides information such as, "This medication is effective in lowering blood pressure, but may cause dizziness as a side effect." The reminder providing unit also includes detailed information about the effects and side effects of the medication in the reminder notification. For example, it provides information such as, "This medication is effective in relieving pain, but may cause drowsiness as a side effect." This makes it easier for the user to understand the effects and side effects of the medication.

[0036] The reminder providing unit can link reminder notifications with devices such as a smartwatch and a smart speaker, allowing the user to receive notifications on multiple devices. For example, the reminder providing unit links reminder notifications with a smartwatch, allowing the user to receive notifications on their wrist. For example, a notification such as "It's time to take your medicine after breakfast" is displayed on the smartwatch. The reminder providing unit also links reminder notifications with a smart speaker, allowing the user to receive notifications by voice. For example, a notification such as "It's time to take your medicine after breakfast" is played by voice from the smart speaker. This allows the user to receive reminder notifications on multiple devices.

[0037] The reminder providing unit can link reminder notifications with a calendar app and automatically add them to a schedule. The reminder providing unit, for example, links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after breakfast" is automatically added to a calendar. The reminder providing unit also links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after dinner" is automatically added to a calendar. In this way, reminders can be added to a schedule in cooperation with the user's calendar app.

[0038] The health data tracking unit can acquire data from the wearable device in real time and have the generating AI analyze the data to automate the input of health data. The health data tracking unit, for example, acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires heart rate and step count data from a smartwatch and analyzes the health condition. The health data tracking unit also acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires data from a fitness tracker and analyzes the health condition. This allows data to be acquired from the wearable device in real time and automates the input of health data.

[0039] The health data tracking unit allows the generation AI to provide the user with specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the health data tracking unit allows the generation AI to provide the user with specific health advice based on the results of the health data analysis. For example, the generation AI may display advice such as "It would be good to exercise for 30 minutes every day." The health data tracking unit also allows the generation AI to provide the user with suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the generation AI may display a suggestion such as "Reconsider the balance of your diet." This allows the generation AI to provide specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis.

[0040] The health data tracking unit stores health data in the cloud and allows it to be shared with doctors and family members, thereby enabling more comprehensive health management. The health data tracking unit, for example, stores health data in the cloud and shares it with doctors, thereby enabling more comprehensive health management. For example, doctors can periodically check the data and provide appropriate advice. The health data tracking unit also stores health data in the cloud and shares it with family members, thereby enabling more comprehensive health management. For example, family members can check the user's health data and provide support. In this way, comprehensive health management can be achieved by storing health data in the cloud and sharing it with doctors and family members.

[0041] The health data tracking unit visualizes health data and allows the user to intuitively understand it with graphs and charts. The health data tracking unit, for example, visualizes health data with graphs and charts to allow the user to intuitively understand it. For example, changes in body temperature and heart rate are displayed with a line graph. The health data tracking unit also visualizes health data to allow the user to intuitively understand it. For example, changes in blood pressure are displayed with a bar graph. In this way, the health data is visualized to allow the user to intuitively understand it.

[0042] The reminder providing unit can link reminder notifications with devices such as a smartwatch and a smart speaker, allowing the user to receive notifications on multiple devices. For example, the reminder providing unit links reminder notifications with a smartwatch, allowing the user to receive notifications on their wrist. For example, a notification such as "It's time to take your medicine after breakfast" is displayed on the smartwatch. The reminder providing unit also links reminder notifications with a smart speaker, allowing the user to receive notifications by voice. For example, a notification such as "It's time to take your medicine after breakfast" is played by voice from the smart speaker. This allows the user to receive reminder notifications on multiple devices.

[0043] The reminder providing unit can link reminder notifications with a calendar app and automatically add them to a schedule. The reminder providing unit, for example, links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after breakfast" is automatically added to a calendar. The reminder providing unit also links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after dinner" is automatically added to a calendar. In this way, reminders can be added to a schedule in cooperation with the user's calendar app.

[0044] The health data tracking unit can acquire data from the wearable device in real time and have the generating AI analyze the data to automate the input of health data. The health data tracking unit, for example, acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires heart rate and step count data from a smartwatch and analyzes the health condition. The health data tracking unit also acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires data from a fitness tracker and analyzes the health condition. This allows data to be acquired from the wearable device in real time and automates the input of health data.

