System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring weather data; means for analyzing the acquired weather data and predicting a change in atmospheric pressure in a specific area; means for managing profile information of a user; means for generating and transmitting a countermeasure advice to the user based on the change in atmospheric pressure; means for collecting feedback from the user and analyzing the data; and means for improving accuracy of the advice based on the collected feedback.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] Many people suffer from headaches and other health problems caused by changes in atmospheric pressure. However, currently, many people only use specific apps to check barometric pressure forecasts, making it difficult to obtain information on specific countermeasures. Furthermore, because effective countermeasures vary from person to person, general information is often insufficient. Therefore, there is a need for a system that proposes individual preventive measures and improves advice based on their effectiveness. The present invention aims to solve these problems and provide a system for preventing health problems caused by changes in atmospheric pressure. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific region, means for managing user profile information, means for generating and transmitting countermeasure advice to individual users based on changes in atmospheric pressure, means for collecting feedback from users and analyzing that data, and means for improving the accuracy of the advice based on the collected feedback. This system can provide users with specific and personalized advice in real time to avoid feeling unwell under specific conditions, and can continuously improve the accuracy of the advice based on feedback on the effectiveness of that advice.

[0006] "Weather data" includes information about weather, such as atmospheric pressure, temperature, humidity, and wind speed, for a particular region and time period.

[0007] "Means for obtaining" refers to a method or device for obtaining weather data from external weather agencies or APIs on a regular or real-time basis.

[0008] "Means for analysis" refers to algorithms and software for processing acquired weather data and predicting changes in atmospheric pressure in a specific area and time.

[0009] "Specific region" refers to the specific geographic area in which a user lives or has access.

[0010] "Changes in atmospheric pressure" refers to an increase or decrease in atmospheric pressure over a certain period of time, which is said to affect physical condition.

[0011] "User profile information" refers to the user's personal data (e.g., area of ​​residence, past trends in poor health, effectiveness of specific measures, etc.).

[0012] "Management means" refers to the method or device for organizing, storing, and updating user profile information using a database or information management system.

[0013] "Countermeasure advice" refers to specific guidelines and suggestions for avoiding or mitigating poor health caused by changes in atmospheric pressure.

[0014] The "means for generating" includes algorithms and software for automatically generating optimal countermeasure advice based on the user's profile information and information on changes in atmospheric pressure.

[0015] The "means for transmitting" refers to a method or device for notifying the generated countermeasure advice to the user's terminal (e.g., smartphone).

[0016] "Feedback" refers to the act or data that a user sends back to the system with information about the results of trying out the countermeasure advice and its effectiveness.

[0017] "Means for collecting" refers to a method or device for receiving feedback from users and storing it in a database or the like.

[0018] "Means for analysis" refers to the algorithms and software used to process the collected feedback data and evaluate its effectiveness.

[0019] "Means for improving accuracy" refers to a method or device for evaluating the effectiveness of countermeasure advice using collected feedback data and improving the quality of advice from the next time onwards.

[0020] "System" refers to a collection of devices and software that integrates the above means into a whole. [Brief explanation of the drawings]

[0021] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] 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.

[0026] 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.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] 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.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] System Overview

[0043] This invention is a system for preventing poor health caused by changes in atmospheric pressure. This system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on profile information managed for each user and notifies the user's device via a communication method such as LINE. It also collects and analyzes feedback from users and uses that data to improve the accuracy of the advice.

[0044] Program processing flow

[0045] Collection and analysis of atmospheric pressure forecast data

[0046] server

[0047] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API.

[0048] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes suddenly and the amount of change are calculated.

[0049] User information management

[0050] server

[0051] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[0052] 4. Link with LINE accounts and organize information for each user.

[0053] Generate and send notification content

[0054] server

[0055] 5. The server generates optimal countermeasure advice for each individual user based on the barometric pressure forecast data and the user's profile information.

[0056] For example, create specific advice such as, "The atmospheric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[0057] 6. The created advice is sent to the user's LINE account.

[0058] Terminal

[0059] 7. The user's device receives the LINE message and displays a notification.

[0060] Gathering feedback and improving advice

[0061] User

[0062] 8. The user tries out the proposed measures and sends feedback on their effectiveness via LINE.

[0063] For example, provide specific feedback such as, "The advice was effective and relieved my headache. I would like to try this method again next time."

[0064] server

[0065] 9. The server receives the feedback from the user and stores it in a database.

[0066] 10. Analyze the collected feedback data and evaluate whether specific measures were effective.

[0067] 11. Using machine learning algorithms, we will update the system to improve the accuracy of the next notification advice.

[0068] Specific examples

[0069] Barometric pressure forecast and notifications

[0070] server

[0071] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0072] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0073] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[0074] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[0075] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[0076] Terminal

[0077] 6. User A's smartphone receives the LINE message and displays a notification.

[0078] Feedback and Improvements

[0079] User

[0080] 1. User A follows the advice and practices hydration and early rest.

[0081] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[0082] server

[0083] 3. The server receives the feedback and updates the database.

[0084] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[0085] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0086] In this way, the present invention provides a series of processes to prevent poor health caused by changes in atmospheric pressure and to provide individualized, specific, and effective countermeasures.

[0087] The processing flow will be explained below.

[0088] Step 1:

[0089] server

[0090] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[0091] Step 2:

[0092] server

[0093] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[0094] Step 3:

[0095] server

[0096] The server maintains a database of user profile information, including the area of ​​residence, past health history, and the effectiveness of each measure.

[0097] Step 4:

[0098] server

[0099] The server combines the user profile information in the database with the barometric pressure forecast data to generate personalized advice for each user, such as "The barometric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[0100] Step 5:

[0101] server

[0102] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[0103] Step 6:

[0104] Terminal

[0105] The user's smartphone receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[0106] Step 7:

[0107] User

[0108] Based on the notification, the user can try the suggested measures, such as drinking more water and taking timely rest.

[0109] Step 8:

[0110] User

[0111] Users can send feedback to the official LINE account about the effectiveness of the measures, providing specific feedback such as, "The advice was effective and my headache has been reduced."

[0112] Step 9:

[0113] server

[0114] The server receives feedback from users and stores it in a database, including details about the effectiveness of the measures.

[0115] Step 10:

[0116] server

[0117] The server analyzes the collected feedback data and evaluates whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[0118] Step 11:

[0119] server

[0120] The server updates the algorithm for generating countermeasure advice based on the analysis results, thereby improving the accuracy of the next notification advice.

[0121] Step 12:

[0122] server

[0123] The server processes the new user profile information and barometric pressure forecast data using updated algorithms, preparing to generate future notification messages. This process is repeated continuously to ensure the advice provided to users is always optimal.

[0124] Example 1

[0125] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0126] It is widely known that changes in atmospheric pressure have an impact on people's health, with many suffering from symptoms such as headaches and weather-related illnesses. However, current technology makes it difficult to take effective measures in response to changes in atmospheric pressure, and no system exists that provides specific advice based on individual profiling. Therefore, there is a need for a system that can predict changes in atmospheric pressure and provide individually optimized advice based on that information.

[0127] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0128] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, thereby making it possible to provide individualized, specific, and effective countermeasures in response to changes in atmospheric pressure.

[0129] "Weather data" refers to weather-related information such as weather forecasts, temperature, air pressure, and precipitation provided by meteorological agencies.

[0130] "Analysis" is the act of processing acquired data and extracting specific insights and figures.

[0131] "Changes in atmospheric pressure" refers to an increase or decrease in atmospheric pressure over a specific period of time.

[0132] "Prediction" is the estimation of future states or events based on acquired data.

[0133] "User profile information" is individually managed information such as the user's area of ​​residence, past health data, and the effectiveness of countermeasures.

[0134] "Management" refers to a series of processes including collecting, storing, updating, and deleting data.

[0135] "Countermeasure advice" is specific guidelines and advice provided to the user to deal with changes in atmospheric pressure.

[0136] "Sending" is the act of delivering the generated information to a recipient through a specific communication means.

[0137] "Feedback" refers to information such as execution results, opinions, and effects provided by users.

[0138] A "machine learning algorithm" is a computational method for learning patterns from collected data and making predictions and classifications.

[0139] System Overview

[0140] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The server acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. Optimal countermeasure advice is generated based on profile information managed for each user, and notified to the user's terminal via communication means. Furthermore, feedback from users is collected and analyzed to improve the accuracy of the next advice.

[0141] Specific processing flow

[0142] 1. Collecting atmospheric pressure forecast data

[0143] The server periodically calls the API of the meteorological agency (e.g., OpenWeatherMap or the API of the Japan Meteorological Agency) to obtain the latest barometric pressure forecast data. The obtained data is stored on the server in JSON format.

[0144] 2. Analysis of atmospheric pressure data

[0145] The server analyzes the acquired atmospheric pressure data using data analysis tools such as Pandas and organizes it by region and time period, allowing the time periods when atmospheric pressure suddenly changes and the magnitude of those changes to be calculated.

[0146] 3. Management of User Information

[0147] The server stores and manages each user's profile information (such as the area they live in, past health data, and the effectiveness of measures they have taken) in a database (e.g., PostgreSQL). It also links the information to their LINE accounts, and organizes the information for each user.

[0148] 4. Generating and sending notification content

[0149] The server uses a generative AI model (e.g., GPT-3) based on the barometric pressure forecast data and the user's profile information to generate optimal advice. A specific example of advice might be, "The barometric pressure will drop suddenly this afternoon. You are likely to experience a headache, so it's a good idea to stay hydrated and get some rest early." The generated advice is sent to the user's LINE account via the LINE Official Account API.

[0150] 5. Gathering Feedback

[0151] The user implements the proposed measures and sends feedback on their effectiveness via LINE, such as "The advice was effective and my headache has subsided."

[0152] 6. Improved advice accuracy

[0153] The server stores the collected feedback in a database and analyzes it using machine learning algorithms (e.g., Scikit-learn or TensorFlow), which then updates the system to improve the accuracy of the next notification advice.

[0154] Specific examples

[0155] Barometric pressure forecast and notifications

[0156] server

[0157] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0158] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0159] 3. Refer to the profile information of User A who lives in Tokyo and recognize that he tends to suffer from frequent headaches during periods of low pressure.

[0160] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[0161] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[0162] Terminal

[0163] 6. User A's smartphone receives the LINE message and displays a notification.

[0164] Feedback and Improvements

[0165] User

[0166] 1. User A follows the advice and hydrates and takes an early rest.

[0167] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[0168] server

[0169] 3. The server receives the feedback and updates the database.

[0170] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[0171] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0172] Through the above process, the present invention prevents poor health caused by changes in atmospheric pressure and provides users with individualized and effective countermeasures.

[0173] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0174] Program processing flow

[0175] Step 1: Collect pressure forecast data

[0176] server

[0177] 1. The server calls the weather agency's API (e.g., OpenWeatherMap, Japan Meteorological Agency's API) every morning at 6:00.

[0178] Input: API key, region-specific request parameters

[0179] Specific operation: Uses Python's requests module to send an HTTP GET request to obtain barometric pressure forecast data.

[0180] Output: Receives barometric pressure forecast data in JSON format.

[0181] Step 2: Analyze the pressure data

[0182] server

[0183] 1. The server analyzes the acquired atmospheric pressure data.

[0184] Input: Pressure forecast data in JSON format

[0185] Specific operation: Use the Pandas library to convert the data into a data frame and perform analysis.

[0186] Output: Pressure change points and their magnitudes organized by region and time period

[0187] Step 3: Managing User Information

[0188] server

[0189] 1. The server retrieves each user's profile information from the database.

[0190] Input: User ID or LINE account ID

[0191] Specific operation: Retrieve user profile information from the PostgreSQL database using a SELECT query.

[0192] Output: Profile information (area of ​​residence, past health status, etc.)

[0193] Step 4: Generate notification content

[0194] server

[0195] 1. The server generates countermeasure advice based on barometric pressure forecast data and the user's profile information.

[0196] Input: Analyzed barometric pressure data, user profile information

[0197] Specific operation: A prompt sentence is input into a generative AI model (e.g., GPT-3) to generate specific countermeasure advice.

[0198] Example prompt: "Generate specific advice for a specific user to prevent headaches when the atmospheric pressure drops in the Tokyo area."

[0199] Output: Advice: "This afternoon, the atmospheric pressure will drop suddenly. You may experience a headache, so make sure to stay hydrated and get some rest."

[0200] Step 5: Submitting Advice

[0201] server

[0202] 1. The server sends the generated advice to the user via the LINE Official Account API.

[0203] Input: Generated countermeasure advice, user's LINE account ID

[0204] Specific operation: Uses the LINE Messaging API to send a POST request and deliver the advice.

[0205] Output: A notification message will be sent to the user's LINE account.

[0206] Terminal

[0207] 2. The user's device receives the LINE message and displays a notification.

[0208] Input: Message received from the LINE server

[0209] Specific behavior: The LINE app displays a notification.

[0210] Output: A notification is displayed on the user's smartphone.

[0211] Step 6: Gather feedback

[0212] User

[0213] 1. The user implements the proposed measures and provides feedback on their effectiveness.

[0214] Input: Impressions after implementing measures and evaluation of their effectiveness

[0215] Specific operation: Send a feedback message through the LINE app.

[0216] Output: Feedback data is sent to the server.

[0217] server

[0218] 2. The server receives the feedback from the user and stores it in a database.

[0219] Input: User feedback message

[0220] Specific operation: Data received via the LINE Messaging API is saved in a PostgreSQL database.

[0221] Output: The feedback data is stored in a database.

[0222] Step 7: Improve the accuracy of your advice

[0223] server

[0224] 1. The server analyzes the collected feedback data.

[0225] Input: Saved feedback data

[0226] Specific operations: Analyze data using machine learning algorithms (e.g., Scikit-learn, TensorFlow).

[0227] Output: A new learning model for improving the accuracy of countermeasure advice

[0228] 2. The server uses machine learning models to improve the accuracy of countermeasure advice.

[0229] Input: A new learning model

[0230] Specific operation: The generative AI model is updated and reflected in the next notification.

[0231] Output: Improved countermeasure advice is generated.

[0232] (Application example 1)

[0233] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0234] Changes in atmospheric pressure can have a significant impact on a user's physical condition. Specifically, in moving objects (such as self-driving vehicles), it is difficult to manage the user's physical condition while in the vehicle, so there is concern that sudden changes in atmospheric pressure may cause poor health. Therefore, there is a need for prevention and countermeasures against poor health caused by changes in atmospheric pressure in moving objects. In addition, there is a need for a system that can provide appropriate advice to individual users and collect feedback to improve the accuracy of the advice.

[0235] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0236] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for notifying the user of the countermeasure advice within the mobile vehicle, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice based on the collected feedback. This makes it possible to individually provide prevention and countermeasures for poor health caused by changes in atmospheric pressure, and effectively manage the user's physical condition even within the mobile vehicle.

[0237] "Weather data" refers to information about atmospheric conditions such as temperature, humidity, air pressure, and wind speed.

[0238] "Analysis" refers to the process of processing acquired data and information to derive meaning and trends.

[0239] "Specific region" refers to a limited geographical area, such as a specific city or area.

[0240] "Changes in atmospheric pressure" refers to increases or decreases in atmospheric pressure over a certain period of time.

[0241] "Prediction" refers to estimating future events or conditions with a certain degree of accuracy.

[0242] "User profile information" refers to data about an individual user, including name, address, health status, and past health history.

[0243] "Advice" refers to specific suggestions or instructions for dealing with a particular situation or problem.

[0244] "Sending" refers to the act of delivering information to a specific recipient.

[0245] "Mobile object" refers to a machine or device that can change position, such as an autonomous vehicle.

[0246] "Feedback" refers to reactions and opinions from users in response to information or advice received.

[0247] "Accuracy" refers to the degree to which the results of predictions, analyses, etc. are accurate.

[0248] "Means" refers to a method or device for achieving a particular purpose or result.

[0249] System Overview

[0250] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The system aims to manage the user's health while in a mobile vehicle by acquiring and analyzing weather data and predicting changes in atmospheric pressure. It also manages profile information for each user and generates and notifies optimal countermeasure advice. It also collects and analyzes feedback from users and uses that data to improve the accuracy of advice.

[0251] Hardware and software used

[0252] server

[0253] The server retrieves the latest barometric pressure forecast data using a weather data API, for example, the API at weatherapi.com.

[0254] The acquired data is analyzed and organized by region and time period.

[0255] Each user's profile information is managed in a database and notifications are sent via the LINE Official Account API.

[0256] Terminal

[0257] The user's smartphone or in-vehicle display receives the LINE message and displays a notification.

[0258] The notification will include detailed advice on how to prevent and deal with illness caused by changes in atmospheric pressure.

[0259] User

[0260] Users implement the recommended measures and provide feedback on their effectiveness.

[0261] Feedback is sent to the server via LINE and will be used to improve the accuracy of the next notification advice.

[0262] Overview of program processing flow

[0263] Meteorological data acquisition and analysis

[0264] The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API, analyzes the data, predicts sudden changes in barometric pressure in a specific area, and stores the results in a database.

[0265] User information management

[0266] The server stores and manages each user's profile information in a database, including the area where they live, their past health history, and the effectiveness of specific measures.

[0267] Generate and send notification content

[0268] The server generates optimal advice for each individual user based on the barometric pressure forecast data and the user's profile information. The advice is then sent to the user's smartphone or in-vehicle display via the LINE official account API.

[0269] Gathering feedback and improving advice

[0270] Users provide feedback on the effectiveness of the advice they receive through LINE. The server receives and analyzes the feedback, updates the database, and uses machine learning algorithms to improve the accuracy of the next notification advice.

[0271] Specific examples and prompt examples for generative AI models

[0272] As a concrete example, if User A is riding in a vehicle in the Tokyo area and receives a forecast from the weather data API that the atmospheric pressure will drop sharply between 2:00 PM and 4:00 PM, the server will generate advice such as "The atmospheric pressure will drop sharply this afternoon. You are likely to experience a headache, so please make sure to stay hydrated in your vehicle and relax and rest," and notify the advice via LINE. User A can follow the advice and provide feedback, thereby contributing to improving the accuracy of the next advice.

[0273] Example prompts for generative AI models:

[0274] Please devise a system that provides advice and notifications to prevent users from becoming ill due to large changes in air pressure while riding in a vehicle. Specifically, please explain the following points:

[0275] 1. Meteorological data collection and analysis

[0276] 2. User profile information management

[0277] 3. Notification delivery via dedicated applications

[0278] 4. Collecting and Using User Feedback

[0279] Please indicate the hardware, software, and specific process steps required for each step.

[0280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0281] Step 1:

[0282] The server periodically calls the weather agency's API to obtain weather data (temperature, pressure, wind speed, etc.). It uses the API key and region code as input, and obtains weather data in JSON format as output. This data is then analyzed to extract changes in pressure in specific regions and time periods. Data processing involves performing time series analysis of the pressure data to identify times when sudden changes in pressure are predicted.

[0283] Step 2:

[0284] The server reads each user's profile information from the database. Using the user ID as input, the server obtains profile information such as the area of ​​residence, health history, and effective countermeasures as output. Based on this information, users who are susceptible to changes in atmospheric pressure are identified. The data is processed by comparing the area information with health history data and analyzing the correlation.

[0285] Step 3:

[0286] The server generates individualized advice based on the barometric pressure forecast data and the user's profile information. The barometric pressure forecast data and profile information are used as input, and textual advice is generated as output. Data calculations involve matching the user's past health history with barometric pressure change patterns, and specific advice (e.g., hydration, rest, etc.) is included in the advice.

[0287] Step 4:

[0288] The server sends the generated advice text to the user's smartphone or in-vehicle display via the LINE Official Account API. LINE account information and advice text are used as input, and a LINE message is sent as output. Specifically, the server sends an API request to the LINE server and delivers the notification message.

[0289] Step 5:

[0290] The user puts into practice the advice received via LINE. The input is a LINE message, the output is putting the advice into practice, and observing the resulting changes in physical condition. Specific actions include following the advice and taking measures such as drinking water and resting.