[0045] The health data tracking unit allows the generation AI to provide the user with specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the health data tracking unit allows the generation AI to provide the user with specific health advice based on the results of the health data analysis. For example, the generation AI may display advice such as "It would be good to exercise for 30 minutes every day." The health data tracking unit also allows the generation AI to provide the user with suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the generation AI may display a suggestion such as "Reconsider the balance of your diet." This allows the generation AI to provide specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis.

[0046] The health data tracking unit visualizes health data and allows the user to intuitively understand it with graphs and charts. The health data tracking unit, for example, visualizes health data with graphs and charts to allow the user to intuitively understand it. For example, changes in body temperature and heart rate are displayed with a line graph. The health data tracking unit also visualizes health data to allow the user to intuitively understand it. For example, changes in blood pressure are displayed with a bar graph. In this way, the health data is visualized to allow the user to intuitively understand it.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] Furthermore, when a user enters drug information into the drug information input section, the drug management system can have the generation AI suggest an optimal drug-taking schedule based on past drug-taking history and health data. For example, when a user enters drug information, the generation AI analyzes the user's past drug-taking history and suggests the optimal timing for taking the drug. It also suggests the optimal timing for taking the drug based on data from when the same drug was taken in the past. It also suggests an optimal drug-taking schedule based on the user's health data. For example, it analyzes the user's body temperature and blood pressure data and suggests the optimal timing for taking the drug. In this way, it is possible to suggest an optimal drug-taking schedule by utilizing the user's past data.

[0049] The medication management system can also use voice recognition technology to input medication information into the medication information input unit, allowing users to input medication information by voice. For example, users can input medication names and dosing schedules by voice. For example, if a user voice-inputs "one aspirin after breakfast and one after dinner," the app converts the information into text and saves it. The generation AI also uses voice recognition technology to analyze the input content and provide accurate information. For example, if a user pronounces "aspirin," the generation AI analyzes the voice and inputs the correct medication name. This allows users to input medication information by voice.

[0050] The medication management system also allows users to input medication information into the medication information input section by scanning QR codes and barcodes, and obtain medication information directly from medication packages. For example, a user can automatically obtain medication information by scanning a QR code or barcode on a medication package. When a user scans a QR code using a smartphone camera, the medication name and dosing schedule are automatically entered. When a user scans a barcode using a barcode reader, the medication name and dosing schedule are automatically entered. This allows users to obtain information directly from medication packages.

[0051] The medication management system further allows the reminder provider to optimize reminder notifications to match the user's lifestyle, with the generation AI sending notifications at the optimal timing. For example, the system analyzes the user's lifestyle and sends reminders at the optimal timing. Based on the time the user eats breakfast, it sends a notification such as "It's time to take your medicine after breakfast." Also, based on the time the user takes their medicine before going to bed, it sends a notification such as "It's time to take your medicine before going to bed." This allows reminders to be provided at the optimal timing to match the user's lifestyle.

[0052] The medication management system can further configure the reminder providing unit to include detailed information about the medication's effects and side effects in the reminder notification and provide it in a form that is easy for the user to understand. For example, detailed information about the medication's effects and side effects can be included in the reminder notification. Information such as "This medication has the effect of lowering blood pressure, but may cause dizziness as a side effect" can be provided. Also, information such as "This medication has the effect of relieving pain, but may cause drowsiness as a side effect" can be provided. This makes it easier for the user to understand the medication's effects and side effects.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The drug information input unit inputs drug information and a dosage schedule from the user. For example, the user inputs the drug name, dosage, and dosage method. The user can also input a dosage schedule. For example, the user inputs a specific dosage schedule such as "one tablet after breakfast and one tablet after dinner." Step 2: The reminder providing unit provides reminders and warnings based on the information input by the medication information input unit. For example, it sends a notification such as "It's time to take your medicine after breakfast." It also provides warnings about drug interactions. For example, it displays a warning such as "This medicine may cause side effects when taken with other medicines." Step 3: The health data tracking unit tracks changes in the user's health data and symptoms. For example, the user enters health data such as body temperature, blood pressure, and heart rate. The generative AI then analyzes the data and provides information on the appropriateness and effectiveness of medication. For example, it may provide information such as, "Your body temperature has dropped since you started taking the medication."

[0055] (Example 2) A medication management system according to an embodiment of the present invention allows a user to input information about prescribed medications and their dosage schedules, and a generating AI provides reminders and warnings about medication dosage, timing, interactions, etc., and tracks changes in the user's health data and symptoms to provide information about the appropriateness and effectiveness of medications. This allows the medication management system to enable users to properly manage medication information and maintain their health.

[0056] A medication management system according to an embodiment includes a medication information input unit, a reminder providing unit, and a health data tracking unit. The medication information input unit receives medication information and a medication schedule from a user. For example, the user inputs the medication name, dosage, and dosage method. The user can also input a medication schedule. For example, the user inputs a specific medication schedule, such as "Take one tablet after breakfast and one tablet after dinner." The reminder providing unit provides reminders and alerts based on the information input by the medication information input unit. For example, the reminder providing unit sends a notification such as "It's time to take your medication after breakfast." The reminder providing unit also provides alerts regarding drug interactions. For example, the reminder providing unit displays a warning such as "Taking this medication with other medications may cause side effects." The health data tracking unit tracks changes in the user's health data and symptoms. For example, the user inputs health data such as body temperature, blood pressure, and heart rate. The AI ​​then analyzes the data and provides information regarding the appropriateness and effectiveness of the medication. For example, the AI ​​provides information such as "Your body temperature has dropped since you started taking the medication." This allows the medication management system according to an embodiment to properly manage medication information and maintain a healthy state.

[0057] When a user enters drug information, the drug information input unit provides an auto-completion function using the generation AI, which can predict and suggest drug names and dosing schedules. For example, when a user enters part of a drug name, the generation AI automatically completes candidate drug names based on that information. For example, if a user enters "aspi," candidates such as "aspirin" and "aspiromycin" are displayed. Furthermore, when a user enters part of a dosing schedule, the generation AI automatically completes candidate schedules based on that information. For example, if a user enters "after breakfast," candidates such as "one tablet after breakfast, one tablet after dinner" are displayed. This reduces the effort required for users to enter drug information and provides accurate information.

[0058] When a user inputs drug information, the drug information input unit allows the generation AI to propose an optimal drug schedule based on past drug-taking history and health data. For example, when a user inputs drug information, the drug information input unit allows the generation AI to analyze the past drug-taking history and propose an optimal drug schedule. For example, it may propose the optimal timing of taking the drug based on data from when the same drug was taken in the past. The drug information input unit also proposes an optimal drug schedule based on the user's health data. For example, it may analyze the user's body temperature and blood pressure data and propose the optimal timing of taking the drug. This makes it possible to propose an optimal drug schedule by utilizing the user's past data.

[0059] The drug information input unit can use the emotion estimation function to detect the stress level of the user when entering drug information and provide a relaxing interface. For example, when the user enters drug information, the emotion estimation function detects the stress level and provides a relaxing interface. For example, the color or design of the input screen can be changed to create an environment where the user can relax. The drug information input unit also detects the stress level of the user when entering drug information and plays relaxing music. For example, if the user is feeling stressed, relaxing music can be played. This allows the user to enter drug information in a relaxed state.

[0060] The drug information input unit uses voice recognition technology to input drug information, allowing the user to input drug information by voice. The drug information input unit allows the user to input drug names and dosing schedules by voice, for example. For example, if the user inputs "one aspirin after breakfast and one after dinner" by voice, the app converts the information into text and saves it. In addition, when the user inputs drug information by voice, the drug information input unit uses speech recognition technology to analyze the input content and provide accurate information. For example, if the user pronounces "aspirin," the generation AI analyzes the voice and inputs the correct drug name. This allows the user to input drug information by voice.

[0061] The drug information input unit inputs drug information by scanning QR codes and barcodes, and can obtain drug information directly from the drug package. The drug information input unit automatically obtains drug information, for example, when a user scans a QR code or barcode on a drug package. For example, when a QR code is scanned using a smartphone camera, the drug name and dosing schedule are automatically entered. The drug information input unit also automatically obtains drug information when a user scans a barcode on a drug package. For example, when a barcode is scanned using a barcode reader, the drug name and dosing schedule are automatically entered. This allows the user to obtain information directly from the drug package.

[0062] The drug information input unit can use the emotion estimation function to analyze the emotion of the user when entering drug information and provide positive feedback to encourage input. For example, when the user enters drug information, the emotion estimation function of the drug information input unit analyzes the emotion and provides positive feedback. For example, a message such as "Great! You're almost done" is displayed. The drug information input unit also analyzes the emotion of the user when entering drug information and provides positive feedback to encourage input. For example, a message such as "Good job! Let's move on to the next step" is displayed. In this way, the user receives positive feedback and is encouraged to enter drug information.