[0291] Step 6:

[0292] The user provides feedback via LINE about the effectiveness of the measures they have implemented. As input, they record changes in their physical condition and send feedback via LINE messages. As output, the feedback information is sent to the server. Specific actions include inputting specific feedback such as "The advice was effective" or "My headache has subsided."

[0293] Step 7:

[0294] The server stores the received feedback in a database and analyzes it. The feedback data is used as input, and the feedback analysis results are obtained as output. For data calculations, the feedback data is input into a machine learning algorithm, and the advice generation model is retrained. This improves the accuracy of notification advice for the next barometric pressure change.

[0295] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0296] System Overview

[0297] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and in particular, combines an emotion engine that recognizes the user's emotions. The system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the profile information and emotional state managed for each user, and notifies the user's device via communication means such as LINE. Furthermore, it collects and analyzes feedback and emotional data from users, and uses this data to improve the accuracy of the advice.

[0298] Program processing flow

[0299] Collection and analysis of atmospheric pressure forecast data

[0300] server

[0301] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API. For example, it is set to retrieve the latest data every morning at 6:00.

[0302] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes most suddenly and the amount of change are calculated, and these events are saved in a database as important events.

[0303] User information management

[0304] server

[0305] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[0306] 4. Link with LINE accounts and organize information for each user.

[0307] 5. The server uses an emotion engine to analyze the user's emotional state in real time and record it in a database.

[0308] Generate and send notification content

[0309] server

[0310] 6. The server generates optimal countermeasure advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only countermeasures against barometric pressure changes but also the user's current emotional state.

[0311] For example, you might create advice like, "The atmospheric pressure will drop suddenly this afternoon. You can expect a headache, so make sure to stay hydrated and get some rest early. Taking some time to relax will also help ease your mood."

[0312] 7. The created advice is sent to the user's LINE account.

[0313] Terminal

[0314] 8. The user's device receives the LINE message and displays a notification within the app. The user confirms the notification.

[0315] Gathering feedback and improving advice

[0316] User

[0317] 9. The user takes action based on the notification, for example, drinking more water and taking timely rest.

[0318] 10. Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[0319] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[0320] server

[0321] 11. The server receives feedback and emotion data from users and stores it in a database.

[0322] 12. Analyze the collected feedback and sentiment data to assess whether a particular strategy was effective under specific conditions. This analysis is done using machine learning algorithms.

[0323] 13. The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[0324] Specific examples

[0325] Barometric pressure forecast and notifications

[0326] server

[0327] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0328] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0329] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[0330] 4. The emotion engine detects that User A's current emotional state is stress.

[0331] 5. Generate the following advice: "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early. You also seem to be stressed, so it's a good idea to take some time to relax."

[0332] 6. Send a notification to User A's LINE account via the LINE Official Account API.

[0333] Terminal

[0334] 7. User A's smartphone receives the LINE message and displays a notification.

[0335] Feedback and Improvements

[0336] User

[0337] 1. User A follows the advice and takes time to hydrate and relax.

[0338] 2. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[0339] server

[0340] 3. The server receives the feedback and emotion data and updates the database.

[0341] 4. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for the next barometric pressure change.

[0342] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0343] In this way, through a series of processes, the present invention not only prevents poor health caused by changes in atmospheric pressure and provides individualized, specific, and effective countermeasures, but also realizes a service that takes into consideration the user's emotional state.

[0344] The processing flow will be explained below.

[0345] Step 1:

[0346] server

[0347] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[0348] Step 2:

[0349] server

[0350] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[0351] Step 3:

[0352] server

[0353] The server stores and manages each user's profile information (such as the area of ​​residence, past health data, and the effectiveness of specific measures) in a database. It also links the information to LINE accounts and organizes the information for each user.

[0354] Step 4:

[0355] server

[0356] The server uses an emotion engine to recognize the user's emotional state. This emotion data is obtained in real time from the user's LINE messages and other interactions and stored in a database.

[0357] Step 5:

[0358] server

[0359] The server generates optimal advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only measures to deal with barometric pressure changes but also the user's current emotional state.

[0360] For example, you could create advice like, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so make sure to stay hydrated and get some rest early. Also, you seem to be feeling stressed, so it's a good idea to take some time to relax."

[0361] Step 6:

[0362] server

[0363] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[0364] Step 7:

[0365] Terminal

[0366] The user's device receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[0367] Step 8:

[0368] User

[0369] Based on the notification, the user can try the suggested measures, such as staying hydrated and taking time to rest, or taking time to relax based on the advice of the emotion engine.

[0370] Step 9:

[0371] User

[0372] Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[0373] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[0374] Step 10:

[0375] server

[0376] The server receives user feedback and sentiment data and stores it in a database. The feedback includes details about the effectiveness of countermeasures.

[0377] Step 11:

[0378] server

[0379] The server analyzes the collected feedback and sentiment data to assess whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[0380] Step 12:

[0381] server

[0382] The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[0383] Step 13:

[0384] server

[0385] The server processes the new user profile information, emotion data, and barometric pressure forecast data using updated algorithms to prepare for generating future notification messages. This process is repeated continuously to ensure that the advice provided to the user is always optimal.

[0386] Specific examples

[0387] server

[0388] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0389] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0390] 3. Based on the profile information and current emotional state (stressed state) of User A who lives in Tokyo, the following advice is generated: "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. Also, it seems that you are stressed, so it would be a good idea to take some time to relax."

[0391] 4. Send a notification to User A's LINE account via the LINE official account.

[0392] Terminal

[0393] 5. User A's smartphone receives the LINE message and displays a notification.

[0394] User

[0395] 6. User A follows the advice and takes time to hydrate and relax.

[0396] 7. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[0397] server

[0398] 8. The server receives the feedback and emotion data and updates the database.

[0399] 9. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for upcoming barometric pressure changes.

[0400] 10. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0401] In this way, the present invention not only prevents poor health due to changes in atmospheric pressure, but also takes into consideration the user's emotional state, thereby realizing a system that provides more personalized advice and kind words.

[0402] Example 2

[0403] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] For people who experience poor health due to changes in atmospheric pressure, effective and timely measures are needed. However, existing systems have difficulty providing personalized advice based on changes in atmospheric pressure. Furthermore, they are unable to generate advice that takes into account the user's emotional state, which limits the effectiveness of the measures.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0406] In this invention, the server includes means for acquiring weather information, means for analyzing the acquired weather information and predicting changes in atmospheric pressure at a specific location, means for managing personal information of a user, means for generating countermeasure advice for a user based on the acquired changes in atmospheric pressure, means for transmitting the generated countermeasure advice to the user via communication means, means for analyzing the emotional state of the user, means for collecting feedback from the user and analyzing the data, means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, and means for transmitting the generated countermeasure advice. This makes it possible to provide effective countermeasures tailored to individual needs for dealing with poor health caused by changes in atmospheric pressure.

[0407] "Weather information" refers to data such as temperature, air pressure, humidity, wind speed, and precipitation obtained from meteorological agencies.

[0408] "Means of acquisition" refers to the programs and hardware used to periodically collect weather information through APIs of external meteorological agencies, etc.

[0409] "Analysis means" refers to algorithms and programs for processing collected weather information and predicting changes in atmospheric pressure at specific locations and times.

[0410] "Personal information" refers to a set of information about the user, such as the area where the user lives, past health conditions, and regular mood states.

[0411] "Management means" refers to the programs and hardware used to register, update, and delete users' personal information and profiles in the database.

[0412] "Measure advice generation means" refers to an algorithm and program for automatically generating appropriate action suggestions based on changes in atmospheric pressure and the user's personal information.

[0413] "Communication means" refers to an internet connection and communication application for transmitting generated countermeasure advice to a user.

[0414] "Emotional state analysis means" refers to natural language processing and machine learning algorithms used to analyze user-provided text data and identify a user's current emotional state.

[0415] "Feedback collection means" refers to a program and hardware for receiving feedback and emotional data from users and registering it in a database.

[0416] A "machine learning algorithm" refers to a program that uses statistical and mathematical methods to learn patterns from large amounts of data and make future predictions and classifications.

[0417] The present invention is a system for preventing poor health caused by changes in atmospheric pressure, and is particularly combined with an emotion engine that recognizes the user's emotions. This system is built with the cooperation of a server, terminals, and users.

[0418] Server Operation

[0419] The server collects and analyzes weather information, manages user information, generates and sends advice on countermeasures, and collects and analyzes feedback. These operations are performed using the following specific hardware and software:

[0420] 1. Obtaining weather information:

[0421] The server calls the weather agency's API every morning at 6:00 to obtain the latest barometric pressure forecast data. This communication is made via an HTTP request, and the data is returned in JSON format.

[0422] 2. Meteorological information analysis:

[0423] The server parses the received JSON data and calculates the change in atmospheric pressure for a specific location and time period, and stores the results in a database.

[0424] 3. Managing User Information:

[0425] User profile information (such as the area of ​​residence and past health history) is stored in a database on the server. LINE account information is also linked via API.

[0426] 4. Emotional state analysis:

[0427] The server analyzes the text data provided by the user using natural language processing (NLP) and machine learning algorithms (e.g., sentiment analysis models) to identify the user's emotional state, which is also stored in a database.

[0428] 5. Generate action advice:

[0429] The server uses a generative AI model to generate optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The advice is written in natural language using, for example, a Python NLP library.

[0430] 6. Sending advice:

[0431] The server uses the LINE Official Account API to send the generated advice to the user's LINE account.

[0432] 7. Feedback Collection and Analysis:

[0433] The server receives user feedback and sentiment data and stores it in a database, which is then fed into a machine learning algorithm to improve the accuracy of next time's advice.

[0434] Device behavior

[0435] The user's device (e.g., a smartphone) receives the notification sent from the server through the LINE app and displays it to the user, allowing the user to check and implement the proposed measures.

[0436] User Actions

[0437] The user receives a notification from the server through the LINE app, implements the proposed measures, and provides feedback on their effectiveness. This feedback should also include the user's emotional state.

[0438] Specific examples

[0439] 1. Barometric pressure forecast and notifications:

[0440] The server calls the weather data API every morning at 6:00 and obtains barometric pressure forecast data for the Tokyo area.

[0441] The analysis confirms that the air pressure drops sharply between 2:00 and 4:00 p.m.

[0442] Based on the profile information of user A who lives in Tokyo, it is recognized that he / she suffers from frequent headaches.

[0443] The emotion engine detects that you are feeling stressed and generates advice such as, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. It seems that you are stressed, so it would also be a good idea to take some time to relax."

[0444] The server sends a notification to User A via the LINE Official Account API.

[0445] User A's smartphone receives the LINE message and displays a notification.

[0446] 2. Feedback and Improvement:

[0447] User A follows the advice and takes time to hydrate and relax.

[0448] Feedback is sent via LINE along with the patient's emotional state, providing specific feedback such as, "The advice was effective, my headache has subsided, and I feel a little better."

[0449] The server receives the feedback and stores it in a database.

[0450] Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of next notification advice.

[0451] Prompt Sentence Examples

[0452] An example of a prompt sentence to be input to the emotion engine is, "Tell me about your emotional state recently. Are you feeling stressed or anxious?" Based on this prompt sentence, the user inputs their own emotions in text format, which is then analyzed.

[0453] In this way, the present invention provides specific and effective countermeasures to prevent health problems caused by changes in atmospheric pressure, and also realizes more personalized services by taking into account the user's emotional state.

[0454] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0455] Step 1:

[0456] The server retrieves the weather information.

[0457] Input: Weather agency API endpoint and credentials.

[0458] Specific operation: Sends an HTTP request every morning at 6:00 and receives data in JSON format.

[0459] Output: Retrieved weather data in JSON format.

[0460] Step 2:

[0461] The server analyzes the weather information it receives and predicts changes in atmospheric pressure at specific locations and times.

[0462] Input: Weather data in JSON format.

[0463] Specific operation: Parse the JSON data, split it by region and time zone, and calculate the change in atmospheric pressure.

[0464] Output: Analyzed pressure forecast data.

[0465] Step 3:

[0466] The server manages the user's personal information.

[0467] Input: Profile information registered by the user (address, past health status, etc.).

[0468] Specific operation: User information is stored and managed in a database. Also, LINE account information is linked via API.

[0469] Output: User profile information stored in a database.

[0470] Step 4:

[0471] The server analyzes the user's emotional state.

[0472] Input: User-provided text data (e.g., diary entries or messages).

[0473] What it does: Uses a generative AI model to analyze text data and identify emotional states.

[0474] Output: Parsed emotional state data.

[0475] Step 5:

[0476] The server generates countermeasure advice based on barometric pressure forecast data, the user's personal information, and their emotional state.

[0477] Input: Parsed barometric pressure forecast data, user profile information, emotional state data.

[0478] What it does: Uses generative AI models to generate personalized countermeasure advice.

[0479] Output: The generated remediation advice text.

[0480] Step 6:

[0481] The server transmits the generated countermeasure advice to the user via a communication means.

[0482] Input: Generated advice text, user's LINE account information.

[0483] Specific operation: Advice on countermeasures is sent as a LINE message via the LINE official account API.

[0484] Output: Send a message to the user's LINE account.

[0485] Step 7:

[0486] The device receives the LINE message sent from the server and displays a notification.

[0487] Input: The LINE message sent from the server.

[0488] What happens: The LINE application displays the notification as a banner, and when the user taps on the message, more details are displayed.

[0489] Output: A notification is displayed to the user.

[0490] Step 8:

[0491] The user implements the measures notified.

[0492] Input: Countermeasure advice received via the LINE app.

[0493] Specific actions: Implement suggested measures (e.g., hydration, rest).

[0494] Output: Results of the implementation of the measures.

[0495] Step 9:

[0496] Users provide feedback on the effectiveness of the measures.

[0497] Input: Results of taking the measures and emotional state.

[0498] Specific behavior: Use the LINE application to enter and send feedback in text format.

[0499] Output: The feedback data sent.

[0500] Step 10:

[0501] The server receives feedback and emotion data from users and stores it in a database.

[0502] Input: Feedback data and emotional state data.

[0503] Specific operation: Save the received data in the database.

[0504] Output: Feedback data stored in a database.

[0505] Step 11:

[0506] The server inputs the collected feedback data and sentiment data into a machine learning algorithm to improve the accuracy of the next countermeasure advice.

[0507] Input: Feedback data and emotional state data.

[0508] Specific operation: A machine learning algorithm is used to analyze the feedback data and update the generative model for countermeasure advice.

[0509] Output: An improved model for generating countermeasure advice.

[0510] Through the above processing steps, a system is realized that prevents users from becoming ill due to changes in atmospheric pressure and provides individual and effective countermeasures for the user.

[0511] (Application example 2)

[0512] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0513] In modern society, changes in atmospheric pressure are a major cause of poor health for many people. In addition, people are exposed to various stressors every day, increasing the risk of poor health. However, current systems are unable to provide prompt and appropriate countermeasures against these changes in atmospheric pressure and stress, resulting in increased health risks. Furthermore, there is a lack of a mechanism for effectively utilizing feedback on health advice to improve the accuracy of countermeasures. Another issue is the lack of a means to smoothly purchase the necessary products as countermeasures.

[0514] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for analyzing the user's emotional state using an emotion analysis engine, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure and the user's emotional state, and means for providing electronic payment functionality and enabling the user to purchase health-related products. This makes it possible to provide personalized advice that takes into account changes in atmospheric pressure and the user's emotional state, and to smoothly purchase necessary countermeasure products. In addition, the server can collect user feedback and use machine learning algorithms to improve the accuracy of the advice.

[0515] "Weather data" is information that indicates atmospheric conditions and changes, and includes elements such as temperature, humidity, air pressure, wind speed, and wind direction.

[0516] An "emotion analysis engine" is software that analyzes a user's emotional state based on their text data and biometric information, and distinguishes between emotions such as anger, sadness, joy, and stress.

[0517] "Electronic payment function" is a system that allows users to pay for goods and services via the Internet, and includes payment methods such as credit cards, debit cards, and electronic money.

[0518] "Feedback" is information provided by the user, and is data that collects reactions such as the effectiveness of advice, changes in physical condition, and usage impressions.

[0519] "Machine learning algorithms" are a field of artificial intelligence that uses patterns learned from past data to make predictions and judgments about new data.

[0520] "Advice on countermeasures" is information that indicates specific actions and precautions that the user should take based on changes in atmospheric pressure and the user's emotional state, and includes suggestions for health management and mood improvement.

[0521] System Overview

[0522] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and is configured with an emotion analysis engine that analyzes the user's emotional state. The system includes a function for collecting and analyzing weather data and predicting changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the acquired data, individual user profile information, and emotion analysis results, and provides a mechanism for enabling users to purchase necessary health products using an electronic payment function.

[0523] System Details

[0524] Meteorological data collection and analysis tools

[0525] The server periodically obtains the latest barometric pressure forecast data via the weather agency's API. For example, it collects data every morning at 6:00 and predicts the daily barometric pressure change in a specific area. If a sudden change in barometric pressure is expected, it analyzes the time and amount of change and stores it in the database as an important event.

[0526] Emotion analysis means

[0527] The server uses an emotion analysis engine to analyze the user's emotional state in real time, which involves analyzing the user's text data and biometric information (e.g., heart rate and skin conductance) to determine emotional states such as stress, anger, and joy.

[0528] How to generate and send advice

[0529] The server generates optimal advice based on the collected barometric pressure forecast data, the user's profile information, and their emotional state. This advice is sent to the user's device via a communication method such as LINE. For example, specific advice such as "This afternoon, the barometric pressure will drop suddenly. You are likely to experience a headache, so it would be a good idea to stay hydrated and rest early. We also recommend that you take some time to relax."

[0530] Electronic payment facility

[0531] The app also provides an electronic payment function for users to purchase necessary health products. For example, health drinks and relaxation items recommended based on advice can be purchased directly within the app. This function allows users to receive advice and purchase products in a single flow.

[0532] Feedback collection and analysis methods

[0533] After implementing the advice, the user provides feedback on its effectiveness. This feedback data is collected along with the user's emotional state. The server analyzes this data and uses machine learning algorithms to improve the accuracy of future advice. For example, the server can improve the quality of the generated advice based on feedback such as, "The advice was effective, my headache was relieved, and I feel a little better."

[0534] Hardware and software used

[0535] Hardware: Smartphone

[0536] Software: LINE Bot API, weather data API, sentiment analysis engine, Python platform

[0537] Example prompt: "User A is experiencing a drop in barometric pressure at 2 PM. He is likely to have a headache, so he should stay hydrated and get some rest. He also seems to be feeling stressed, so it's a good idea to take some time to relax."

[0538] Specific examples

[0539] Example 1: User A, who lives in Tokyo, receives barometric pressure forecast information every morning via an app. This morning, the app predicted that barometric pressure would drop sharply between 2:00 PM and 4:00 PM, and sent User A advice on what to do via LINE. The advice sent reads, "Barometric pressure will drop sharply this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. You are also feeling stressed, so we recommend that you take some time to relax."

[0540] Example 2: User A purchased the recommended health drink within the app using the electronic payment function and hydrated. When the atmospheric pressure dropped in the afternoon, the advance precautions prevented a headache. That evening, User A sent feedback via LINE that the advice was effective. The server applies a machine learning algorithm based on this feedback data to improve the accuracy of the advice.

[0541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0542] Step 1:

[0543] The server obtains the latest barometric pressure forecast data from the meteorological agency's API. It calls the API every morning at 6:00 to collect barometric pressure data for each region. The input is the weather API endpoint, and the output is the barometric pressure forecast data for each region. It analyzes the data, identifies times of day when barometric pressure changes most suddenly, and stores the data in a database.

[0544] Step 2:

[0545] The server manages user profile information. It stores and updates the database with information collected at the time of initial registration, such as the area of ​​residence, past health conditions, and addictive measures. The input is the user's registration information, and the output is each user's profile data.

[0546] Step 3:

[0547] The server uses an emotion analysis engine to analyze the user's emotional state in real time. It determines emotions based on LINE messages and biometric data and records them in a database. The input is text data and biometric information, and the output is the analyzed emotional state.

[0548] Step 4:

[0549] The server generates optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The generated advice is sent to the user's device as a LINE message. The input is barometric pressure data, profile information, and their emotional state, and the output is an advice message. A generative AI model is used to create the advice.

[0550] Step 5:

[0551] The user receives the LINE message and takes the necessary measures according to the advice, such as staying hydrated or taking time to relax. The input is the received advice message, and the output is the implemented measures.

[0552] Step 6:

[0553] Users provide feedback on the effectiveness of the measures. They report the effectiveness along with their emotional state via LINE. The input is the emotional state after the measures are implemented and the feedback content, and the output is the sent feedback data.

[0554] Step 7:

[0555] The server collects user feedback and stores it in a database. It analyzes the feedback data and uses a machine learning algorithm to improve the countermeasure advice generation algorithm. The input is the feedback data, and the output is the improved advice generation algorithm.

[0556] Step 8:

[0557] The server improves the accuracy of the notification advice for the next barometric pressure change based on the improved advice generation algorithm. The newly generated advice message provides more accurate measures for the next barometric pressure change. The input is the improved algorithm, and the output is a more accurate advice message.

[0558] 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.

[0559] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0560] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0561] [Second embodiment]

[0562] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0563] 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.

[0564] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0565] 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.

[0566] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0567] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0568] 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.

[0569] 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.

[0570] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] 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.

[0572] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0573] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0574] System Overview

[0575] This invention is a system for preventing poor health caused by changes in atmospheric pressure. This system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on profile information managed for each user and notifies the user's device via a communication method such as LINE. It also collects and analyzes feedback from users and uses that data to improve the accuracy of the advice.

[0576] Program processing flow

[0577] Collection and analysis of atmospheric pressure forecast data

[0578] server

[0579] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API.

[0580] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes suddenly and the amount of change are calculated.

[0581] User information management

[0582] server

[0583] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[0584] 4. Link with LINE accounts and organize information for each user.

[0585] Generate and send notification content

[0586] server

[0587] 5. The server generates optimal countermeasure advice for each individual user based on the barometric pressure forecast data and the user's profile information.

[0588] For example, create specific advice such as, "The atmospheric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[0589] 6. The created advice is sent to the user's LINE account.

[0590] Terminal

[0591] 7. The user's device receives the LINE message and displays a notification.

[0592] Gathering feedback and improving advice

[0593] User

[0594] 8. The user tries out the proposed measures and sends feedback on their effectiveness via LINE.

[0595] For example, provide specific feedback such as, "The advice was effective and relieved my headache. I would like to try this method again next time."

[0596] server

[0597] 9. The server receives the feedback from the user and stores it in a database.

[0598] 10. Analyze the collected feedback data and evaluate whether specific measures were effective.

[0599] 11. Using machine learning algorithms, we will update the system to improve the accuracy of the next notification advice.

[0600] Specific examples

[0601] Barometric pressure forecast and notifications

[0602] server

[0603] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0604] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0605] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[0606] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[0607] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[0608] Terminal

[0609] 6. User A's smartphone receives the LINE message and displays a notification.

[0610] Feedback and Improvements

[0611] User

[0612] 1. User A follows the advice and practices hydration and early rest.

[0613] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[0614] server

[0615] 3. The server receives the feedback and updates the database.

[0616] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[0617] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0618] In this way, the present invention provides a series of processes to prevent poor health caused by changes in atmospheric pressure and to provide individualized, specific, and effective countermeasures.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] server

[0622] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[0623] Step 2:

[0624] server

[0625] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[0626] Step 3:

[0627] server

[0628] The server maintains a database of user profile information, including the area of ​​residence, past health history, and the effectiveness of each measure.

[0629] Step 4:

[0630] server

[0631] The server combines the user profile information in the database with the barometric pressure forecast data to generate personalized advice for each user, such as "The barometric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[0632] Step 5:

[0633] server

[0634] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[0635] Step 6:

[0636] Terminal

[0637] The user's smartphone receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[0638] Step 7:

[0639] User

[0640] Based on the notification, the user can try the suggested measures, such as drinking more water and taking timely rest.

[0641] Step 8:

[0642] User

[0643] Users can send feedback to the official LINE account about the effectiveness of the measures, providing specific feedback such as, "The advice was effective and my headache has been reduced."

[0644] Step 9:

[0645] server

[0646] The server receives feedback from users and stores it in a database, including details about the effectiveness of the measures.

[0647] Step 10:

[0648] server

[0649] The server analyzes the collected feedback data and evaluates whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[0650] Step 11:

[0651] server

[0652] The server updates the algorithm for generating countermeasure advice based on the analysis results, thereby improving the accuracy of the next notification advice.

[0653] Step 12:

[0654] server

[0655] The server processes the new user profile information and barometric pressure forecast data using updated algorithms, preparing to generate future notification messages. This process is repeated continuously to ensure the advice provided to users is always optimal.

[0656] Example 1

[0657] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0658] It is widely known that changes in atmospheric pressure have an impact on people's health, with many suffering from symptoms such as headaches and weather-related illnesses. However, current technology makes it difficult to take effective measures in response to changes in atmospheric pressure, and no system exists that provides specific advice based on individual profiling. Therefore, there is a need for a system that can predict changes in atmospheric pressure and provide individually optimized advice based on that information.

[0659] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0660] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, thereby making it possible to provide individualized, specific, and effective countermeasures in response to changes in atmospheric pressure.

[0661] "Weather data" refers to weather-related information such as weather forecasts, temperature, air pressure, and precipitation provided by meteorological agencies.

[0662] "Analysis" is the act of processing acquired data and extracting specific insights and figures.

[0663] "Changes in atmospheric pressure" refers to an increase or decrease in atmospheric pressure over a specific period of time.

[0664] "Prediction" is the estimation of future states or events based on acquired data.

[0665] "User profile information" is individually managed information such as the user's area of ​​residence, past health data, and the effectiveness of countermeasures.

[0666] "Management" refers to a series of processes including collecting, storing, updating, and deleting data.

[0667] "Countermeasure advice" is specific guidelines and advice provided to the user to deal with changes in atmospheric pressure.

[0668] "Sending" is the act of delivering the generated information to a recipient through a specific communication means.

[0669] "Feedback" refers to information such as execution results, opinions, and effects provided by users.

[0670] A "machine learning algorithm" is a computational method for learning patterns from collected data and making predictions and classifications.

[0671] System Overview

[0672] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The server acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. Optimal countermeasure advice is generated based on profile information managed for each user, and notified to the user's terminal via communication means. Furthermore, feedback from users is collected and analyzed to improve the accuracy of the next advice.

[0673] Specific processing flow

[0674] 1. Collecting atmospheric pressure forecast data

[0675] The server periodically calls the API of the meteorological agency (e.g., OpenWeatherMap or the API of the Japan Meteorological Agency) to obtain the latest barometric pressure forecast data. The obtained data is stored on the server in JSON format.

[0676] 2. Analysis of atmospheric pressure data

[0677] The server analyzes the acquired atmospheric pressure data using data analysis tools such as Pandas and organizes it by region and time period, allowing the time periods when atmospheric pressure suddenly changes and the magnitude of those changes to be calculated.

[0678] 3. Management of User Information

[0679] The server stores and manages each user's profile information (such as the area they live in, past health data, and the effectiveness of measures they have taken) in a database (e.g., PostgreSQL). It also links the information to their LINE accounts, and organizes the information for each user.

[0680] 4. Generating and sending notification content

[0681] The server uses a generative AI model (e.g., GPT-3) based on the barometric pressure forecast data and the user's profile information to generate optimal advice. A specific example of advice might be, "The barometric pressure will drop suddenly this afternoon. You are likely to experience a headache, so it's a good idea to stay hydrated and get some rest early." The generated advice is sent to the user's LINE account via the LINE Official Account API.

[0682] 5. Gathering Feedback

[0683] The user implements the proposed measures and sends feedback on their effectiveness via LINE, such as "The advice was effective and my headache has subsided."

[0684] 6. Improved advice accuracy

[0685] The server stores the collected feedback in a database and analyzes it using machine learning algorithms (e.g., Scikit-learn or TensorFlow), which then updates the system to improve the accuracy of the next notification advice.

[0686] Specific examples

[0687] Barometric pressure forecast and notifications

[0688] server

[0689] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0690] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0691] 3. Refer to the profile information of User A who lives in Tokyo and recognize that he tends to suffer from frequent headaches during periods of low pressure.

[0692] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[0693] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[0694] Terminal

[0695] 6. User A's smartphone receives the LINE message and displays a notification.

[0696] Feedback and Improvements

[0697] User

[0698] 1. User A follows the advice and hydrates and takes an early rest.

[0699] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[0700] server

[0701] 3. The server receives the feedback and updates the database.

[0702] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[0703] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0704] Through the above process, the present invention prevents poor health caused by changes in atmospheric pressure and provides users with individualized and effective countermeasures.

[0705] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0706] Program processing flow

[0707] Step 1: Collect pressure forecast data

[0708] server

[0709] 1. The server calls the weather agency's API (e.g., OpenWeatherMap, Japan Meteorological Agency's API) every morning at 6:00.

[0710] Input: API key, region-specific request parameters

[0711] Specific operation: Uses Python's requests module to send an HTTP GET request to obtain barometric pressure forecast data.

[0712] Output: Receives barometric pressure forecast data in JSON format.

[0713] Step 2: Analyze the pressure data

[0714] server

[0715] 1. The server analyzes the acquired atmospheric pressure data.

[0716] Input: Pressure forecast data in JSON format

[0717] Specific operation: Use the Pandas library to convert the data into a data frame and perform analysis.

[0718] Output: Pressure change points and their magnitudes organized by region and time period

[0719] Step 3: Managing User Information

[0720] server

[0721] 1. The server retrieves each user's profile information from the database.

[0722] Input: User ID or LINE account ID

[0723] Specific operation: Retrieve user profile information from the PostgreSQL database using a SELECT query.

[0724] Output: Profile information (area of ​​residence, past health status, etc.)

[0725] Step 4: Generate notification content

[0726] server

[0727] 1. The server generates countermeasure advice based on barometric pressure forecast data and the user's profile information.

[0728] Input: Analyzed barometric pressure data, user profile information

[0729] Specific operation: A prompt sentence is input into a generative AI model (e.g., GPT-3) to generate specific countermeasure advice.

[0730] Example prompt: "Generate specific advice for a specific user to prevent headaches when the atmospheric pressure drops in the Tokyo area."

[0731] Output: Advice: "This afternoon, the atmospheric pressure will drop suddenly. You may experience a headache, so make sure to stay hydrated and get some rest."

[0732] Step 5: Submitting Advice

[0733] server

[0734] 1. The server sends the generated advice to the user via the LINE Official Account API.

[0735] Input: Generated countermeasure advice, user's LINE account ID

[0736] Specific operation: Uses the LINE Messaging API to send a POST request and deliver the advice.

[0737] Output: A notification message will be sent to the user's LINE account.

[0738] Terminal

[0739] 2. The user's device receives the LINE message and displays a notification.

[0740] Input: Message received from the LINE server

[0741] Specific behavior: The LINE app displays a notification.

[0742] Output: A notification is displayed on the user's smartphone.

[0743] Step 6: Gather feedback

[0744] User

[0745] 1. The user implements the proposed measures and provides feedback on their effectiveness.

[0746] Input: Impressions after implementing measures and evaluation of their effectiveness

[0747] Specific operation: Send a feedback message through the LINE app.

[0748] Output: Feedback data is sent to the server.

[0749] server

[0750] 2. The server receives the feedback from the user and stores it in a database.

[0751] Input: User feedback message

[0752] Specific operation: Data received via the LINE Messaging API is saved in a PostgreSQL database.

[0753] Output: The feedback data is stored in a database.

[0754] Step 7: Improve the accuracy of your advice

[0755] server

[0756] 1. The server analyzes the collected feedback data.

[0757] Input: Saved feedback data

[0758] Specific operations: Analyze data using machine learning algorithms (e.g., Scikit-learn, TensorFlow).

[0759] Output: A new learning model for improving the accuracy of countermeasure advice

[0760] 2. The server uses machine learning models to improve the accuracy of countermeasure advice.

[0761] Input: A new learning model

[0762] Specific operation: The generative AI model is updated and reflected in the next notification.

[0763] Output: Improved countermeasure advice is generated.

[0764] (Application example 1)

[0765] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0766] Changes in atmospheric pressure can have a significant impact on a user's physical condition. Specifically, in moving objects (such as self-driving vehicles), it is difficult to manage the user's physical condition while in the vehicle, so there is concern that sudden changes in atmospheric pressure may cause poor health. Therefore, there is a need for prevention and countermeasures against poor health caused by changes in atmospheric pressure in moving objects. In addition, there is a need for a system that can provide appropriate advice to individual users and collect feedback to improve the accuracy of the advice.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0768] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for notifying the user of the countermeasure advice within the mobile vehicle, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice based on the collected feedback. This makes it possible to individually provide prevention and countermeasures for poor health caused by changes in atmospheric pressure, and effectively manage the user's physical condition even within the mobile vehicle.

[0769] "Weather data" refers to information about atmospheric conditions such as temperature, humidity, air pressure, and wind speed.

[0770] "Analysis" refers to the process of processing acquired data and information to derive meaning and trends.

[0771] "Specific region" refers to a limited geographical area, such as a specific city or area.

[0772] "Changes in atmospheric pressure" refers to increases or decreases in atmospheric pressure over a certain period of time.

[0773] "Prediction" refers to estimating future events or conditions with a certain degree of accuracy.

[0774] "User profile information" refers to data about an individual user, including name, address, health status, and past health history.

[0775] "Advice" refers to specific suggestions or instructions for dealing with a particular situation or problem.

[0776] "Sending" refers to the act of delivering information to a specific recipient.

[0777] "Mobile object" refers to a machine or device that can change position, such as an autonomous vehicle.

[0778] "Feedback" refers to reactions and opinions from users in response to information or advice received.

[0779] "Accuracy" refers to the degree to which the results of predictions, analyses, etc. are accurate.

[0780] "Means" refers to a method or device for achieving a particular purpose or result.

[0781] System Overview

[0782] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The system aims to manage the user's health while in a mobile vehicle by acquiring and analyzing weather data and predicting changes in atmospheric pressure. It also manages profile information for each user and generates and notifies optimal countermeasure advice. It also collects and analyzes feedback from users and uses that data to improve the accuracy of advice.

[0783] Hardware and software used

[0784] server

[0785] The server retrieves the latest barometric pressure forecast data using a weather data API, for example, the API at weatherapi.com.

[0786] The acquired data is analyzed and organized by region and time period.

[0787] Each user's profile information is managed in a database and notifications are sent via the LINE Official Account API.

[0788] Terminal

[0789] The user's smartphone or in-vehicle display receives the LINE message and displays a notification.

[0790] The notification will include detailed advice on how to prevent and deal with illness caused by changes in atmospheric pressure.

[0791] User

[0792] Users implement the recommended measures and provide feedback on their effectiveness.

[0793] Feedback is sent to the server via LINE and will be used to improve the accuracy of the next notification advice.

[0794] Overview of program processing flow

[0795] Meteorological data acquisition and analysis

[0796] The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API, analyzes the data, predicts sudden changes in barometric pressure in a specific area, and stores the results in a database.

[0797] User information management

[0798] The server stores and manages each user's profile information in a database, including the area where they live, their past health history, and the effectiveness of specific measures.

[0799] Generate and send notification content

[0800] The server generates optimal advice for each individual user based on the barometric pressure forecast data and the user's profile information. The advice is then sent to the user's smartphone or in-vehicle display via the LINE official account API.

[0801] Gathering feedback and improving advice

[0802] Users provide feedback on the effectiveness of the advice they receive through LINE. The server receives and analyzes the feedback, updates the database, and uses machine learning algorithms to improve the accuracy of the next notification advice.

[0803] Specific examples and prompt examples for generative AI models

[0804] As a concrete example, if User A is riding in a vehicle in the Tokyo area and receives a forecast from the weather data API that the atmospheric pressure will drop sharply between 2:00 PM and 4:00 PM, the server will generate advice such as "The atmospheric pressure will drop sharply this afternoon. You are likely to experience a headache, so please make sure to stay hydrated in your vehicle and relax and rest," and notify the advice via LINE. User A can follow the advice and provide feedback, thereby contributing to improving the accuracy of the next advice.

[0805] Example prompts for generative AI models:

[0806] Please devise a system that provides advice and notifications to prevent users from becoming ill due to large changes in air pressure while riding in a vehicle. Specifically, please explain the following points:

[0807] 1. Meteorological data collection and analysis

[0808] 2. User profile information management

[0809] 3. Notification delivery via dedicated applications

[0810] 4. Collecting and Using User Feedback

[0811] Please indicate the hardware, software, and specific process steps required for each step.

[0812] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0813] Step 1:

[0814] The server periodically calls the weather agency's API to obtain weather data (temperature, pressure, wind speed, etc.). It uses the API key and region code as input, and obtains weather data in JSON format as output. This data is then analyzed to extract changes in pressure in specific regions and time periods. Data processing involves performing time series analysis of the pressure data to identify times when sudden changes in pressure are predicted.

[0815] Step 2:

[0816] The server reads each user's profile information from the database. Using the user ID as input, the server obtains profile information such as the area of ​​residence, health history, and effective countermeasures as output. Based on this information, users who are susceptible to changes in atmospheric pressure are identified. The data is processed by comparing the area information with health history data and analyzing the correlation.

[0817] Step 3:

[0818] The server generates individualized advice based on the barometric pressure forecast data and the user's profile information. The barometric pressure forecast data and profile information are used as input, and textual advice is generated as output. Data calculations involve matching the user's past health history with barometric pressure change patterns, and specific advice (e.g., hydration, rest, etc.) is included in the advice.

[0819] Step 4:

[0820] The server sends the generated advice text to the user's smartphone or in-vehicle display via the LINE Official Account API. LINE account information and advice text are used as input, and a LINE message is sent as output. Specifically, the server sends an API request to the LINE server and delivers the notification message.

[0821] Step 5:

[0822] The user puts into practice the advice received via LINE. The input is a LINE message, the output is putting the advice into practice, and observing the resulting changes in physical condition. Specific actions include following the advice and taking measures such as drinking water and resting.

[0823] Step 6:

[0824] The user provides feedback via LINE about the effectiveness of the measures they have implemented. As input, they record changes in their physical condition and send feedback via LINE messages. As output, the feedback information is sent to the server. Specific actions include inputting specific feedback such as "The advice was effective" or "My headache has subsided."

[0825] Step 7:

[0826] The server stores the received feedback in a database and analyzes it. The feedback data is used as input, and the feedback analysis results are obtained as output. For data calculations, the feedback data is input into a machine learning algorithm, and the advice generation model is retrained. This improves the accuracy of notification advice for the next barometric pressure change.

[0827] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0828] System Overview

[0829] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and in particular, combines an emotion engine that recognizes the user's emotions. The system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the profile information and emotional state managed for each user, and notifies the user's device via communication means such as LINE. Furthermore, it collects and analyzes feedback and emotional data from users, and uses this data to improve the accuracy of the advice.

[0830] Program processing flow

[0831] Collection and analysis of atmospheric pressure forecast data

[0832] server

[0833] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API. For example, it is set to retrieve the latest data every morning at 6:00.

[0834] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes most suddenly and the amount of change are calculated, and these events are saved in a database as important events.

[0835] User information management

[0836] server

[0837] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[0838] 4. Link with LINE accounts and organize information for each user.

[0839] 5. The server uses an emotion engine to analyze the user's emotional state in real time and record it in a database.

[0840] Generate and send notification content

[0841] server

[0842] 6. The server generates optimal countermeasure advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only countermeasures against barometric pressure changes but also the user's current emotional state.

[0843] For example, you might create advice like, "The atmospheric pressure will drop suddenly this afternoon. You can expect a headache, so make sure to stay hydrated and get some rest early. Taking some time to relax will also help ease your mood."