[0063] The reminder providing unit optimizes reminder notifications to match the user's lifestyle rhythm, and the generation AI can send notifications at the optimal timing. The reminder providing unit, for example, analyzes the user's lifestyle rhythm and sends reminders at the optimal timing. For example, based on the time the user takes breakfast, it sends a notification such as "It's time to take your medicine after breakfast." The reminder providing unit also analyzes the user's lifestyle rhythm and sends reminders at the optimal timing. For example, based on the time the user takes their medicine before going to bed, it sends a notification such as "It's time to take your medicine before going to bed." This allows reminders to be provided at the optimal timing to match the user's lifestyle rhythm.

[0064] The reminder providing unit can include detailed information about the effects and side effects of the medication in the reminder notification and provide the information in a form that is easy for the user to understand. For example, the reminder providing unit includes detailed information about the effects and side effects of the medication in the reminder notification. For example, it provides information such as, "This medication is effective in lowering blood pressure, but may cause dizziness as a side effect." The reminder providing unit also includes detailed information about the effects and side effects of the medication in the reminder notification. For example, it provides information such as, "This medication is effective in relieving pain, but may cause drowsiness as a side effect." This makes it easier for the user to understand the effects and side effects of the medication.

[0065] The reminder providing unit can use the emotion estimation function to adjust the tone and content of the reminder according to the emotional state of the user. For example, the reminder providing unit uses the emotion estimation function to adjust the tone and content of the reminder according to the emotional state of the user. For example, if the user is feeling stressed, the reminder providing unit sends a reminder in a gentle tone. The reminder providing unit also uses the emotion estimation function to adjust the tone and content of the reminder according to the emotional state of the user. For example, if the user is relaxed, the reminder is sent in a bright tone. This makes it possible to provide a reminder according to the emotional state of the user.

[0066] The reminder providing unit can link reminder notifications with devices such as a smartwatch and a smart speaker, allowing the user to receive notifications on multiple devices. For example, the reminder providing unit links reminder notifications with a smartwatch, allowing the user to receive notifications on their wrist. For example, a notification such as "It's time to take your medicine after breakfast" is displayed on the smartwatch. The reminder providing unit also links reminder notifications with a smart speaker, allowing the user to receive notifications by voice. For example, a notification such as "It's time to take your medicine after breakfast" is played by voice from the smart speaker. This allows the user to receive reminder notifications on multiple devices.

[0067] The reminder providing unit can link reminder notifications with a calendar app and automatically add them to a schedule. The reminder providing unit, for example, links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after breakfast" is automatically added to a calendar. The reminder providing unit also links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after dinner" is automatically added to a calendar. In this way, reminders can be added to a schedule in cooperation with the user's calendar app.

[0068] The reminder providing unit can use the emotion estimation function to analyze the user's emotional response when receiving a reminder and optimize the frequency and content of notifications. The reminder providing unit, for example, uses the emotion estimation function to analyze the user's emotional response when receiving a reminder. For example, it analyzes the user's facial expression and voice when receiving a reminder and calculates an emotion score. The reminder providing unit also uses the emotion estimation function to analyze the user's emotional response when receiving a reminder and optimizes the frequency and content of notifications. For example, if the user is feeling stressed, it reduces the frequency of notifications and simplifies the content. This makes it possible to optimize reminder notifications based on the user's emotional response.

[0069] The health data tracking unit can acquire data from the wearable device in real time and have the generating AI analyze the data to automate the input of health data. The health data tracking unit, for example, acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires heart rate and step count data from a smartwatch and analyzes the health condition. The health data tracking unit also acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires data from a fitness tracker and analyzes the health condition. This allows data to be acquired from the wearable device in real time and automates the input of health data.

[0070] The health data tracking unit allows the generation AI to provide the user with specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the health data tracking unit allows the generation AI to provide the user with specific health advice based on the results of the health data analysis. For example, the generation AI may display advice such as "It would be good to exercise for 30 minutes every day." The health data tracking unit also allows the generation AI to provide the user with suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the generation AI may display a suggestion such as "Reconsider the balance of your diet." This allows the generation AI to provide specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis.