[0844] 7. The created advice is sent to the user's LINE account.

[0845] Terminal

[0846] 8. The user's device receives the LINE message and displays a notification within the app. The user confirms the notification.

[0847] Gathering feedback and improving advice

[0848] User

[0849] 9. The user takes action based on the notification, for example, drinking more water and taking timely rest.

[0850] 10. Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[0851] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[0852] server

[0853] 11. The server receives feedback and emotion data from users and stores it in a database.

[0854] 12. Analyze the collected feedback and sentiment data to assess whether a particular strategy was effective under specific conditions. This analysis is done using machine learning algorithms.

[0855] 13. The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[0856] Specific examples

[0857] Barometric pressure forecast and notifications

[0858] server

[0859] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0860] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0861] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[0862] 4. The emotion engine detects that User A's current emotional state is stress.

[0863] 5. Generate the following advice: "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early. You also seem to be stressed, so it's a good idea to take some time to relax."

[0864] 6. Send a notification to User A's LINE account via the LINE Official Account API.

[0865] Terminal

[0866] 7. User A's smartphone receives the LINE message and displays a notification.

[0867] Feedback and Improvements

[0868] User

[0869] 1. User A follows the advice and takes time to hydrate and relax.

[0870] 2. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[0871] server

[0872] 3. The server receives the feedback and emotion data and updates the database.

[0873] 4. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for the next barometric pressure change.

[0874] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0875] In this way, through a series of processes, the present invention not only prevents poor health caused by changes in atmospheric pressure and provides individualized, specific, and effective countermeasures, but also realizes a service that takes into consideration the user's emotional state.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] server

[0879] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[0880] Step 2:

[0881] server

[0882] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[0883] Step 3:

[0884] server

[0885] The server stores and manages each user's profile information (such as the area of ​​residence, past health data, and the effectiveness of specific measures) in a database. It also links the information to LINE accounts and organizes the information for each user.

[0886] Step 4:

[0887] server

[0888] The server uses an emotion engine to recognize the user's emotional state. This emotion data is obtained in real time from the user's LINE messages and other interactions and stored in a database.

[0889] Step 5:

[0890] server

[0891] The server generates optimal advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only measures to deal with barometric pressure changes but also the user's current emotional state.

[0892] For example, you could create advice like, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so make sure to stay hydrated and get some rest early. Also, you seem to be feeling stressed, so it's a good idea to take some time to relax."

[0893] Step 6:

[0894] server

[0895] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[0896] Step 7:

[0897] Terminal

[0898] The user's device receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[0899] Step 8:

[0900] User

[0901] Based on the notification, the user can try the suggested measures, such as staying hydrated and taking time to rest, or taking time to relax based on the advice of the emotion engine.

[0902] Step 9:

[0903] User

[0904] Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[0905] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[0906] Step 10:

[0907] server

[0908] The server receives user feedback and sentiment data and stores it in a database. The feedback includes details about the effectiveness of countermeasures.

[0909] Step 11:

[0910] server

[0911] The server analyzes the collected feedback and sentiment data to assess whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[0912] Step 12:

[0913] server

[0914] The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[0915] Step 13:

[0916] server

[0917] The server processes the new user profile information, emotion data, and barometric pressure forecast data using updated algorithms to prepare for generating future notification messages. This process is repeated continuously to ensure that the advice provided to the user is always optimal.

[0918] Specific examples

[0919] server

[0920] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[0921] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[0922] 3. Based on the profile information and current emotional state (stressed state) of User A who lives in Tokyo, the following advice is generated: "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. Also, it seems that you are stressed, so it would be a good idea to take some time to relax."

[0923] 4. Send a notification to User A's LINE account via the LINE official account.

[0924] Terminal

[0925] 5. User A's smartphone receives the LINE message and displays a notification.

[0926] User

[0927] 6. User A follows the advice and takes time to hydrate and relax.

[0928] 7. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[0929] server

[0930] 8. The server receives the feedback and emotion data and updates the database.

[0931] 9. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for upcoming barometric pressure changes.

[0932] 10. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[0933] In this way, the present invention not only prevents poor health due to changes in atmospheric pressure, but also takes into consideration the user's emotional state, thereby realizing a system that provides more personalized advice and kind words.

[0934] Example 2

[0935] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0936] For people who experience poor health due to changes in atmospheric pressure, effective and timely measures are needed. However, existing systems have difficulty providing personalized advice based on changes in atmospheric pressure. Furthermore, they are unable to generate advice that takes into account the user's emotional state, which limits the effectiveness of the measures.

[0937] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0938] In this invention, the server includes means for acquiring weather information, means for analyzing the acquired weather information and predicting changes in atmospheric pressure at a specific location, means for managing personal information of a user, means for generating countermeasure advice for a user based on the acquired changes in atmospheric pressure, means for transmitting the generated countermeasure advice to the user via communication means, means for analyzing the emotional state of the user, means for collecting feedback from the user and analyzing the data, means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, and means for transmitting the generated countermeasure advice. This makes it possible to provide effective countermeasures tailored to individual needs for dealing with poor health caused by changes in atmospheric pressure.

[0939] "Weather information" refers to data such as temperature, air pressure, humidity, wind speed, and precipitation obtained from meteorological agencies.

[0940] "Means of acquisition" refers to the programs and hardware used to periodically collect weather information through APIs of external meteorological agencies, etc.

[0941] "Analysis means" refers to algorithms and programs for processing collected weather information and predicting changes in atmospheric pressure at specific locations and times.

[0942] "Personal information" refers to a set of information about the user, such as the area where the user lives, past health conditions, and regular mood states.

[0943] "Management means" refers to the programs and hardware used to register, update, and delete users' personal information and profiles in the database.

[0944] "Measure advice generation means" refers to an algorithm and program for automatically generating appropriate action suggestions based on changes in atmospheric pressure and the user's personal information.

[0945] "Communication means" refers to an internet connection and communication application for transmitting generated countermeasure advice to a user.

[0946] "Emotional state analysis means" refers to natural language processing and machine learning algorithms used to analyze user-provided text data and identify a user's current emotional state.

[0947] "Feedback collection means" refers to a program and hardware for receiving feedback and emotional data from users and registering it in a database.

[0948] A "machine learning algorithm" refers to a program that uses statistical and mathematical methods to learn patterns from large amounts of data and make future predictions and classifications.

[0949] The present invention is a system for preventing poor health caused by changes in atmospheric pressure, and is particularly combined with an emotion engine that recognizes the user's emotions. This system is built with the cooperation of a server, terminals, and users.

[0950] Server Operation

[0951] The server collects and analyzes weather information, manages user information, generates and sends advice on countermeasures, and collects and analyzes feedback. These operations are performed using the following specific hardware and software:

[0952] 1. Obtaining weather information:

[0953] The server calls the weather agency's API every morning at 6:00 to obtain the latest barometric pressure forecast data. This communication is made via an HTTP request, and the data is returned in JSON format.

[0954] 2. Meteorological information analysis:

[0955] The server parses the received JSON data and calculates the change in atmospheric pressure for a specific location and time period, and stores the results in a database.

[0956] 3. Managing User Information:

[0957] User profile information (such as the area of ​​residence and past health history) is stored in a database on the server. LINE account information is also linked via API.

[0958] 4. Emotional state analysis:

[0959] The server analyzes the text data provided by the user using natural language processing (NLP) and machine learning algorithms (e.g., sentiment analysis models) to identify the user's emotional state, which is also stored in a database.

[0960] 5. Generate action advice:

[0961] The server uses a generative AI model to generate optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The advice is written in natural language using, for example, a Python NLP library.

[0962] 6. Sending advice:

[0963] The server uses the LINE Official Account API to send the generated advice to the user's LINE account.

[0964] 7. Feedback Collection and Analysis:

[0965] The server receives user feedback and sentiment data and stores it in a database, which is then fed into a machine learning algorithm to improve the accuracy of next time's advice.

[0966] Device behavior

[0967] The user's device (e.g., a smartphone) receives the notification sent from the server through the LINE app and displays it to the user, allowing the user to check and implement the proposed measures.

[0968] User Actions

[0969] The user receives a notification from the server through the LINE app, implements the proposed measures, and provides feedback on their effectiveness. This feedback should also include the user's emotional state.

[0970] Specific examples

[0971] 1. Barometric pressure forecast and notifications:

[0972] The server calls the weather data API every morning at 6:00 and obtains barometric pressure forecast data for the Tokyo area.

[0973] The analysis confirms that the air pressure drops sharply between 2:00 and 4:00 p.m.

[0974] Based on the profile information of user A who lives in Tokyo, it is recognized that he / she suffers from frequent headaches.

[0975] The emotion engine detects that you are feeling stressed and generates advice such as, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. It seems that you are stressed, so it would also be a good idea to take some time to relax."

[0976] The server sends a notification to User A via the LINE Official Account API.

[0977] User A's smartphone receives the LINE message and displays a notification.

[0978] 2. Feedback and Improvement:

[0979] User A follows the advice and takes time to hydrate and relax.

[0980] Feedback is sent via LINE along with the patient's emotional state, providing specific feedback such as, "The advice was effective, my headache has subsided, and I feel a little better."

[0981] The server receives the feedback and stores it in a database.

[0982] Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of next notification advice.

[0983] Prompt Sentence Examples

[0984] An example of a prompt sentence to be input to the emotion engine is, "Tell me about your emotional state recently. Are you feeling stressed or anxious?" Based on this prompt sentence, the user inputs their own emotions in text format, which is then analyzed.

[0985] In this way, the present invention provides specific and effective countermeasures to prevent health problems caused by changes in atmospheric pressure, and also realizes more personalized services by taking into account the user's emotional state.

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Step 1:

[0988] The server retrieves the weather information.

[0989] Input: Weather agency API endpoint and credentials.

[0990] Specific operation: Sends an HTTP request every morning at 6:00 and receives data in JSON format.

[0991] Output: Retrieved weather data in JSON format.

[0992] Step 2:

[0993] The server analyzes the weather information it receives and predicts changes in atmospheric pressure at specific locations and times.

[0994] Input: Weather data in JSON format.

[0995] Specific operation: Parse the JSON data, split it by region and time zone, and calculate the change in atmospheric pressure.

[0996] Output: Analyzed pressure forecast data.

[0997] Step 3:

[0998] The server manages the user's personal information.

[0999] Input: Profile information registered by the user (address, past health status, etc.).

[1000] Specific operation: User information is stored and managed in a database. Also, LINE account information is linked via API.

[1001] Output: User profile information stored in a database.

[1002] Step 4:

[1003] The server analyzes the user's emotional state.

[1004] Input: User-provided text data (e.g., diary entries or messages).

[1005] What it does: Uses a generative AI model to analyze text data and identify emotional states.

[1006] Output: Parsed emotional state data.

[1007] Step 5:

[1008] The server generates countermeasure advice based on barometric pressure forecast data, the user's personal information, and their emotional state.

[1009] Input: Parsed barometric pressure forecast data, user profile information, emotional state data.

[1010] What it does: Uses generative AI models to generate personalized countermeasure advice.

[1011] Output: The generated remediation advice text.

[1012] Step 6:

[1013] The server transmits the generated countermeasure advice to the user via a communication means.

[1014] Input: Generated advice text, user's LINE account information.

[1015] Specific operation: Advice on countermeasures is sent as a LINE message via the LINE official account API.

[1016] Output: Send a message to the user's LINE account.

[1017] Step 7:

[1018] The device receives the LINE message sent from the server and displays a notification.

[1019] Input: The LINE message sent from the server.

[1020] What happens: The LINE application displays the notification as a banner, and when the user taps on the message, more details are displayed.

[1021] Output: A notification is displayed to the user.

[1022] Step 8:

[1023] The user implements the measures notified.

[1024] Input: Countermeasure advice received via the LINE app.

[1025] Specific actions: Implement suggested measures (e.g., hydration, rest).

[1026] Output: Results of the implementation of the measures.

[1027] Step 9:

[1028] Users provide feedback on the effectiveness of the measures.

[1029] Input: Results of taking the measures and emotional state.

[1030] Specific behavior: Use the LINE application to enter and send feedback in text format.

[1031] Output: The feedback data sent.

[1032] Step 10:

[1033] The server receives feedback and emotion data from users and stores it in a database.

[1034] Input: Feedback data and emotional state data.

[1035] Specific operation: Save the received data in the database.

[1036] Output: Feedback data stored in a database.

[1037] Step 11:

[1038] The server inputs the collected feedback data and sentiment data into a machine learning algorithm to improve the accuracy of the next countermeasure advice.

[1039] Input: Feedback data and emotional state data.

[1040] Specific operation: A machine learning algorithm is used to analyze the feedback data and update the generative model for countermeasure advice.

[1041] Output: An improved model for generating countermeasure advice.

[1042] Through the above processing steps, a system is realized that prevents users from becoming ill due to changes in atmospheric pressure and provides individual and effective countermeasures for the user.

[1043] (Application example 2)

[1044] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1045] In modern society, changes in atmospheric pressure are a major cause of poor health for many people. In addition, people are exposed to various stressors every day, increasing the risk of poor health. However, current systems are unable to provide prompt and appropriate countermeasures against these changes in atmospheric pressure and stress, resulting in increased health risks. Furthermore, there is a lack of a mechanism for effectively utilizing feedback on health advice to improve the accuracy of countermeasures. Another issue is the lack of a means to smoothly purchase the necessary products as countermeasures.

[1046] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for analyzing the user's emotional state using an emotion analysis engine, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure and the user's emotional state, and means for providing electronic payment functionality and enabling the user to purchase health-related products. This makes it possible to provide personalized advice that takes into account changes in atmospheric pressure and the user's emotional state, and to smoothly purchase necessary countermeasure products. In addition, the server can collect user feedback and use machine learning algorithms to improve the accuracy of the advice.

[1047] "Weather data" is information that indicates atmospheric conditions and changes, and includes elements such as temperature, humidity, air pressure, wind speed, and wind direction.

[1048] An "emotion analysis engine" is software that analyzes a user's emotional state based on their text data and biometric information, and distinguishes between emotions such as anger, sadness, joy, and stress.

[1049] "Electronic payment function" is a system that allows users to pay for goods and services via the Internet, and includes payment methods such as credit cards, debit cards, and electronic money.

[1050] "Feedback" is information provided by the user, and is data that collects reactions such as the effectiveness of advice, changes in physical condition, and usage impressions.

[1051] "Machine learning algorithms" are a field of artificial intelligence that uses patterns learned from past data to make predictions and judgments about new data.

[1052] "Advice on countermeasures" is information that indicates specific actions and precautions that the user should take based on changes in atmospheric pressure and the user's emotional state, and includes suggestions for health management and mood improvement.

[1053] System Overview

[1054] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and is configured with an emotion analysis engine that analyzes the user's emotional state. The system includes a function for collecting and analyzing weather data and predicting changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the acquired data, individual user profile information, and emotion analysis results, and provides a mechanism for enabling users to purchase necessary health products using an electronic payment function.

[1055] System Details

[1056] Meteorological data collection and analysis tools

[1057] The server periodically obtains the latest barometric pressure forecast data via the weather agency's API. For example, it collects data every morning at 6:00 and predicts the daily barometric pressure change in a specific area. If a sudden change in barometric pressure is expected, it analyzes the time and amount of change and stores it in the database as an important event.

[1058] Emotion analysis means

[1059] The server uses an emotion analysis engine to analyze the user's emotional state in real time, which involves analyzing the user's text data and biometric information (e.g., heart rate and skin conductance) to determine emotional states such as stress, anger, and joy.

[1060] How to generate and send advice

[1061] The server generates optimal advice based on the collected barometric pressure forecast data, the user's profile information, and their emotional state. This advice is sent to the user's device via a communication method such as LINE. For example, specific advice such as "This afternoon, the barometric pressure will drop suddenly. You are likely to experience a headache, so it would be a good idea to stay hydrated and rest early. We also recommend that you take some time to relax."

[1062] Electronic payment facility

[1063] The app also provides an electronic payment function for users to purchase necessary health products. For example, health drinks and relaxation items recommended based on advice can be purchased directly within the app. This function allows users to receive advice and purchase products in a single flow.

[1064] Feedback collection and analysis methods

[1065] After implementing the advice, the user provides feedback on its effectiveness. This feedback data is collected along with the user's emotional state. The server analyzes this data and uses machine learning algorithms to improve the accuracy of future advice. For example, the server can improve the quality of the generated advice based on feedback such as, "The advice was effective, my headache was relieved, and I feel a little better."

[1066] Hardware and software used

[1067] Hardware: Smartphone

[1068] Software: LINE Bot API, weather data API, sentiment analysis engine, Python platform

[1069] Example prompt: "User A is experiencing a drop in barometric pressure at 2 PM. He is likely to have a headache, so he should stay hydrated and get some rest. He also seems to be feeling stressed, so it's a good idea to take some time to relax."

[1070] Specific examples

[1071] Example 1: User A, who lives in Tokyo, receives barometric pressure forecast information every morning via an app. This morning, the app predicted that barometric pressure would drop sharply between 2:00 PM and 4:00 PM, and sent User A advice on what to do via LINE. The advice sent reads, "Barometric pressure will drop sharply this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. You are also feeling stressed, so we recommend that you take some time to relax."

[1072] Example 2: User A purchased the recommended health drink within the app using the electronic payment function and hydrated. When the atmospheric pressure dropped in the afternoon, the advance precautions prevented a headache. That evening, User A sent feedback via LINE that the advice was effective. The server applies a machine learning algorithm based on this feedback data to improve the accuracy of the advice.

[1073] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1074] Step 1:

[1075] The server obtains the latest barometric pressure forecast data from the meteorological agency's API. It calls the API every morning at 6:00 to collect barometric pressure data for each region. The input is the weather API endpoint, and the output is the barometric pressure forecast data for each region. It analyzes the data, identifies times of day when barometric pressure changes most suddenly, and stores the data in a database.

[1076] Step 2:

[1077] The server manages user profile information. It stores and updates the database with information collected at the time of initial registration, such as the area of ​​residence, past health conditions, and addictive measures. The input is the user's registration information, and the output is each user's profile data.

[1078] Step 3:

[1079] The server uses an emotion analysis engine to analyze the user's emotional state in real time. It determines emotions based on LINE messages and biometric data and records them in a database. The input is text data and biometric information, and the output is the analyzed emotional state.

[1080] Step 4:

[1081] The server generates optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The generated advice is sent to the user's device as a LINE message. The input is barometric pressure data, profile information, and their emotional state, and the output is an advice message. A generative AI model is used to create the advice.

[1082] Step 5:

[1083] The user receives the LINE message and takes the necessary measures according to the advice, such as staying hydrated or taking time to relax. The input is the received advice message, and the output is the implemented measures.

[1084] Step 6:

[1085] Users provide feedback on the effectiveness of the measures. They report the effectiveness along with their emotional state via LINE. The input is the emotional state after the measures are implemented and the feedback content, and the output is the sent feedback data.

[1086] Step 7:

[1087] The server collects user feedback and stores it in a database. It analyzes the feedback data and uses a machine learning algorithm to improve the countermeasure advice generation algorithm. The input is the feedback data, and the output is the improved advice generation algorithm.

[1088] Step 8:

[1089] The server improves the accuracy of the notification advice for the next barometric pressure change based on the improved advice generation algorithm. The newly generated advice message provides more accurate measures for the next barometric pressure change. The input is the improved algorithm, and the output is a more accurate advice message.

[1090] 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.

[1091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1092] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1093] [Third embodiment]

[1094] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1095] 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.

[1096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1097] 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.

[1098] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1100] 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.

[1101] 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.

[1102] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1103] 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.

[1104] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1105] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1106] System Overview

[1107] This invention is a system for preventing poor health caused by changes in atmospheric pressure. This system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on profile information managed for each user and notifies the user's device via a communication method such as LINE. It also collects and analyzes feedback from users and uses that data to improve the accuracy of the advice.

[1108] Program processing flow

[1109] Collection and analysis of atmospheric pressure forecast data

[1110] server

[1111] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API.