[0071] The health data tracking unit can use the emotion estimation function to analyze the relationship between the user's emotional state and health data and suggest health management based on the emotion. The health data tracking unit, for example, uses the emotion estimation function to analyze the relationship between the user's emotional state and health data. For example, it analyzes the tendency for heart rate to increase during periods of high stress and provides stress management advice. The health data tracking unit also uses the emotion estimation function to analyze the relationship between the user's emotional state and health data and suggest health management based on the emotion. For example, it analyzes that the user's health is good when they are relaxed and suggests ways to relax. In this way, it is possible to analyze the relationship between the emotional state and health data and suggest health management based on the emotion.

[0072] The health data tracking unit stores health data in the cloud and allows it to be shared with doctors and family members, thereby enabling more comprehensive health management. The health data tracking unit, for example, stores health data in the cloud and shares it with doctors, thereby enabling more comprehensive health management. For example, doctors can periodically check the data and provide appropriate advice. The health data tracking unit also stores health data in the cloud and shares it with family members, thereby enabling more comprehensive health management. For example, family members can check the user's health data and provide support. In this way, comprehensive health management can be achieved by storing health data in the cloud and sharing it with doctors and family members.

[0073] The health data tracking unit visualizes health data and allows the user to intuitively understand it with graphs and charts. The health data tracking unit, for example, visualizes health data with graphs and charts to allow the user to intuitively understand it. For example, changes in body temperature and heart rate are displayed with a line graph. The health data tracking unit also visualizes health data to allow the user to intuitively understand it. For example, changes in blood pressure are displayed with a bar graph. In this way, the health data is visualized to allow the user to intuitively understand it.

[0074] The health data tracking unit can use the emotion estimation function to track the user's emotional state and provide health management advice according to changes in emotion. The health data tracking unit, for example, uses the emotion estimation function to track the user's emotional state in real time and provide health management advice according to changes in emotion. For example, during times of high stress, the health data tracking unit can suggest relaxation methods. The health data tracking unit can also use the emotion estimation function to track the user's emotional state and provide health management advice according to changes in emotion. For example, during times of relaxation, the health data tracking unit can provide advice on maintaining relaxation methods. This makes it possible to provide health management advice according to changes in the emotional state.

[0075] The reminder providing unit can link reminder notifications with devices such as a smartwatch and a smart speaker, allowing the user to receive notifications on multiple devices. For example, the reminder providing unit links reminder notifications with a smartwatch, allowing the user to receive notifications on their wrist. For example, a notification such as "It's time to take your medicine after breakfast" is displayed on the smartwatch. The reminder providing unit also links reminder notifications with a smart speaker, allowing the user to receive notifications by voice. For example, a notification such as "It's time to take your medicine after breakfast" is played by voice from the smart speaker. This allows the user to receive reminder notifications on multiple devices.

[0076] The reminder providing unit can link reminder notifications with a calendar app and automatically add them to a schedule. The reminder providing unit, for example, links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after breakfast" is automatically added to a calendar. The reminder providing unit also links reminder notifications with a calendar app and automatically adds them to a schedule. For example, a reminder such as "take medicine after dinner" is automatically added to a calendar. In this way, reminders can be added to a schedule in cooperation with the user's calendar app.

[0077] The reminder providing unit can use the emotion estimation function to analyze the user's emotional response when receiving a reminder and optimize the frequency and content of notifications. The reminder providing unit, for example, uses the emotion estimation function to analyze the user's emotional response when receiving a reminder. For example, it analyzes the user's facial expression and voice when receiving a reminder and calculates an emotion score. The reminder providing unit also uses the emotion estimation function to analyze the user's emotional response when receiving a reminder and optimizes the frequency and content of notifications. For example, if the user is feeling stressed, it reduces the frequency of notifications and simplifies the content. This makes it possible to optimize reminder notifications based on the user's emotional response.

[0078] The health data tracking unit can acquire data from the wearable device in real time and have the generating AI analyze the data to automate the input of health data. The health data tracking unit, for example, acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires heart rate and step count data from a smartwatch and analyzes the health condition. The health data tracking unit also acquires data from the wearable device in real time and has the generating AI analyze it. For example, it acquires data from a fitness tracker and analyzes the health condition. This allows data to be acquired from the wearable device in real time and automates the input of health data.

[0079] The health data tracking unit allows the generation AI to provide the user with specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the health data tracking unit allows the generation AI to provide the user with specific health advice based on the results of the health data analysis. For example, the generation AI may display advice such as "It would be good to exercise for 30 minutes every day." The health data tracking unit also allows the generation AI to provide the user with suggestions for improving lifestyle habits based on the results of the health data analysis. For example, the generation AI may display a suggestion such as "Reconsider the balance of your diet." This allows the generation AI to provide specific health advice and suggestions for improving lifestyle habits based on the results of the health data analysis.

[0080] The health data tracking unit can use the emotion estimation function to analyze the relationship between the user's emotional state and health data and suggest health management based on the emotion. The health data tracking unit, for example, uses the emotion estimation function to analyze the relationship between the user's emotional state and health data. For example, it analyzes the tendency for heart rate to increase during periods of high stress and provides stress management advice. The health data tracking unit also uses the emotion estimation function to analyze the relationship between the user's emotional state and health data and suggest health management based on the emotion. For example, it analyzes that the user's health is good when they are relaxed and suggests ways to relax. In this way, it is possible to analyze the relationship between the emotional state and health data and suggest health management based on the emotion.

[0081] The health data tracking unit visualizes health data and allows the user to intuitively understand it with graphs and charts. The health data tracking unit, for example, visualizes health data with graphs and charts to allow the user to intuitively understand it. For example, changes in body temperature and heart rate are displayed with a line graph. The health data tracking unit also visualizes health data to allow the user to intuitively understand it. For example, changes in blood pressure are displayed with a bar graph. In this way, the health data is visualized to allow the user to intuitively understand it.

[0082] The health data tracking unit can use the emotion estimation function to track the user's emotional state and provide health management advice according to changes in emotion. The health data tracking unit, for example, uses the emotion estimation function to track the user's emotional state in real time and provide health management advice according to changes in emotion. For example, during times of high stress, the health data tracking unit can suggest relaxation methods. The health data tracking unit can also use the emotion estimation function to track the user's emotional state and provide health management advice according to changes in emotion. For example, during times of relaxation, the health data tracking unit can provide advice on maintaining relaxation methods. This makes it possible to provide health management advice according to changes in the emotional state.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] Furthermore, when a user enters drug information into the drug information input section, the drug management system can have the generation AI suggest an optimal drug-taking schedule based on past drug-taking history and health data. For example, when a user enters drug information, the generation AI analyzes the user's past drug-taking history and suggests the optimal timing for taking the drug. It also suggests the optimal timing for taking the drug based on data from when the same drug was taken in the past. It also suggests an optimal drug-taking schedule based on the user's health data. For example, it analyzes the user's body temperature and blood pressure data and suggests the optimal timing for taking the drug. In this way, it is possible to suggest an optimal drug-taking schedule by utilizing the user's past data.

[0085] The medication management system can also use voice recognition technology to input medication information into the medication information input unit, allowing users to input medication information by voice. For example, users can input medication names and dosing schedules by voice. For example, if a user voice-inputs "one aspirin after breakfast and one after dinner," the app converts the information into text and saves it. The generation AI also uses voice recognition technology to analyze the input content and provide accurate information. For example, if a user pronounces "aspirin," the generation AI analyzes the voice and inputs the correct medication name. This allows users to input medication information by voice.

[0086] The medication management system also allows users to input medication information into the medication information input section by scanning QR codes and barcodes, and obtain medication information directly from medication packages. For example, a user can automatically obtain medication information by scanning a QR code or barcode on a medication package. When a user scans a QR code using a smartphone camera, the medication name and dosing schedule are automatically entered. When a user scans a barcode using a barcode reader, the medication name and dosing schedule are automatically entered. This allows users to obtain information directly from medication packages.

[0087] The medication management system further allows the reminder provider to optimize reminder notifications to match the user's lifestyle, with the generation AI sending notifications at the optimal timing. For example, the system analyzes the user's lifestyle and sends reminders at the optimal timing. Based on the time the user eats breakfast, it sends a notification such as "It's time to take your medicine after breakfast." Also, based on the time the user takes their medicine before going to bed, it sends a notification such as "It's time to take your medicine before going to bed." This allows reminders to be provided at the optimal timing to match the user's lifestyle.

[0088] The medication management system can further configure the reminder providing unit to include detailed information about the medication's effects and side effects in the reminder notification and provide it in a form that is easy for the user to understand. For example, detailed information about the medication's effects and side effects can be included in the reminder notification. Information such as "This medication has the effect of lowering blood pressure, but may cause dizziness as a side effect" can be provided. Also, information such as "This medication has the effect of relieving pain, but may cause drowsiness as a side effect" can be provided. This makes it easier for the user to understand the medication's effects and side effects.