[1112] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes suddenly and the amount of change are calculated.

[1113] User information management

[1114] server

[1115] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[1116] 4. Link with LINE accounts and organize information for each user.

[1117] Generate and send notification content

[1118] server

[1119] 5. The server generates optimal countermeasure advice for each individual user based on the barometric pressure forecast data and the user's profile information.

[1120] For example, create specific advice such as, "The atmospheric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[1121] 6. The created advice is sent to the user's LINE account.

[1122] Terminal

[1123] 7. The user's device receives the LINE message and displays a notification.

[1124] Gathering feedback and improving advice

[1125] User

[1126] 8. The user tries out the proposed measures and sends feedback on their effectiveness via LINE.

[1127] For example, provide specific feedback such as, "The advice was effective and relieved my headache. I would like to try this method again next time."

[1128] server

[1129] 9. The server receives the feedback from the user and stores it in a database.

[1130] 10. Analyze the collected feedback data and evaluate whether specific measures were effective.

[1131] 11. Using machine learning algorithms, we will update the system to improve the accuracy of the next notification advice.

[1132] Specific examples

[1133] Barometric pressure forecast and notifications

[1134] server

[1135] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1136] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1137] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[1138] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[1139] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[1140] Terminal

[1141] 6. User A's smartphone receives the LINE message and displays a notification.

[1142] Feedback and Improvements

[1143] User

[1144] 1. User A follows the advice and practices hydration and early rest.

[1145] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[1146] server

[1147] 3. The server receives the feedback and updates the database.

[1148] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[1149] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1150] In this way, the present invention provides a series of processes to prevent poor health caused by changes in atmospheric pressure and to provide individualized, specific, and effective countermeasures.

[1151] The processing flow will be explained below.

[1152] Step 1:

[1153] server

[1154] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[1155] Step 2:

[1156] server

[1157] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[1158] Step 3:

[1159] server

[1160] The server maintains a database of user profile information, including the area of ​​residence, past health history, and the effectiveness of each measure.

[1161] Step 4:

[1162] server

[1163] The server combines the user profile information in the database with the barometric pressure forecast data to generate personalized advice for each user, such as "The barometric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[1164] Step 5:

[1165] server

[1166] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[1167] Step 6:

[1168] Terminal

[1169] The user's smartphone receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[1170] Step 7:

[1171] User

[1172] Based on the notification, the user can try the suggested measures, such as drinking more water and taking timely rest.

[1173] Step 8:

[1174] User

[1175] Users can send feedback to the official LINE account about the effectiveness of the measures, providing specific feedback such as, "The advice was effective and my headache has been reduced."

[1176] Step 9:

[1177] server

[1178] The server receives feedback from users and stores it in a database, including details about the effectiveness of the measures.

[1179] Step 10:

[1180] server

[1181] The server analyzes the collected feedback data and evaluates whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[1182] Step 11:

[1183] server

[1184] The server updates the algorithm for generating countermeasure advice based on the analysis results, thereby improving the accuracy of the next notification advice.

[1185] Step 12:

[1186] server

[1187] The server processes the new user profile information and barometric pressure forecast data using updated algorithms, preparing to generate future notification messages. This process is repeated continuously to ensure the advice provided to users is always optimal.

[1188] Example 1

[1189] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1190] It is widely known that changes in atmospheric pressure have an impact on people's health, with many suffering from symptoms such as headaches and weather-related illnesses. However, current technology makes it difficult to take effective measures in response to changes in atmospheric pressure, and no system exists that provides specific advice based on individual profiling. Therefore, there is a need for a system that can predict changes in atmospheric pressure and provide individually optimized advice based on that information.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1192] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, thereby making it possible to provide individualized, specific, and effective countermeasures in response to changes in atmospheric pressure.

[1193] "Weather data" refers to weather-related information such as weather forecasts, temperature, air pressure, and precipitation provided by meteorological agencies.

[1194] "Analysis" is the act of processing acquired data and extracting specific insights and figures.

[1195] "Changes in atmospheric pressure" refers to an increase or decrease in atmospheric pressure over a specific period of time.

[1196] "Prediction" is the estimation of future states or events based on acquired data.

[1197] "User profile information" is individually managed information such as the user's area of ​​residence, past health data, and the effectiveness of countermeasures.

[1198] "Management" refers to a series of processes including collecting, storing, updating, and deleting data.

[1199] "Countermeasure advice" is specific guidelines and advice provided to the user to deal with changes in atmospheric pressure.

[1200] "Sending" is the act of delivering the generated information to a recipient through a specific communication means.

[1201] "Feedback" refers to information such as execution results, opinions, and effects provided by users.

[1202] A "machine learning algorithm" is a computational method for learning patterns from collected data and making predictions and classifications.

[1203] System Overview

[1204] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The server acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. Optimal countermeasure advice is generated based on profile information managed for each user, and notified to the user's terminal via communication means. Furthermore, feedback from users is collected and analyzed to improve the accuracy of the next advice.

[1205] Specific processing flow

[1206] 1. Collecting atmospheric pressure forecast data

[1207] The server periodically calls the API of the meteorological agency (e.g., OpenWeatherMap or the API of the Japan Meteorological Agency) to obtain the latest barometric pressure forecast data. The obtained data is stored on the server in JSON format.

[1208] 2. Analysis of atmospheric pressure data

[1209] The server analyzes the acquired atmospheric pressure data using data analysis tools such as Pandas and organizes it by region and time period, allowing the time periods when atmospheric pressure suddenly changes and the magnitude of those changes to be calculated.

[1210] 3. Management of User Information

[1211] The server stores and manages each user's profile information (such as the area they live in, past health data, and the effectiveness of measures they have taken) in a database (e.g., PostgreSQL). It also links the information to their LINE accounts, and organizes the information for each user.

[1212] 4. Generating and sending notification content

[1213] The server uses a generative AI model (e.g., GPT-3) based on the barometric pressure forecast data and the user's profile information to generate optimal advice. A specific example of advice might be, "The barometric pressure will drop suddenly this afternoon. You are likely to experience a headache, so it's a good idea to stay hydrated and get some rest early." The generated advice is sent to the user's LINE account via the LINE Official Account API.

[1214] 5. Gathering Feedback

[1215] The user implements the proposed measures and sends feedback on their effectiveness via LINE, such as "The advice was effective and my headache has subsided."

[1216] 6. Improved advice accuracy

[1217] The server stores the collected feedback in a database and analyzes it using machine learning algorithms (e.g., Scikit-learn or TensorFlow), which then updates the system to improve the accuracy of the next notification advice.

[1218] Specific examples

[1219] Barometric pressure forecast and notifications

[1220] server

[1221] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1222] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1223] 3. Refer to the profile information of User A who lives in Tokyo and recognize that he tends to suffer from frequent headaches during periods of low pressure.

[1224] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[1225] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[1226] Terminal

[1227] 6. User A's smartphone receives the LINE message and displays a notification.

[1228] Feedback and Improvements

[1229] User

[1230] 1. User A follows the advice and hydrates and takes an early rest.

[1231] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[1232] server

[1233] 3. The server receives the feedback and updates the database.

[1234] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[1235] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1236] Through the above process, the present invention prevents poor health caused by changes in atmospheric pressure and provides users with individualized and effective countermeasures.

[1237] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1238] Program processing flow

[1239] Step 1: Collect pressure forecast data

[1240] server

[1241] 1. The server calls the weather agency's API (e.g., OpenWeatherMap, Japan Meteorological Agency's API) every morning at 6:00.

[1242] Input: API key, region-specific request parameters

[1243] Specific operation: Uses Python's requests module to send an HTTP GET request to obtain barometric pressure forecast data.

[1244] Output: Receives barometric pressure forecast data in JSON format.

[1245] Step 2: Analyze the pressure data

[1246] server

[1247] 1. The server analyzes the acquired atmospheric pressure data.

[1248] Input: Pressure forecast data in JSON format

[1249] Specific operation: Use the Pandas library to convert the data into a data frame and perform analysis.

[1250] Output: Pressure change points and their magnitudes organized by region and time period

[1251] Step 3: Managing User Information

[1252] server

[1253] 1. The server retrieves each user's profile information from the database.

[1254] Input: User ID or LINE account ID

[1255] Specific operation: Retrieve user profile information from the PostgreSQL database using a SELECT query.

[1256] Output: Profile information (area of ​​residence, past health status, etc.)

[1257] Step 4: Generate notification content

[1258] server

[1259] 1. The server generates countermeasure advice based on barometric pressure forecast data and the user's profile information.

[1260] Input: Analyzed barometric pressure data, user profile information

[1261] Specific operation: A prompt sentence is input into a generative AI model (e.g., GPT-3) to generate specific countermeasure advice.

[1262] Example prompt: "Generate specific advice for a specific user to prevent headaches when the atmospheric pressure drops in the Tokyo area."

[1263] Output: Advice: "This afternoon, the atmospheric pressure will drop suddenly. You may experience a headache, so make sure to stay hydrated and get some rest."

[1264] Step 5: Submitting Advice

[1265] server

[1266] 1. The server sends the generated advice to the user via the LINE Official Account API.

[1267] Input: Generated countermeasure advice, user's LINE account ID

[1268] Specific operation: Uses the LINE Messaging API to send a POST request and deliver the advice.

[1269] Output: A notification message will be sent to the user's LINE account.

[1270] Terminal

[1271] 2. The user's device receives the LINE message and displays a notification.

[1272] Input: Message received from the LINE server

[1273] Specific behavior: The LINE app displays a notification.

[1274] Output: A notification is displayed on the user's smartphone.

[1275] Step 6: Gather feedback

[1276] User

[1277] 1. The user implements the proposed measures and provides feedback on their effectiveness.

[1278] Input: Impressions after implementing measures and evaluation of their effectiveness

[1279] Specific operation: Send a feedback message through the LINE app.

[1280] Output: Feedback data is sent to the server.

[1281] server

[1282] 2. The server receives the feedback from the user and stores it in a database.

[1283] Input: User feedback message

[1284] Specific operation: Data received via the LINE Messaging API is saved in a PostgreSQL database.

[1285] Output: The feedback data is stored in a database.

[1286] Step 7: Improve the accuracy of your advice

[1287] server

[1288] 1. The server analyzes the collected feedback data.

[1289] Input: Saved feedback data

[1290] Specific operations: Analyze data using machine learning algorithms (e.g., Scikit-learn, TensorFlow).

[1291] Output: A new learning model for improving the accuracy of countermeasure advice

[1292] 2. The server uses machine learning models to improve the accuracy of countermeasure advice.

[1293] Input: A new learning model

[1294] Specific operation: The generative AI model is updated and reflected in the next notification.

[1295] Output: Improved countermeasure advice is generated.

[1296] (Application example 1)

[1297] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1298] Changes in atmospheric pressure can have a significant impact on a user's physical condition. Specifically, in moving objects (such as self-driving vehicles), it is difficult to manage the user's physical condition while in the vehicle, so there is concern that sudden changes in atmospheric pressure may cause poor health. Therefore, there is a need for prevention and countermeasures against poor health caused by changes in atmospheric pressure in moving objects. In addition, there is a need for a system that can provide appropriate advice to individual users and collect feedback to improve the accuracy of the advice.

[1299] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1300] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for notifying the user of the countermeasure advice within the mobile vehicle, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice based on the collected feedback. This makes it possible to individually provide prevention and countermeasures for poor health caused by changes in atmospheric pressure, and effectively manage the user's physical condition even within the mobile vehicle.

[1301] "Weather data" refers to information about atmospheric conditions such as temperature, humidity, air pressure, and wind speed.

[1302] "Analysis" refers to the process of processing acquired data and information to derive meaning and trends.

[1303] "Specific region" refers to a limited geographical area, such as a specific city or area.

[1304] "Changes in atmospheric pressure" refers to increases or decreases in atmospheric pressure over a certain period of time.

[1305] "Prediction" refers to estimating future events or conditions with a certain degree of accuracy.

[1306] "User profile information" refers to data about an individual user, including name, address, health status, and past health history.

[1307] "Advice" refers to specific suggestions or instructions for dealing with a particular situation or problem.

[1308] "Sending" refers to the act of delivering information to a specific recipient.

[1309] "Mobile object" refers to a machine or device that can change position, such as an autonomous vehicle.

[1310] "Feedback" refers to reactions and opinions from users in response to information or advice received.

[1311] "Accuracy" refers to the degree to which the results of predictions, analyses, etc. are accurate.

[1312] "Means" refers to a method or device for achieving a particular purpose or result.

[1313] System Overview

[1314] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The system aims to manage the user's health while in a mobile vehicle by acquiring and analyzing weather data and predicting changes in atmospheric pressure. It also manages profile information for each user and generates and notifies optimal countermeasure advice. It also collects and analyzes feedback from users and uses that data to improve the accuracy of advice.

[1315] Hardware and software used

[1316] server

[1317] The server retrieves the latest barometric pressure forecast data using a weather data API, for example, the API at weatherapi.com.

[1318] The acquired data is analyzed and organized by region and time period.

[1319] Each user's profile information is managed in a database and notifications are sent via the LINE Official Account API.

[1320] Terminal

[1321] The user's smartphone or in-vehicle display receives the LINE message and displays a notification.

[1322] The notification will include detailed advice on how to prevent and deal with illness caused by changes in atmospheric pressure.

[1323] User

[1324] Users implement the recommended measures and provide feedback on their effectiveness.

[1325] Feedback is sent to the server via LINE and will be used to improve the accuracy of the next notification advice.

[1326] Overview of program processing flow

[1327] Meteorological data acquisition and analysis

[1328] The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API, analyzes the data, predicts sudden changes in barometric pressure in a specific area, and stores the results in a database.

[1329] User information management

[1330] The server stores and manages each user's profile information in a database, including the area where they live, their past health history, and the effectiveness of specific measures.

[1331] Generate and send notification content

[1332] The server generates optimal advice for each individual user based on the barometric pressure forecast data and the user's profile information. The advice is then sent to the user's smartphone or in-vehicle display via the LINE official account API.

[1333] Gathering feedback and improving advice

[1334] Users provide feedback on the effectiveness of the advice they receive through LINE. The server receives and analyzes the feedback, updates the database, and uses machine learning algorithms to improve the accuracy of the next notification advice.

[1335] Specific examples and prompt examples for generative AI models

[1336] As a concrete example, if User A is riding in a vehicle in the Tokyo area and receives a forecast from the weather data API that the atmospheric pressure will drop sharply between 2:00 PM and 4:00 PM, the server will generate advice such as "The atmospheric pressure will drop sharply this afternoon. You are likely to experience a headache, so please make sure to stay hydrated in your vehicle and relax and rest," and notify the advice via LINE. User A can follow the advice and provide feedback, thereby contributing to improving the accuracy of the next advice.

[1337] Example prompts for generative AI models:

[1338] Please devise a system that provides advice and notifications to prevent users from becoming ill due to large changes in air pressure while riding in a vehicle. Specifically, please explain the following points:

[1339] 1. Meteorological data collection and analysis

[1340] 2. User profile information management

[1341] 3. Notification delivery via dedicated applications

[1342] 4. Collecting and Using User Feedback

[1343] Please indicate the hardware, software, and specific process steps required for each step.

[1344] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1345] Step 1:

[1346] The server periodically calls the weather agency's API to obtain weather data (temperature, pressure, wind speed, etc.). It uses the API key and region code as input, and obtains weather data in JSON format as output. This data is then analyzed to extract changes in pressure in specific regions and time periods. Data processing involves performing time series analysis of the pressure data to identify times when sudden changes in pressure are predicted.

[1347] Step 2:

[1348] The server reads each user's profile information from the database. Using the user ID as input, the server obtains profile information such as the area of ​​residence, health history, and effective countermeasures as output. Based on this information, users who are susceptible to changes in atmospheric pressure are identified. The data is processed by comparing the area information with health history data and analyzing the correlation.

[1349] Step 3:

[1350] The server generates individualized advice based on the barometric pressure forecast data and the user's profile information. The barometric pressure forecast data and profile information are used as input, and textual advice is generated as output. Data calculations involve matching the user's past health history with barometric pressure change patterns, and specific advice (e.g., hydration, rest, etc.) is included in the advice.

[1351] Step 4:

[1352] The server sends the generated advice text to the user's smartphone or in-vehicle display via the LINE Official Account API. LINE account information and advice text are used as input, and a LINE message is sent as output. Specifically, the server sends an API request to the LINE server and delivers the notification message.

[1353] Step 5:

[1354] The user puts into practice the advice received via LINE. The input is a LINE message, the output is putting the advice into practice, and observing the resulting changes in physical condition. Specific actions include following the advice and taking measures such as drinking water and resting.

[1355] Step 6:

[1356] The user provides feedback via LINE about the effectiveness of the measures they have implemented. As input, they record changes in their physical condition and send feedback via LINE messages. As output, the feedback information is sent to the server. Specific actions include inputting specific feedback such as "The advice was effective" or "My headache has subsided."

[1357] Step 7:

[1358] The server stores the received feedback in a database and analyzes it. The feedback data is used as input, and the feedback analysis results are obtained as output. For data calculations, the feedback data is input into a machine learning algorithm, and the advice generation model is retrained. This improves the accuracy of notification advice for the next barometric pressure change.

[1359] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1360] System Overview

[1361] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and in particular, combines an emotion engine that recognizes the user's emotions. The system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the profile information and emotional state managed for each user, and notifies the user's device via communication means such as LINE. Furthermore, it collects and analyzes feedback and emotional data from users, and uses this data to improve the accuracy of the advice.

[1362] Program processing flow

[1363] Collection and analysis of atmospheric pressure forecast data

[1364] server

[1365] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API. For example, it is set to retrieve the latest data every morning at 6:00.

[1366] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes most suddenly and the amount of change are calculated, and these events are saved in a database as important events.

[1367] User information management

[1368] server

[1369] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[1370] 4. Link with LINE accounts and organize information for each user.

[1371] 5. The server uses an emotion engine to analyze the user's emotional state in real time and record it in a database.

[1372] Generate and send notification content

[1373] server

[1374] 6. The server generates optimal countermeasure advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only countermeasures against barometric pressure changes but also the user's current emotional state.

[1375] For example, you might create advice like, "The atmospheric pressure will drop suddenly this afternoon. You can expect a headache, so make sure to stay hydrated and get some rest early. Taking some time to relax will also help ease your mood."

[1376] 7. The created advice is sent to the user's LINE account.

[1377] Terminal

[1378] 8. The user's device receives the LINE message and displays a notification within the app. The user confirms the notification.

[1379] Gathering feedback and improving advice

[1380] User

[1381] 9. The user takes action based on the notification, for example, drinking more water and taking timely rest.

[1382] 10. Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[1383] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[1384] server

[1385] 11. The server receives feedback and emotion data from users and stores it in a database.

[1386] 12. Analyze the collected feedback and sentiment data to assess whether a particular strategy was effective under specific conditions. This analysis is done using machine learning algorithms.

[1387] 13. The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[1388] Specific examples

[1389] Barometric pressure forecast and notifications

[1390] server

[1391] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1392] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1393] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[1394] 4. The emotion engine detects that User A's current emotional state is stress.

[1395] 5. Generate the following advice: "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early. You also seem to be stressed, so it's a good idea to take some time to relax."

[1396] 6. Send a notification to User A's LINE account via the LINE Official Account API.

[1397] Terminal

[1398] 7. User A's smartphone receives the LINE message and displays a notification.

[1399] Feedback and Improvements

[1400] User

[1401] 1. User A follows the advice and takes time to hydrate and relax.

[1402] 2. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[1403] server

[1404] 3. The server receives the feedback and emotion data and updates the database.

[1405] 4. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for the next barometric pressure change.

[1406] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1407] In this way, through a series of processes, the present invention not only prevents poor health caused by changes in atmospheric pressure and provides individualized, specific, and effective countermeasures, but also realizes a service that takes into consideration the user's emotional state.

[1408] The processing flow will be explained below.

[1409] Step 1:

[1410] server

[1411] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[1412] Step 2:

[1413] server

[1414] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[1415] Step 3:

[1416] server

[1417] The server stores and manages each user's profile information (such as the area of ​​residence, past health data, and the effectiveness of specific measures) in a database. It also links the information to LINE accounts and organizes the information for each user.

[1418] Step 4:

[1419] server

[1420] The server uses an emotion engine to recognize the user's emotional state. This emotion data is obtained in real time from the user's LINE messages and other interactions and stored in a database.

[1421] Step 5:

[1422] server

[1423] The server generates optimal advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only measures to deal with barometric pressure changes but also the user's current emotional state.

[1424] For example, you could create advice like, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so make sure to stay hydrated and get some rest early. Also, you seem to be feeling stressed, so it's a good idea to take some time to relax."

[1425] Step 6:

[1426] server

[1427] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[1428] Step 7:

[1429] Terminal

[1430] The user's device receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[1431] Step 8:

[1432] User

[1433] Based on the notification, the user can try the suggested measures, such as staying hydrated and taking time to rest, or taking time to relax based on the advice of the emotion engine.

[1434] Step 9:

[1435] User

[1436] Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[1437] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[1438] Step 10:

[1439] server

[1440] The server receives user feedback and sentiment data and stores it in a database. The feedback includes details about the effectiveness of countermeasures.

[1441] Step 11:

[1442] server

[1443] The server analyzes the collected feedback and sentiment data to assess whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[1444] Step 12:

[1445] server

[1446] The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[1447] Step 13:

[1448] server

[1449] The server processes the new user profile information, emotion data, and barometric pressure forecast data using updated algorithms to prepare for generating future notification messages. This process is repeated continuously to ensure that the advice provided to the user is always optimal.

[1450] Specific examples

[1451] server

[1452] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1453] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1454] 3. Based on the profile information and current emotional state (stressed state) of User A who lives in Tokyo, the following advice is generated: "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. Also, it seems that you are stressed, so it would be a good idea to take some time to relax."

[1455] 4. Send a notification to User A's LINE account via the LINE official account.

[1456] Terminal

[1457] 5. User A's smartphone receives the LINE message and displays a notification.

[1458] User

[1459] 6. User A follows the advice and takes time to hydrate and relax.

[1460] 7. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[1461] server

[1462] 8. The server receives the feedback and emotion data and updates the database.

[1463] 9. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for upcoming barometric pressure changes.

[1464] 10. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1465] In this way, the present invention not only prevents poor health due to changes in atmospheric pressure, but also takes into consideration the user's emotional state, thereby realizing a system that provides more personalized advice and kind words.

[1466] Example 2

[1467] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1468] For people who experience poor health due to changes in atmospheric pressure, effective and timely measures are needed. However, existing systems have difficulty providing personalized advice based on changes in atmospheric pressure. Furthermore, they are unable to generate advice that takes into account the user's emotional state, which limits the effectiveness of the measures.

[1469] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1470] In this invention, the server includes means for acquiring weather information, means for analyzing the acquired weather information and predicting changes in atmospheric pressure at a specific location, means for managing personal information of a user, means for generating countermeasure advice for a user based on the acquired changes in atmospheric pressure, means for transmitting the generated countermeasure advice to the user via communication means, means for analyzing the emotional state of the user, means for collecting feedback from the user and analyzing the data, means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, and means for transmitting the generated countermeasure advice. This makes it possible to provide effective countermeasures tailored to individual needs for dealing with poor health caused by changes in atmospheric pressure.

[1471] "Weather information" refers to data such as temperature, air pressure, humidity, wind speed, and precipitation obtained from meteorological agencies.

[1472] "Means of acquisition" refers to the programs and hardware used to periodically collect weather information through APIs of external meteorological agencies, etc.

[1473] "Analysis means" refers to algorithms and programs for processing collected weather information and predicting changes in atmospheric pressure at specific locations and times.

[1474] "Personal information" refers to a set of information about the user, such as the area where the user lives, past health conditions, and regular mood states.

[1475] "Management means" refers to the programs and hardware used to register, update, and delete users' personal information and profiles in the database.

[1476] "Measure advice generation means" refers to an algorithm and program for automatically generating appropriate action suggestions based on changes in atmospheric pressure and the user's personal information.

[1477] "Communication means" refers to an internet connection and communication application for transmitting generated countermeasure advice to a user.

[1478] "Emotional state analysis means" refers to natural language processing and machine learning algorithms used to analyze user-provided text data and identify a user's current emotional state.

[1479] "Feedback collection means" refers to a program and hardware for receiving feedback and emotional data from users and registering it in a database.

[1480] A "machine learning algorithm" refers to a program that uses statistical and mathematical methods to learn patterns from large amounts of data and make future predictions and classifications.

[1481] The present invention is a system for preventing poor health caused by changes in atmospheric pressure, and is particularly combined with an emotion engine that recognizes the user's emotions. This system is built with the cooperation of a server, terminals, and users.

[1482] Server Operation

[1483] The server collects and analyzes weather information, manages user information, generates and sends advice on countermeasures, and collects and analyzes feedback. These operations are performed using the following specific hardware and software:

[1484] 1. Obtaining weather information:

[1485] The server calls the weather agency's API every morning at 6:00 to obtain the latest barometric pressure forecast data. This communication is made via an HTTP request, and the data is returned in JSON format.

[1486] 2. Meteorological information analysis:

[1487] The server parses the received JSON data and calculates the change in atmospheric pressure for a specific location and time period, and stores the results in a database.

[1488] 3. Managing User Information:

[1489] User profile information (such as the area of ​​residence and past health history) is stored in a database on the server. LINE account information is also linked via API.

[1490] 4. Emotional state analysis:

[1491] The server analyzes the text data provided by the user using natural language processing (NLP) and machine learning algorithms (e.g., sentiment analysis models) to identify the user's emotional state, which is also stored in a database.

[1492] 5. Generate action advice:

[1493] The server uses a generative AI model to generate optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The advice is written in natural language using, for example, a Python NLP library.

[1494] 6. Sending advice:

[1495] The server uses the LINE Official Account API to send the generated advice to the user's LINE account.

[1496] 7. Feedback Collection and Analysis:

[1497] The server receives user feedback and sentiment data and stores it in a database, which is then fed into a machine learning algorithm to improve the accuracy of next time's advice.

[1498] Device behavior

[1499] The user's device (e.g., a smartphone) receives the notification sent from the server through the LINE app and displays it to the user, allowing the user to check and implement the proposed measures.

[1500] User Actions

[1501] The user receives a notification from the server through the LINE app, implements the proposed measures, and provides feedback on their effectiveness. This feedback should also include the user's emotional state.

[1502] Specific examples

[1503] 1. Barometric pressure forecast and notifications:

[1504] The server calls the weather data API every morning at 6:00 and obtains barometric pressure forecast data for the Tokyo area.

[1505] The analysis confirms that the air pressure drops sharply between 2:00 and 4:00 p.m.

[1506] Based on the profile information of user A who lives in Tokyo, it is recognized that he / she suffers from frequent headaches.

[1507] The emotion engine detects that you are feeling stressed and generates advice such as, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. It seems that you are stressed, so it would also be a good idea to take some time to relax."

[1508] The server sends a notification to User A via the LINE Official Account API.

[1509] User A's smartphone receives the LINE message and displays a notification.

[1510] 2. Feedback and Improvement:

[1511] User A follows the advice and takes time to hydrate and relax.

[1512] Feedback is sent via LINE along with the patient's emotional state, providing specific feedback such as, "The advice was effective, my headache has subsided, and I feel a little better."

[1513] The server receives the feedback and stores it in a database.

[1514] Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of next notification advice.

[1515] Prompt Sentence Examples

[1516] An example of a prompt sentence to be input to the emotion engine is, "Tell me about your emotional state recently. Are you feeling stressed or anxious?" Based on this prompt sentence, the user inputs their own emotions in text format, which is then analyzed.

[1517] In this way, the present invention provides specific and effective countermeasures to prevent health problems caused by changes in atmospheric pressure, and also realizes more personalized services by taking into account the user's emotional state.

[1518] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1519] Step 1:

[1520] The server retrieves the weather information.

[1521] Input: Weather agency API endpoint and credentials.

[1522] Specific operation: Sends an HTTP request every morning at 6:00 and receives data in JSON format.

[1523] Output: Retrieved weather data in JSON format.

[1524] Step 2:

[1525] The server analyzes the weather information it receives and predicts changes in atmospheric pressure at specific locations and times.

[1526] Input: Weather data in JSON format.

[1527] Specific operation: Parse the JSON data, split it by region and time zone, and calculate the change in atmospheric pressure.

[1528] Output: Analyzed pressure forecast data.

[1529] Step 3:

[1530] The server manages the user's personal information.

[1531] Input: Profile information registered by the user (address, past health status, etc.).

[1532] Specific operation: User information is stored and managed in a database. Also, LINE account information is linked via API.

[1533] Output: User profile information stored in a database.

[1534] Step 4:

[1535] The server analyzes the user's emotional state.

[1536] Input: User-provided text data (e.g., diary entries or messages).

[1537] What it does: Uses a generative AI model to analyze text data and identify emotional states.

[1538] Output: Parsed emotional state data.

[1539] Step 5:

[1540] The server generates countermeasure advice based on barometric pressure forecast data, the user's personal information, and their emotional state.

[1541] Input: Parsed barometric pressure forecast data, user profile information, emotional state data.

[1542] What it does: Uses generative AI models to generate personalized countermeasure advice.

[1543] Output: The generated remediation advice text.

[1544] Step 6:

[1545] The server transmits the generated countermeasure advice to the user via a communication means.

[1546] Input: Generated advice text, user's LINE account information.

[1547] Specific operation: Advice on countermeasures is sent as a LINE message via the LINE official account API.

[1548] Output: Send a message to the user's LINE account.

[1549] Step 7:

[1550] The device receives the LINE message sent from the server and displays a notification.

[1551] Input: The LINE message sent from the server.

[1552] What happens: The LINE application displays the notification as a banner, and when the user taps on the message, more details are displayed.

[1553] Output: A notification is displayed to the user.

[1554] Step 8:

[1555] The user implements the measures notified.

[1556] Input: Countermeasure advice received via the LINE app.

[1557] Specific actions: Implement suggested measures (e.g., hydration, rest).

[1558] Output: Results of the implementation of the measures.

[1559] Step 9:

[1560] Users provide feedback on the effectiveness of the measures.

[1561] Input: Results of taking the measures and emotional state.

[1562] Specific behavior: Use the LINE application to enter and send feedback in text format.

[1563] Output: The feedback data sent.

[1564] Step 10:

[1565] The server receives feedback and emotion data from users and stores it in a database.

[1566] Input: Feedback data and emotional state data.

[1567] Specific operation: Save the received data in the database.

[1568] Output: Feedback data stored in a database.

[1569] Step 11:

[1570] The server inputs the collected feedback data and sentiment data into a machine learning algorithm to improve the accuracy of the next countermeasure advice.

[1571] Input: Feedback data and emotional state data.

[1572] Specific operation: A machine learning algorithm is used to analyze the feedback data and update the generative model for countermeasure advice.

[1573] Output: An improved model for generating countermeasure advice.

[1574] Through the above processing steps, a system is realized that prevents users from becoming ill due to changes in atmospheric pressure and provides individual and effective countermeasures for the user.

[1575] (Application example 2)

[1576] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1577] In modern society, changes in atmospheric pressure are a major cause of poor health for many people. In addition, people are exposed to various stressors every day, increasing the risk of poor health. However, current systems are unable to provide prompt and appropriate countermeasures against these changes in atmospheric pressure and stress, resulting in increased health risks. Furthermore, there is a lack of a mechanism for effectively utilizing feedback on health advice to improve the accuracy of countermeasures. Another issue is the lack of a means to smoothly purchase the necessary products as countermeasures.

[1578] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for analyzing the user's emotional state using an emotion analysis engine, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure and the user's emotional state, and means for providing electronic payment functionality and enabling the user to purchase health-related products. This makes it possible to provide personalized advice that takes into account changes in atmospheric pressure and the user's emotional state, and to smoothly purchase necessary countermeasure products. In addition, the server can collect user feedback and use machine learning algorithms to improve the accuracy of the advice.

[1579] "Weather data" is information that indicates atmospheric conditions and changes, and includes elements such as temperature, humidity, air pressure, wind speed, and wind direction.

[1580] An "emotion analysis engine" is software that analyzes a user's emotional state based on their text data and biometric information, and distinguishes between emotions such as anger, sadness, joy, and stress.

[1581] "Electronic payment function" is a system that allows users to pay for goods and services via the Internet, and includes payment methods such as credit cards, debit cards, and electronic money.

[1582] "Feedback" is information provided by the user, and is data that collects reactions such as the effectiveness of advice, changes in physical condition, and usage impressions.

[1583] "Machine learning algorithms" are a field of artificial intelligence that uses patterns learned from past data to make predictions and judgments about new data.

[1584] "Advice on countermeasures" is information that indicates specific actions and precautions that the user should take based on changes in atmospheric pressure and the user's emotional state, and includes suggestions for health management and mood improvement.

[1585] System Overview

[1586] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and is configured with an emotion analysis engine that analyzes the user's emotional state. The system includes a function for collecting and analyzing weather data and predicting changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the acquired data, individual user profile information, and emotion analysis results, and provides a mechanism for enabling users to purchase necessary health products using an electronic payment function.

[1587] System Details

[1588] Meteorological data collection and analysis tools

[1589] The server periodically obtains the latest barometric pressure forecast data via the weather agency's API. For example, it collects data every morning at 6:00 and predicts the daily barometric pressure change in a specific area. If a sudden change in barometric pressure is expected, it analyzes the time and amount of change and stores it in the database as an important event.

[1590] Emotion analysis means

[1591] The server uses an emotion analysis engine to analyze the user's emotional state in real time, which involves analyzing the user's text data and biometric information (e.g., heart rate and skin conductance) to determine emotional states such as stress, anger, and joy.

[1592] How to generate and send advice

[1593] The server generates optimal advice based on the collected barometric pressure forecast data, the user's profile information, and their emotional state. This advice is sent to the user's device via a communication method such as LINE. For example, specific advice such as "This afternoon, the barometric pressure will drop suddenly. You are likely to experience a headache, so it would be a good idea to stay hydrated and rest early. We also recommend that you take some time to relax."

[1594] Electronic payment facility

[1595] The app also provides an electronic payment function for users to purchase necessary health products. For example, health drinks and relaxation items recommended based on advice can be purchased directly within the app. This function allows users to receive advice and purchase products in a single flow.

[1596] Feedback collection and analysis methods

[1597] After implementing the advice, the user provides feedback on its effectiveness. This feedback data is collected along with the user's emotional state. The server analyzes this data and uses machine learning algorithms to improve the accuracy of future advice. For example, the server can improve the quality of the generated advice based on feedback such as, "The advice was effective, my headache was relieved, and I feel a little better."

[1598] Hardware and software used

[1599] Hardware: Smartphone

[1600] Software: LINE Bot API, weather data API, sentiment analysis engine, Python platform

[1601] Example prompt: "User A is experiencing a drop in barometric pressure at 2 PM. He is likely to have a headache, so he should stay hydrated and get some rest. He also seems to be feeling stressed, so it's a good idea to take some time to relax."

[1602] Specific examples

[1603] Example 1: User A, who lives in Tokyo, receives barometric pressure forecast information every morning via an app. This morning, the app predicted that barometric pressure would drop sharply between 2:00 PM and 4:00 PM, and sent User A advice on what to do via LINE. The advice sent reads, "Barometric pressure will drop sharply this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. You are also feeling stressed, so we recommend that you take some time to relax."

[1604] Example 2: User A purchased the recommended health drink within the app using the electronic payment function and hydrated. When the atmospheric pressure dropped in the afternoon, the advance precautions prevented a headache. That evening, User A sent feedback via LINE that the advice was effective. The server applies a machine learning algorithm based on this feedback data to improve the accuracy of the advice.

[1605] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1606] Step 1:

[1607] The server obtains the latest barometric pressure forecast data from the meteorological agency's API. It calls the API every morning at 6:00 to collect barometric pressure data for each region. The input is the weather API endpoint, and the output is the barometric pressure forecast data for each region. It analyzes the data, identifies times of day when barometric pressure changes most suddenly, and stores the data in a database.

[1608] Step 2:

[1609] The server manages user profile information. It stores and updates the database with information collected at the time of initial registration, such as the area of ​​residence, past health conditions, and addictive measures. The input is the user's registration information, and the output is each user's profile data.

[1610] Step 3:

[1611] The server uses an emotion analysis engine to analyze the user's emotional state in real time. It determines emotions based on LINE messages and biometric data and records them in a database. The input is text data and biometric information, and the output is the analyzed emotional state.

[1612] Step 4:

[1613] The server generates optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The generated advice is sent to the user's device as a LINE message. The input is barometric pressure data, profile information, and their emotional state, and the output is an advice message. A generative AI model is used to create the advice.

[1614] Step 5:

[1615] The user receives the LINE message and takes the necessary measures according to the advice, such as staying hydrated or taking time to relax. The input is the received advice message, and the output is the implemented measures.

[1616] Step 6:

[1617] Users provide feedback on the effectiveness of the measures. They report the effectiveness along with their emotional state via LINE. The input is the emotional state after the measures are implemented and the feedback content, and the output is the sent feedback data.

[1618] Step 7:

[1619] The server collects user feedback and stores it in a database. It analyzes the feedback data and uses a machine learning algorithm to improve the countermeasure advice generation algorithm. The input is the feedback data, and the output is the improved advice generation algorithm.

[1620] Step 8:

[1621] The server improves the accuracy of the notification advice for the next barometric pressure change based on the improved advice generation algorithm. The newly generated advice message provides more accurate measures for the next barometric pressure change. The input is the improved algorithm, and the output is a more accurate advice message.

[1622] 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.

[1623] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1624] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1625] [Fourth embodiment]

[1626] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1627] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1628] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1629] 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.

[1630] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1631] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1632] 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.

[1633] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1634] 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.

[1635] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1636] 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.

[1637] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1638] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1639] System Overview

[1640] This invention is a system for preventing poor health caused by changes in atmospheric pressure. This system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on profile information managed for each user and notifies the user's device via a communication method such as LINE. It also collects and analyzes feedback from users and uses that data to improve the accuracy of the advice.

[1641] Program processing flow

[1642] Collection and analysis of atmospheric pressure forecast data

[1643] server

[1644] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API.

[1645] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes suddenly and the amount of change are calculated.

[1646] User information management

[1647] server

[1648] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[1649] 4. Link with LINE accounts and organize information for each user.

[1650] Generate and send notification content

[1651] server

[1652] 5. The server generates optimal countermeasure advice for each individual user based on the barometric pressure forecast data and the user's profile information.

[1653] For example, create specific advice such as, "The atmospheric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[1654] 6. The created advice is sent to the user's LINE account.

[1655] Terminal

[1656] 7. The user's device receives the LINE message and displays a notification.

[1657] Gathering feedback and improving advice

[1658] User

[1659] 8. The user tries out the proposed measures and sends feedback on their effectiveness via LINE.

[1660] For example, provide specific feedback such as, "The advice was effective and relieved my headache. I would like to try this method again next time."

[1661] server

[1662] 9. The server receives the feedback from the user and stores it in a database.

[1663] 10. Analyze the collected feedback data and evaluate whether specific measures were effective.

[1664] 11. Using machine learning algorithms, we will update the system to improve the accuracy of the next notification advice.

[1665] Specific examples

[1666] Barometric pressure forecast and notifications

[1667] server

[1668] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1669] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1670] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[1671] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[1672] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[1673] Terminal

[1674] 6. User A's smartphone receives the LINE message and displays a notification.

[1675] Feedback and Improvements

[1676] User

[1677] 1. User A follows the advice and practices hydration and early rest.

[1678] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[1679] server

[1680] 3. The server receives the feedback and updates the database.

[1681] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[1682] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1683] In this way, the present invention provides a series of processes to prevent poor health caused by changes in atmospheric pressure and to provide individualized, specific, and effective countermeasures.