[0089] The medicine management system can further use an emotion estimation function in the medicine information input unit to detect the stress level of the user when entering information and provide a relaxing interface. For example, when the user enters medicine information, the emotion estimation function detects the stress level and provides a relaxing interface. The color and design of the input screen are changed to create an environment where the user can relax. In addition, the system detects the stress level of the user when entering information and plays relaxing music. If the user is feeling stressed, relaxing music is played. This allows the user to enter medicine information in a relaxed state.

[0090] The medication management system can further use an emotion estimation function in the medication information input unit to analyze the emotions of the user when entering medication information and provide positive feedback to encourage input. For example, when a user enters medication information, the emotion estimation function analyzes the emotions and provides positive feedback, such as displaying a message like "Great! You're almost done." The system can also analyze the emotions of the user when entering medication information and provide positive feedback to encourage input, such as displaying a message like "Good job! Let's move on to the next step." This allows the user to receive positive feedback and encourage input of medication information.

[0091] The medication management system can further use an emotion estimation function in the reminder providing unit to adjust the tone and content of the reminder according to the user's emotional state. For example, the emotion estimation function is used to adjust the tone and content of the reminder according to the user's emotional state. If the user is feeling stressed, the reminder is sent in a gentle tone. If the user is relaxed, the reminder is sent in a bright tone. This allows the system to provide reminders according to the user's emotional state.

[0092] The medication management system can further use an emotion estimation function in the reminder providing unit to analyze the user's emotional response when receiving a reminder and optimize the frequency and content of notifications. For example, the emotion estimation function is used to analyze the user's emotional response when receiving a reminder. The facial expression and voice when receiving the reminder are analyzed to calculate an emotion score. The system also analyzes the user's emotional response when receiving a reminder and optimizes the frequency and content of notifications. If the user is feeling stressed, the frequency of notifications is reduced and the content is made simpler. This makes it possible to optimize reminder notifications based on the user's emotional response.

[0093] The medication management system can further use an emotion estimation function in the health data tracking unit to analyze the relationship between the user's emotional state and health data and suggest health management based on emotions. For example, the emotion estimation function is used to analyze the relationship between the user's emotional state and health data. It analyzes the tendency for heart rate to increase during periods of high stress and provides stress management advice. It also analyzes the relationship between the user's emotional state and health data and suggests health management based on emotions. It analyzes that the user's health is good when they are relaxed and suggests ways to relax. In this way, it is possible to analyze the relationship between the emotional state and health data and suggest health management based on emotions.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The drug information input unit inputs drug information and a dosage schedule from the user. For example, the user inputs the drug name, dosage, and dosage method. The user can also input a dosage schedule. For example, the user inputs a specific dosage schedule such as "one tablet after breakfast and one tablet after dinner." Step 2: The reminder providing unit provides reminders and warnings based on the information input by the medication information input unit. For example, it sends a notification such as "It's time to take your medicine after breakfast." It also provides warnings about drug interactions. For example, it displays a warning such as "This medicine may cause side effects when taken with other medicines." Step 3: The health data tracking unit tracks changes in the user's health data and symptoms. For example, the user enters health data such as body temperature, blood pressure, and heart rate. The generative AI then analyzes the data and provides information on the appropriateness and effectiveness of medication. For example, it may provide information such as, "Your body temperature has dropped since you started taking the medication."

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] 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.

[0155] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a drug information input unit for inputting drug information and a drug administration schedule from a user; a reminder providing unit that provides a reminder and a warning based on the information input by the drug information input unit; a health data tracking unit that tracks changes in the user's health data and symptoms. A system characterized by:

2. The drug information input unit Drug information is entered using voice recognition technology, allowing users to enter drug information by voice.

2. The system of claim 1.

3. The reminder providing unit The reminder notifications are optimized to fit the user's daily rhythm, and the generation AI sends notifications at the optimal time.

2. The system of claim 1.

4. The health data tracking unit: To automate the input of health data, data from wearable devices is acquired in real time and analyzed by a generative AI.

2. The system of claim 1.

5. The drug information input unit Detects the user's stress level while typing and provides a relaxing interface 2. The system of claim 1.

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