[1684] The processing flow will be explained below.

[1685] Step 1:

[1686] server

[1687] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[1688] Step 2:

[1689] server

[1690] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[1691] Step 3:

[1692] server

[1693] The server maintains a database of user profile information, including the area of ​​residence, past health history, and the effectiveness of each measure.

[1694] Step 4:

[1695] server

[1696] The server combines the user profile information in the database with the barometric pressure forecast data to generate personalized advice for each user, such as "The barometric pressure will drop suddenly this afternoon. You may experience a headache, so make sure to stay hydrated and get some rest early."

[1697] Step 5:

[1698] server

[1699] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[1700] Step 6:

[1701] Terminal

[1702] The user's smartphone receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[1703] Step 7:

[1704] User

[1705] Based on the notification, the user can try the suggested measures, such as drinking more water and taking timely rest.

[1706] Step 8:

[1707] User

[1708] Users can send feedback to the official LINE account about the effectiveness of the measures, providing specific feedback such as, "The advice was effective and my headache has been reduced."

[1709] Step 9:

[1710] server

[1711] The server receives feedback from users and stores it in a database, including details about the effectiveness of the measures.

[1712] Step 10:

[1713] server

[1714] The server analyzes the collected feedback data and evaluates whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[1715] Step 11:

[1716] server

[1717] The server updates the algorithm for generating countermeasure advice based on the analysis results, thereby improving the accuracy of the next notification advice.

[1718] Step 12:

[1719] server

[1720] The server processes the new user profile information and barometric pressure forecast data using updated algorithms, preparing to generate future notification messages. This process is repeated continuously to ensure the advice provided to users is always optimal.

[1721] Example 1

[1722] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1723] It is widely known that changes in atmospheric pressure have an impact on people's health, with many suffering from symptoms such as headaches and weather-related illnesses. However, current technology makes it difficult to take effective measures in response to changes in atmospheric pressure, and no system exists that provides specific advice based on individual profiling. Therefore, there is a need for a system that can predict changes in atmospheric pressure and provide individually optimized advice based on that information.

[1724] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1725] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, thereby making it possible to provide individualized, specific, and effective countermeasures in response to changes in atmospheric pressure.

[1726] "Weather data" refers to weather-related information such as weather forecasts, temperature, air pressure, and precipitation provided by meteorological agencies.

[1727] "Analysis" is the act of processing acquired data and extracting specific insights and figures.

[1728] "Changes in atmospheric pressure" refers to an increase or decrease in atmospheric pressure over a specific period of time.

[1729] "Prediction" is the estimation of future states or events based on acquired data.

[1730] "User profile information" is individually managed information such as the user's area of ​​residence, past health data, and the effectiveness of countermeasures.

[1731] "Management" refers to a series of processes including collecting, storing, updating, and deleting data.

[1732] "Countermeasure advice" is specific guidelines and advice provided to the user to deal with changes in atmospheric pressure.

[1733] "Sending" is the act of delivering the generated information to a recipient through a specific communication means.

[1734] "Feedback" refers to information such as execution results, opinions, and effects provided by users.

[1735] A "machine learning algorithm" is a computational method for learning patterns from collected data and making predictions and classifications.

[1736] System Overview

[1737] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The server acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. Optimal countermeasure advice is generated based on profile information managed for each user, and notified to the user's terminal via communication means. Furthermore, feedback from users is collected and analyzed to improve the accuracy of the next advice.

[1738] Specific processing flow

[1739] 1. Collecting atmospheric pressure forecast data

[1740] The server periodically calls the API of the meteorological agency (e.g., OpenWeatherMap or the API of the Japan Meteorological Agency) to obtain the latest barometric pressure forecast data. The obtained data is stored on the server in JSON format.

[1741] 2. Analysis of atmospheric pressure data

[1742] The server analyzes the acquired atmospheric pressure data using data analysis tools such as Pandas and organizes it by region and time period, allowing the time periods when atmospheric pressure suddenly changes and the magnitude of those changes to be calculated.

[1743] 3. Management of User Information

[1744] The server stores and manages each user's profile information (such as the area they live in, past health data, and the effectiveness of measures they have taken) in a database (e.g., PostgreSQL). It also links the information to their LINE accounts, and organizes the information for each user.

[1745] 4. Generating and sending notification content

[1746] The server uses a generative AI model (e.g., GPT-3) based on the barometric pressure forecast data and the user's profile information to generate optimal advice. A specific example of advice might be, "The barometric pressure will drop suddenly this afternoon. You are likely to experience a headache, so it's a good idea to stay hydrated and get some rest early." The generated advice is sent to the user's LINE account via the LINE Official Account API.

[1747] 5. Gathering Feedback

[1748] The user implements the proposed measures and sends feedback on their effectiveness via LINE, such as "The advice was effective and my headache has subsided."

[1749] 6. Improved advice accuracy

[1750] The server stores the collected feedback in a database and analyzes it using machine learning algorithms (e.g., Scikit-learn or TensorFlow), which then updates the system to improve the accuracy of the next notification advice.

[1751] Specific examples

[1752] Barometric pressure forecast and notifications

[1753] server

[1754] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1755] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1756] 3. Refer to the profile information of User A who lives in Tokyo and recognize that he tends to suffer from frequent headaches during periods of low pressure.

[1757] 4. Generate the advice, "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early."

[1758] 5. A notification is sent to User A's LINE account via the LINE Official Account API.

[1759] Terminal

[1760] 6. User A's smartphone receives the LINE message and displays a notification.

[1761] Feedback and Improvements

[1762] User

[1763] 1. User A follows the advice and hydrates and takes an early rest.

[1764] 2. That night, User A sends feedback via LINE saying, "The advice was effective and my headache has been relieved."

[1765] server

[1766] 3. The server receives the feedback and updates the database.

[1767] 4. Machine learning algorithms analyze the feedback and improve the accuracy of notification advice for the next barometric pressure change.

[1768] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1769] Through the above process, the present invention prevents poor health caused by changes in atmospheric pressure and provides users with individualized and effective countermeasures.

[1770] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1771] Program processing flow

[1772] Step 1: Collect pressure forecast data

[1773] server

[1774] 1. The server calls the weather agency's API (e.g., OpenWeatherMap, Japan Meteorological Agency's API) every morning at 6:00.

[1775] Input: API key, region-specific request parameters

[1776] Specific operation: Uses Python's requests module to send an HTTP GET request to obtain barometric pressure forecast data.

[1777] Output: Receives barometric pressure forecast data in JSON format.

[1778] Step 2: Analyze the pressure data

[1779] server

[1780] 1. The server analyzes the acquired atmospheric pressure data.

[1781] Input: Pressure forecast data in JSON format

[1782] Specific operation: Use the Pandas library to convert the data into a data frame and perform analysis.

[1783] Output: Pressure change points and their magnitudes organized by region and time period

[1784] Step 3: Managing User Information

[1785] server

[1786] 1. The server retrieves each user's profile information from the database.

[1787] Input: User ID or LINE account ID

[1788] Specific operation: Retrieve user profile information from the PostgreSQL database using a SELECT query.

[1789] Output: Profile information (area of ​​residence, past health status, etc.)

[1790] Step 4: Generate notification content

[1791] server

[1792] 1. The server generates countermeasure advice based on barometric pressure forecast data and the user's profile information.

[1793] Input: Analyzed barometric pressure data, user profile information

[1794] Specific operation: A prompt sentence is input into a generative AI model (e.g., GPT-3) to generate specific countermeasure advice.

[1795] Example prompt: "Generate specific advice for a specific user to prevent headaches when the atmospheric pressure drops in the Tokyo area."

[1796] Output: Advice: "This afternoon, the atmospheric pressure will drop suddenly. You may experience a headache, so make sure to stay hydrated and get some rest."

[1797] Step 5: Submitting Advice

[1798] server

[1799] 1. The server sends the generated advice to the user via the LINE Official Account API.

[1800] Input: Generated countermeasure advice, user's LINE account ID

[1801] Specific operation: Uses the LINE Messaging API to send a POST request and deliver the advice.

[1802] Output: A notification message will be sent to the user's LINE account.

[1803] Terminal

[1804] 2. The user's device receives the LINE message and displays a notification.

[1805] Input: Message received from the LINE server

[1806] Specific behavior: The LINE app displays a notification.

[1807] Output: A notification is displayed on the user's smartphone.

[1808] Step 6: Gather feedback

[1809] User

[1810] 1. The user implements the proposed measures and provides feedback on their effectiveness.

[1811] Input: Impressions after implementing measures and evaluation of their effectiveness

[1812] Specific operation: Send a feedback message through the LINE app.

[1813] Output: Feedback data is sent to the server.

[1814] server

[1815] 2. The server receives the feedback from the user and stores it in a database.

[1816] Input: User feedback message

[1817] Specific operation: Data received via the LINE Messaging API is saved in a PostgreSQL database.

[1818] Output: The feedback data is stored in a database.

[1819] Step 7: Improve the accuracy of your advice

[1820] server

[1821] 1. The server analyzes the collected feedback data.

[1822] Input: Saved feedback data

[1823] Specific operations: Analyze data using machine learning algorithms (e.g., Scikit-learn, TensorFlow).

[1824] Output: A new learning model for improving the accuracy of countermeasure advice

[1825] 2. The server uses machine learning models to improve the accuracy of countermeasure advice.

[1826] Input: A new learning model

[1827] Specific operation: The generative AI model is updated and reflected in the next notification.

[1828] Output: Improved countermeasure advice is generated.

[1829] (Application example 1)

[1830] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1831] Changes in atmospheric pressure can have a significant impact on a user's physical condition. Specifically, in moving objects (such as self-driving vehicles), it is difficult to manage the user's physical condition while in the vehicle, so there is concern that sudden changes in atmospheric pressure may cause poor health. Therefore, there is a need for prevention and countermeasures against poor health caused by changes in atmospheric pressure in moving objects. In addition, there is a need for a system that can provide appropriate advice to individual users and collect feedback to improve the accuracy of the advice.

[1832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1833] In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for managing user profile information, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure, means for notifying the user of the countermeasure advice within the mobile vehicle, means for collecting feedback from the user and analyzing the data, and means for improving the accuracy of the advice based on the collected feedback. This makes it possible to individually provide prevention and countermeasures for poor health caused by changes in atmospheric pressure, and effectively manage the user's physical condition even within the mobile vehicle.

[1834] "Weather data" refers to information about atmospheric conditions such as temperature, humidity, air pressure, and wind speed.

[1835] "Analysis" refers to the process of processing acquired data and information to derive meaning and trends.

[1836] "Specific region" refers to a limited geographical area, such as a specific city or area.

[1837] "Changes in atmospheric pressure" refers to increases or decreases in atmospheric pressure over a certain period of time.

[1838] "Prediction" refers to estimating future events or conditions with a certain degree of accuracy.

[1839] "User profile information" refers to data about an individual user, including name, address, health status, and past health history.

[1840] "Advice" refers to specific suggestions or instructions for dealing with a particular situation or problem.

[1841] "Sending" refers to the act of delivering information to a specific recipient.

[1842] "Mobile object" refers to a machine or device that can change position, such as an autonomous vehicle.

[1843] "Feedback" refers to reactions and opinions from users in response to information or advice received.

[1844] "Accuracy" refers to the degree to which the results of predictions, analyses, etc. are accurate.

[1845] "Means" refers to a method or device for achieving a particular purpose or result.

[1846] System Overview

[1847] This invention is a system for preventing poor health caused by changes in atmospheric pressure. The system aims to manage the user's health while in a mobile vehicle by acquiring and analyzing weather data and predicting changes in atmospheric pressure. It also manages profile information for each user and generates and notifies optimal countermeasure advice. It also collects and analyzes feedback from users and uses that data to improve the accuracy of advice.

[1848] Hardware and software used

[1849] server

[1850] The server retrieves the latest barometric pressure forecast data using a weather data API, for example, the API at weatherapi.com.

[1851] The acquired data is analyzed and organized by region and time period.

[1852] Each user's profile information is managed in a database and notifications are sent via the LINE Official Account API.

[1853] Terminal

[1854] The user's smartphone or in-vehicle display receives the LINE message and displays a notification.

[1855] The notification will include detailed advice on how to prevent and deal with illness caused by changes in atmospheric pressure.

[1856] User

[1857] Users implement the recommended measures and provide feedback on their effectiveness.

[1858] Feedback is sent to the server via LINE and will be used to improve the accuracy of the next notification advice.

[1859] Overview of program processing flow

[1860] Meteorological data acquisition and analysis

[1861] The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API, analyzes the data, predicts sudden changes in barometric pressure in a specific area, and stores the results in a database.

[1862] User information management

[1863] The server stores and manages each user's profile information in a database, including the area where they live, their past health history, and the effectiveness of specific measures.

[1864] Generate and send notification content

[1865] The server generates optimal advice for each individual user based on the barometric pressure forecast data and the user's profile information. The advice is then sent to the user's smartphone or in-vehicle display via the LINE official account API.

[1866] Gathering feedback and improving advice

[1867] Users provide feedback on the effectiveness of the advice they receive through LINE. The server receives and analyzes the feedback, updates the database, and uses machine learning algorithms to improve the accuracy of the next notification advice.

[1868] Specific examples and prompt examples for generative AI models

[1869] As a concrete example, if User A is riding in a vehicle in the Tokyo area and receives a forecast from the weather data API that the atmospheric pressure will drop sharply between 2:00 PM and 4:00 PM, the server will generate advice such as "The atmospheric pressure will drop sharply this afternoon. You are likely to experience a headache, so please make sure to stay hydrated in your vehicle and relax and rest," and notify the advice via LINE. User A can follow the advice and provide feedback, thereby contributing to improving the accuracy of the next advice.

[1870] Example prompts for generative AI models:

[1871] Please devise a system that provides advice and notifications to prevent users from becoming ill due to large changes in air pressure while riding in a vehicle. Specifically, please explain the following points:

[1872] 1. Meteorological data collection and analysis

[1873] 2. User profile information management

[1874] 3. Notification delivery via dedicated applications

[1875] 4. Collecting and Using User Feedback

[1876] Please indicate the hardware, software, and specific process steps required for each step.

[1877] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1878] Step 1:

[1879] The server periodically calls the weather agency's API to obtain weather data (temperature, pressure, wind speed, etc.). It uses the API key and region code as input, and obtains weather data in JSON format as output. This data is then analyzed to extract changes in pressure in specific regions and time periods. Data processing involves performing time series analysis of the pressure data to identify times when sudden changes in pressure are predicted.

[1880] Step 2:

[1881] The server reads each user's profile information from the database. Using the user ID as input, the server obtains profile information such as the area of ​​residence, health history, and effective countermeasures as output. Based on this information, users who are susceptible to changes in atmospheric pressure are identified. The data is processed by comparing the area information with health history data and analyzing the correlation.

[1882] Step 3:

[1883] The server generates individualized advice based on the barometric pressure forecast data and the user's profile information. The barometric pressure forecast data and profile information are used as input, and textual advice is generated as output. Data calculations involve matching the user's past health history with barometric pressure change patterns, and specific advice (e.g., hydration, rest, etc.) is included in the advice.

[1884] Step 4:

[1885] The server sends the generated advice text to the user's smartphone or in-vehicle display via the LINE Official Account API. LINE account information and advice text are used as input, and a LINE message is sent as output. Specifically, the server sends an API request to the LINE server and delivers the notification message.

[1886] Step 5:

[1887] The user puts into practice the advice received via LINE. The input is a LINE message, the output is putting the advice into practice, and observing the resulting changes in physical condition. Specific actions include following the advice and taking measures such as drinking water and resting.

[1888] Step 6:

[1889] The user provides feedback via LINE about the effectiveness of the measures they have implemented. As input, they record changes in their physical condition and send feedback via LINE messages. As output, the feedback information is sent to the server. Specific actions include inputting specific feedback such as "The advice was effective" or "My headache has subsided."

[1890] Step 7:

[1891] The server stores the received feedback in a database and analyzes it. The feedback data is used as input, and the feedback analysis results are obtained as output. For data calculations, the feedback data is input into a machine learning algorithm, and the advice generation model is retrained. This improves the accuracy of notification advice for the next barometric pressure change.

[1892] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1893] System Overview

[1894] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and in particular, combines an emotion engine that recognizes the user's emotions. The system acquires and analyzes weather data to predict changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the profile information and emotional state managed for each user, and notifies the user's device via communication means such as LINE. Furthermore, it collects and analyzes feedback and emotional data from users, and uses this data to improve the accuracy of the advice.

[1895] Program processing flow

[1896] Collection and analysis of atmospheric pressure forecast data

[1897] server

[1898] 1. The server periodically retrieves the latest barometric pressure forecast data from the weather agency's API. For example, it is set to retrieve the latest data every morning at 6:00.

[1899] 2. The acquired data is organized by region and time period, and the times of day when atmospheric pressure changes most suddenly and the amount of change are calculated, and these events are saved in a database as important events.

[1900] User information management

[1901] server

[1902] 3. The server stores and manages each user's profile information (such as the area of ​​residence, past health conditions, and the effectiveness of specific measures) in a database.

[1903] 4. Link with LINE accounts and organize information for each user.

[1904] 5. The server uses an emotion engine to analyze the user's emotional state in real time and record it in a database.

[1905] Generate and send notification content

[1906] server

[1907] 6. The server generates optimal countermeasure advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only countermeasures against barometric pressure changes but also the user's current emotional state.

[1908] For example, you might create advice like, "The atmospheric pressure will drop suddenly this afternoon. You can expect a headache, so make sure to stay hydrated and get some rest early. Taking some time to relax will also help ease your mood."

[1909] 7. The created advice is sent to the user's LINE account.

[1910] Terminal

[1911] 8. The user's device receives the LINE message and displays a notification within the app. The user confirms the notification.

[1912] Gathering feedback and improving advice

[1913] User

[1914] 9. The user takes action based on the notification, for example, drinking more water and taking timely rest.

[1915] 10. Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[1916] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[1917] server

[1918] 11. The server receives feedback and emotion data from users and stores it in a database.

[1919] 12. Analyze the collected feedback and sentiment data to assess whether a particular strategy was effective under specific conditions. This analysis is done using machine learning algorithms.

[1920] 13. The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[1921] Specific examples

[1922] Barometric pressure forecast and notifications

[1923] server

[1924] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1925] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1926] 3. By looking at the profile information of user A who lives in Tokyo, you realize that headaches tend to occur more frequently during periods of low pressure.

[1927] 4. The emotion engine detects that User A's current emotional state is stress.

[1928] 5. Generate the following advice: "The atmospheric pressure will drop suddenly this afternoon. You may have a headache, so make sure to stay hydrated and get some rest early. You also seem to be stressed, so it's a good idea to take some time to relax."

[1929] 6. Send a notification to User A's LINE account via the LINE Official Account API.

[1930] Terminal

[1931] 7. User A's smartphone receives the LINE message and displays a notification.

[1932] Feedback and Improvements

[1933] User

[1934] 1. User A follows the advice and takes time to hydrate and relax.

[1935] 2. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[1936] server

[1937] 3. The server receives the feedback and emotion data and updates the database.

[1938] 4. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for the next barometric pressure change.

[1939] 5. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1940] In this way, through a series of processes, the present invention not only prevents poor health caused by changes in atmospheric pressure and provides individualized, specific, and effective countermeasures, but also realizes a service that takes into consideration the user's emotional state.

[1941] The processing flow will be explained below.

[1942] Step 1:

[1943] server

[1944] The server calls the weather agency's API to periodically obtain the latest barometric pressure forecast data. For example, it is set to obtain the latest data every morning at 6:00.

[1945] Step 2:

[1946] server

[1947] The server organizes the acquired barometric pressure forecast data by region and time period, calculates the times of day when barometric pressure changes most rapidly, and the magnitude of those changes, and stores these as important events in a database.

[1948] Step 3:

[1949] server

[1950] The server stores and manages each user's profile information (such as the area of ​​residence, past health data, and the effectiveness of specific measures) in a database. It also links the information to LINE accounts and organizes the information for each user.

[1951] Step 4:

[1952] server

[1953] The server uses an emotion engine to recognize the user's emotional state. This emotion data is obtained in real time from the user's LINE messages and other interactions and stored in a database.

[1954] Step 5:

[1955] server

[1956] The server generates optimal advice based on the barometric pressure forecast data, the user's profile information, and their emotional state. This advice takes into account not only measures to deal with barometric pressure changes but also the user's current emotional state.

[1957] For example, you could create advice like, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so make sure to stay hydrated and get some rest early. Also, you seem to be feeling stressed, so it's a good idea to take some time to relax."

[1958] Step 6:

[1959] server

[1960] The server sends the generated message to the user's LINE account via the LINE Official Account API.

[1961] Step 7:

[1962] Terminal

[1963] The user's device receives the LINE message and displays a notification within the app. The user can then confirm the notification.

[1964] Step 8:

[1965] User

[1966] Based on the notification, the user can try the suggested measures, such as staying hydrated and taking time to rest, or taking time to relax based on the advice of the emotion engine.

[1967] Step 9:

[1968] User

[1969] Users can send feedback to the official LINE account about the effectiveness of the measures, including their emotional state at the time, using the emotion engine.

[1970] For example, provide specific feedback and emotional data such as, "The advice was effective, my headache was reduced, and I feel a little better."

[1971] Step 10:

[1972] server

[1973] The server receives user feedback and sentiment data and stores it in a database. The feedback includes details about the effectiveness of countermeasures.

[1974] Step 11:

[1975] server

[1976] The server analyzes the collected feedback and sentiment data to assess whether a particular measure was effective under certain conditions. This analysis is done using machine learning algorithms.

[1977] Step 12:

[1978] server

[1979] The server updates the algorithm for generating countermeasure advice and the emotion engine based on the analysis results, thereby improving the accuracy of the next notification advice.

[1980] Step 13:

[1981] server

[1982] The server processes the new user profile information, emotion data, and barometric pressure forecast data using updated algorithms to prepare for generating future notification messages. This process is repeated continuously to ensure that the advice provided to the user is always optimal.

[1983] Specific examples

[1984] server

[1985] 1. Call the weather data API every morning at 6:00 to obtain barometric pressure forecast data for the Tokyo area.

[1986] 2. Analyze the data and determine that there is a sudden drop in air pressure between 2:00 and 4:00 PM.

[1987] 3. Based on the profile information and current emotional state (stressed state) of User A who lives in Tokyo, the following advice is generated: "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. Also, it seems that you are stressed, so it would be a good idea to take some time to relax."

[1988] 4. Send a notification to User A's LINE account via the LINE official account.

[1989] Terminal

[1990] 5. User A's smartphone receives the LINE message and displays a notification.

[1991] User

[1992] 6. User A follows the advice and takes time to hydrate and relax.

[1993] 7. That night, User A sends feedback via LINE along with his / her emotional state, saying, "The advice was effective, my headache has subsided, and I feel a little better."

[1994] server

[1995] 8. The server receives the feedback and emotion data and updates the database.

[1996] 9. Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of notification advice for upcoming barometric pressure changes.

[1997] 10. Increase the probability of providing similar advice to other users based on the collective knowledge accumulated in the database.

[1998] In this way, the present invention not only prevents poor health due to changes in atmospheric pressure, but also takes into consideration the user's emotional state, thereby realizing a system that provides more personalized advice and kind words.

[1999] Example 2

[2000] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2001] For people who experience poor health due to changes in atmospheric pressure, effective and timely measures are needed. However, existing systems have difficulty providing personalized advice based on changes in atmospheric pressure. Furthermore, they are unable to generate advice that takes into account the user's emotional state, which limits the effectiveness of the measures.

[2002] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2003] In this invention, the server includes means for acquiring weather information, means for analyzing the acquired weather information and predicting changes in atmospheric pressure at a specific location, means for managing personal information of a user, means for generating countermeasure advice for a user based on the acquired changes in atmospheric pressure, means for transmitting the generated countermeasure advice to the user via communication means, means for analyzing the emotional state of the user, means for collecting feedback from the user and analyzing the data, means for improving the accuracy of the advice using a machine learning algorithm based on the collected feedback, and means for transmitting the generated countermeasure advice. This makes it possible to provide effective countermeasures tailored to individual needs for dealing with poor health caused by changes in atmospheric pressure.

[2004] "Weather information" refers to data such as temperature, air pressure, humidity, wind speed, and precipitation obtained from meteorological agencies.

[2005] "Means of acquisition" refers to the programs and hardware used to periodically collect weather information through APIs of external meteorological agencies, etc.

[2006] "Analysis means" refers to algorithms and programs for processing collected weather information and predicting changes in atmospheric pressure at specific locations and times.

[2007] "Personal information" refers to a set of information about the user, such as the area where the user lives, past health conditions, and regular mood states.

[2008] "Management means" refers to the programs and hardware used to register, update, and delete users' personal information and profiles in the database.

[2009] "Measure advice generation means" refers to an algorithm and program for automatically generating appropriate action suggestions based on changes in atmospheric pressure and the user's personal information.

[2010] "Communication means" refers to an internet connection and communication application for transmitting generated countermeasure advice to a user.

[2011] "Emotional state analysis means" refers to natural language processing and machine learning algorithms used to analyze user-provided text data and identify a user's current emotional state.

[2012] "Feedback collection means" refers to a program and hardware for receiving feedback and emotional data from users and registering it in a database.

[2013] A "machine learning algorithm" refers to a program that uses statistical and mathematical methods to learn patterns from large amounts of data and make future predictions and classifications.

[2014] The present invention is a system for preventing poor health caused by changes in atmospheric pressure, and is particularly combined with an emotion engine that recognizes the user's emotions. This system is built with the cooperation of a server, terminals, and users.

[2015] Server Operation

[2016] The server collects and analyzes weather information, manages user information, generates and sends advice on countermeasures, and collects and analyzes feedback. These operations are performed using the following specific hardware and software:

[2017] 1. Obtaining weather information:

[2018] The server calls the weather agency's API every morning at 6:00 to obtain the latest barometric pressure forecast data. This communication is made via an HTTP request, and the data is returned in JSON format.

[2019] 2. Meteorological information analysis:

[2020] The server parses the received JSON data and calculates the change in atmospheric pressure for a specific location and time period, and stores the results in a database.

[2021] 3. Managing User Information:

[2022] User profile information (such as the area of ​​residence and past health history) is stored in a database on the server. LINE account information is also linked via API.

[2023] 4. Emotional state analysis:

[2024] The server analyzes the text data provided by the user using natural language processing (NLP) and machine learning algorithms (e.g., sentiment analysis models) to identify the user's emotional state, which is also stored in a database.

[2025] 5. Generate action advice:

[2026] The server uses a generative AI model to generate optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The advice is written in natural language using, for example, a Python NLP library.

[2027] 6. Sending advice:

[2028] The server uses the LINE Official Account API to send the generated advice to the user's LINE account.

[2029] 7. Feedback Collection and Analysis:

[2030] The server receives user feedback and sentiment data and stores it in a database, which is then fed into a machine learning algorithm to improve the accuracy of next time's advice.

[2031] Device behavior

[2032] The user's device (e.g., a smartphone) receives the notification sent from the server through the LINE app and displays it to the user, allowing the user to check and implement the proposed measures.

[2033] User Actions

[2034] The user receives a notification from the server through the LINE app, implements the proposed measures, and provides feedback on their effectiveness. This feedback should also include the user's emotional state.

[2035] Specific examples

[2036] 1. Barometric pressure forecast and notifications:

[2037] The server calls the weather data API every morning at 6:00 and obtains barometric pressure forecast data for the Tokyo area.

[2038] The analysis confirms that the air pressure drops sharply between 2:00 and 4:00 p.m.

[2039] Based on the profile information of user A who lives in Tokyo, it is recognized that he / she suffers from frequent headaches.

[2040] The emotion engine detects that you are feeling stressed and generates advice such as, "The atmospheric pressure will drop suddenly this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. It seems that you are stressed, so it would also be a good idea to take some time to relax."

[2041] The server sends a notification to User A via the LINE Official Account API.

[2042] User A's smartphone receives the LINE message and displays a notification.

[2043] 2. Feedback and Improvement:

[2044] User A follows the advice and takes time to hydrate and relax.

[2045] Feedback is sent via LINE along with the patient's emotional state, providing specific feedback such as, "The advice was effective, my headache has subsided, and I feel a little better."

[2046] The server receives the feedback and stores it in a database.

[2047] Machine learning algorithms analyze feedback and sentiment data to improve the accuracy of next notification advice.

[2048] Prompt Sentence Examples

[2049] An example of a prompt sentence to be input to the emotion engine is, "Tell me about your emotional state recently. Are you feeling stressed or anxious?" Based on this prompt sentence, the user inputs their own emotions in text format, which is then analyzed.

[2050] In this way, the present invention provides specific and effective countermeasures to prevent health problems caused by changes in atmospheric pressure, and also realizes more personalized services by taking into account the user's emotional state.

[2051] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2052] Step 1:

[2053] The server retrieves the weather information.

[2054] Input: Weather agency API endpoint and credentials.

[2055] Specific operation: Sends an HTTP request every morning at 6:00 and receives data in JSON format.

[2056] Output: Retrieved weather data in JSON format.

[2057] Step 2:

[2058] The server analyzes the weather information it receives and predicts changes in atmospheric pressure at specific locations and times.

[2059] Input: Weather data in JSON format.

[2060] Specific operation: Parse the JSON data, split it by region and time zone, and calculate the change in atmospheric pressure.

[2061] Output: Analyzed pressure forecast data.

[2062] Step 3:

[2063] The server manages the user's personal information.

[2064] Input: Profile information registered by the user (address, past health status, etc.).

[2065] Specific operation: User information is stored and managed in a database. Also, LINE account information is linked via API.

[2066] Output: User profile information stored in a database.

[2067] Step 4:

[2068] The server analyzes the user's emotional state.

[2069] Input: User-provided text data (e.g., diary entries or messages).

[2070] What it does: Uses a generative AI model to analyze text data and identify emotional states.

[2071] Output: Parsed emotional state data.

[2072] Step 5:

[2073] The server generates countermeasure advice based on barometric pressure forecast data, the user's personal information, and their emotional state.

[2074] Input: Parsed barometric pressure forecast data, user profile information, emotional state data.

[2075] What it does: Uses generative AI models to generate personalized countermeasure advice.

[2076] Output: The generated remediation advice text.

[2077] Step 6:

[2078] The server transmits the generated countermeasure advice to the user via a communication means.

[2079] Input: Generated advice text, user's LINE account information.

[2080] Specific operation: Advice on countermeasures is sent as a LINE message via the LINE official account API.

[2081] Output: Send a message to the user's LINE account.

[2082] Step 7:

[2083] The device receives the LINE message sent from the server and displays a notification.

[2084] Input: The LINE message sent from the server.

[2085] What happens: The LINE application displays the notification as a banner, and when the user taps on the message, more details are displayed.

[2086] Output: A notification is displayed to the user.

[2087] Step 8:

[2088] The user implements the measures notified.

[2089] Input: Countermeasure advice received via the LINE app.

[2090] Specific actions: Implement suggested measures (e.g., hydration, rest).

[2091] Output: Results of the implementation of the measures.

[2092] Step 9:

[2093] Users provide feedback on the effectiveness of the measures.

[2094] Input: Results of taking the measures and emotional state.

[2095] Specific behavior: Use the LINE application to enter and send feedback in text format.

[2096] Output: The feedback data sent.

[2097] Step 10:

[2098] The server receives feedback and emotion data from users and stores it in a database.

[2099] Input: Feedback data and emotional state data.

[2100] Specific operation: Save the received data in the database.

[2101] Output: Feedback data stored in a database.

[2102] Step 11:

[2103] The server inputs the collected feedback data and sentiment data into a machine learning algorithm to improve the accuracy of the next countermeasure advice.

[2104] Input: Feedback data and emotional state data.

[2105] Specific operation: A machine learning algorithm is used to analyze the feedback data and update the generative model for countermeasure advice.

[2106] Output: An improved model for generating countermeasure advice.

[2107] Through the above processing steps, a system is realized that prevents users from becoming ill due to changes in atmospheric pressure and provides individual and effective countermeasures for the user.

[2108] (Application example 2)

[2109] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2110] In modern society, changes in atmospheric pressure are a major cause of poor health for many people. In addition, people are exposed to various stressors every day, increasing the risk of poor health. However, current systems are unable to provide prompt and appropriate countermeasures against these changes in atmospheric pressure and stress, resulting in increased health risks. Furthermore, there is a lack of a mechanism for effectively utilizing feedback on health advice to improve the accuracy of countermeasures. Another issue is the lack of a means to smoothly purchase the necessary products as countermeasures.

[2111] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring weather data, means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area, means for analyzing the user's emotional state using an emotion analysis engine, means for generating and transmitting countermeasure advice to the user based on changes in atmospheric pressure and the user's emotional state, and means for providing electronic payment functionality and enabling the user to purchase health-related products. This makes it possible to provide personalized advice that takes into account changes in atmospheric pressure and the user's emotional state, and to smoothly purchase necessary countermeasure products. In addition, the server can collect user feedback and use machine learning algorithms to improve the accuracy of the advice.

[2112] "Weather data" is information that indicates atmospheric conditions and changes, and includes elements such as temperature, humidity, air pressure, wind speed, and wind direction.

[2113] An "emotion analysis engine" is software that analyzes a user's emotional state based on their text data and biometric information, and distinguishes between emotions such as anger, sadness, joy, and stress.

[2114] "Electronic payment function" is a system that allows users to pay for goods and services via the Internet, and includes payment methods such as credit cards, debit cards, and electronic money.

[2115] "Feedback" is information provided by the user, and is data that collects reactions such as the effectiveness of advice, changes in physical condition, and usage impressions.

[2116] "Machine learning algorithms" are a field of artificial intelligence that uses patterns learned from past data to make predictions and judgments about new data.

[2117] "Advice on countermeasures" is information that indicates specific actions and precautions that the user should take based on changes in atmospheric pressure and the user's emotional state, and includes suggestions for health management and mood improvement.

[2118] System Overview

[2119] This invention is a system for preventing poor health caused by changes in atmospheric pressure, and is configured with an emotion analysis engine that analyzes the user's emotional state. The system includes a function for collecting and analyzing weather data and predicting changes in atmospheric pressure in a specific area. It also generates optimal countermeasure advice based on the acquired data, individual user profile information, and emotion analysis results, and provides a mechanism for enabling users to purchase necessary health products using an electronic payment function.

[2120] System Details

[2121] Meteorological data collection and analysis tools

[2122] The server periodically obtains the latest barometric pressure forecast data via the weather agency's API. For example, it collects data every morning at 6:00 and predicts the daily barometric pressure change in a specific area. If a sudden change in barometric pressure is expected, it analyzes the time and amount of change and stores it in the database as an important event.

[2123] Emotion analysis means

[2124] The server uses an emotion analysis engine to analyze the user's emotional state in real time, which involves analyzing the user's text data and biometric information (e.g., heart rate and skin conductance) to determine emotional states such as stress, anger, and joy.

[2125] How to generate and send advice

[2126] The server generates optimal advice based on the collected barometric pressure forecast data, the user's profile information, and their emotional state. This advice is sent to the user's device via a communication method such as LINE. For example, specific advice such as "This afternoon, the barometric pressure will drop suddenly. You are likely to experience a headache, so it would be a good idea to stay hydrated and rest early. We also recommend that you take some time to relax."

[2127] Electronic payment facility

[2128] The app also provides an electronic payment function for users to purchase necessary health products. For example, health drinks and relaxation items recommended based on advice can be purchased directly within the app. This function allows users to receive advice and purchase products in a single flow.

[2129] Feedback collection and analysis methods

[2130] After implementing the advice, the user provides feedback on its effectiveness. This feedback data is collected along with the user's emotional state. The server analyzes this data and uses machine learning algorithms to improve the accuracy of future advice. For example, the server can improve the quality of the generated advice based on feedback such as, "The advice was effective, my headache was relieved, and I feel a little better."

[2131] Hardware and software used

[2132] Hardware: Smartphone

[2133] Software: LINE Bot API, weather data API, sentiment analysis engine, Python platform

[2134] Example prompt: "User A is experiencing a drop in barometric pressure at 2 PM. He is likely to have a headache, so he should stay hydrated and get some rest. He also seems to be feeling stressed, so it's a good idea to take some time to relax."

[2135] Specific examples

[2136] Example 1: User A, who lives in Tokyo, receives barometric pressure forecast information every morning via an app. This morning, the app predicted that barometric pressure would drop sharply between 2:00 PM and 4:00 PM, and sent User A advice on what to do via LINE. The advice sent reads, "Barometric pressure will drop sharply this afternoon. You are likely to get a headache, so it would be a good idea to stay hydrated and get some rest early. You are also feeling stressed, so we recommend that you take some time to relax."

[2137] Example 2: User A purchased the recommended health drink within the app using the electronic payment function and hydrated. When the atmospheric pressure dropped in the afternoon, the advance precautions prevented a headache. That evening, User A sent feedback via LINE that the advice was effective. The server applies a machine learning algorithm based on this feedback data to improve the accuracy of the advice.

[2138] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2139] Step 1:

[2140] The server obtains the latest barometric pressure forecast data from the meteorological agency's API. It calls the API every morning at 6:00 to collect barometric pressure data for each region. The input is the weather API endpoint, and the output is the barometric pressure forecast data for each region. It analyzes the data, identifies times of day when barometric pressure changes most suddenly, and stores the data in a database.

[2141] Step 2:

[2142] The server manages user profile information. It stores and updates the database with information collected at the time of initial registration, such as the area of ​​residence, past health conditions, and addictive measures. The input is the user's registration information, and the output is each user's profile data.

[2143] Step 3:

[2144] The server uses an emotion analysis engine to analyze the user's emotional state in real time. It determines emotions based on LINE messages and biometric data and records them in a database. The input is text data and biometric information, and the output is the analyzed emotional state.

[2145] Step 4:

[2146] The server generates optimal advice based on barometric pressure forecast data, the user's profile information, and their emotional state. The generated advice is sent to the user's device as a LINE message. The input is barometric pressure data, profile information, and their emotional state, and the output is an advice message. A generative AI model is used to create the advice.

[2147] Step 5:

[2148] The user receives the LINE message and takes the necessary measures according to the advice, such as staying hydrated or taking time to relax. The input is the received advice message, and the output is the implemented measures.

[2149] Step 6:

[2150] Users provide feedback on the effectiveness of the measures. They report the effectiveness along with their emotional state via LINE. The input is the emotional state after the measures are implemented and the feedback content, and the output is the sent feedback data.

[2151] Step 7:

[2152] The server collects user feedback and stores it in a database. It analyzes the feedback data and uses a machine learning algorithm to improve the countermeasure advice generation algorithm. The input is the feedback data, and the output is the improved advice generation algorithm.

[2153] Step 8:

[2154] The server improves the accuracy of the notification advice for the next barometric pressure change based on the improved advice generation algorithm. The newly generated advice message provides more accurate measures for the next barometric pressure change. The input is the improved algorithm, and the output is a more accurate advice message.

[2155] 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.

[2156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2157] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2158] 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.

[2159] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[2160] 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.

[2161] 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).

[2162] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2163] 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."

[2164] 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.

[2165] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2166] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2167] In the above embodiment, an example in which the specific processing program 56 is stored in the stora...

Claims

1. a means for acquiring weather data; A means for analyzing the acquired weather data and predicting changes in atmospheric pressure in a specific area; means for managing user profile information; means for generating and transmitting countermeasure advice to a user based on changes in atmospheric pressure; a means for collecting feedback from users and analyzing the data; A means to improve the accuracy of advice based on collected feedback; and A system including:

2. The system according to claim 1, wherein advice on measures to be taken in response to changes in atmospheric pressure is notified to the user via LINE.

3. The system of claim 1 , wherein the system uses machine learning algorithms to improve the accuracy of the advice based on user feedback.

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