system

Abstract

We provide the system. [Solution] Meteorological data collection methods, An interface means for inputting user health status data, A database means for storing the aforementioned weather data and the aforementioned health status data, A generative artificial intelligence means for analyzing the correlation between weather changes and health status based on the aforementioned database, A prediction means for predicting the occurrence of poor health based on the aforementioned correlation analysis, An alert sending means that notifies the user based on the prediction results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, along with climate change, the impact on health due to rapid changes in atmospheric pressure and weather has been increasing. Physical discomfort caused by such climate change, particularly symptoms such as headaches and dizziness, is difficult to predict and it is difficult to take appropriate preventive measures. As a result, individual users cannot cope with sudden physical discomfort, and there is a problem that the quality of life deteriorates. To solve such problems, a system that can analyze the correlation between the health status of individual users and climate change, predict the occurrence of physical discomfort according to climate change, and take effective countermeasures in advance is necessary.

Means for Solving the Problems

[0005] This invention provides an interface for acquiring weather information through a weather data collection means and inputting user health status data, thereby understanding the relationship between individual users' health status and weather changes. Based on this, a generative artificial intelligence means analyzes the correlation between weather changes and health status using data stored in a database. Furthermore, the system includes a prediction means for predicting the occurrence of health problems based on the correlation analysis, and an alert transmission means for sending specific alerts to users based on the prediction results, thereby providing a system that allows users to take countermeasures in advance. This system enables users to respond early to health problems caused by weather changes.

[0006] "Mechanisms for collecting weather data" refers to the function of periodically acquiring data from external weather data provision services or APIs in order to obtain weather information.

[0007] "An interface for inputting user health data" refers to a function that provides a form or application for users to input information about their own health condition.

[0008] A "database system" is a system for efficiently storing and managing acquired weather data and user health status data.

[0009] "Generative artificial intelligence means" refers to artificial intelligence technology that has the ability to analyze the correlation between weather changes and health conditions by utilizing machine learning algorithms based on collected data.

[0010] "Prediction means" refers to a function that uses generative artificial intelligence means to predict in advance the occurrence of health problems from weather data.

[0011] The "alert sending method" is a function that sends notifications to users based on the possibility of poor health determined by the prediction method, prompting them to take preventative measures. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The weather-related pain prediction system of the present invention operates based on the participation of a server, terminals, and users. The system first functions by having the server periodically acquire local weather and atmospheric pressure data from external weather data provision services via the internet. The server uses this data to store it in a database so that users can understand weather conditions at different locations and times.

[0034] The terminal provides an interface for users to input information about their health status and any health problems they may be experiencing. Through this interface, users input information such as the type, severity, and date and time of their symptoms, and send this information to the server. The server receives this data and stores it in a database, associating it with each user's profile.

[0035] The server uses weather and health data stored in a database and performs analysis using generative artificial intelligence. This AI learns patterns of health problems in response to weather changes and generates predictive models tailored to each user.

[0036] The key to this invention is a prediction method that predicts the occurrence of health problems based on future weather conditions, using a predictive model generated by a server using a machine learning algorithm. This makes it possible to predict the occurrence of health problems tailored to the user's health condition with high accuracy. For example, if there is a prediction of a sharp drop in atmospheric pressure in two days, and the server determines that there is a risk of headaches by referring to user A's past data, the server will issue a warning to user A.

[0037] Based on the forecast results, the server uses an alert sending mechanism to send notifications to the device, and the user receives the notification on their device. This notification includes specific countermeasures, such as, "The atmospheric pressure will drop sharply the afternoon after tomorrow, which may cause headaches. Please consider taking preventative medication." In this way, users can take precautions in advance and prepare for health problems caused by weather changes.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server periodically calls the API of a weather data service to retrieve the latest weather and atmospheric pressure data for a specified area. The retrieved data includes temperature, atmospheric pressure, humidity, and precipitation. This data is stored in a database within the system.

[0041] Step 2:

[0042] The device displays an interface prompting the user to report their health status. The user enters details of past health problems (e.g., date and time of occurrence, details of symptoms, and severity), and presses the submit button to send the data to the server.

[0043] Step 3:

[0044] The server stores health data received from users in a database and updates user-specific profiles. This allows for systematic management of past health history.

[0045] Step 4:

[0046] The server utilizes generative artificial intelligence to perform correlation analysis using weather and health data from a database. This allows it to learn the impact of specific weather conditions on health and build different predictive models for each user.

[0047] Step 5:

[0048] Based on a predictive model built by the server, the system predicts the occurrence of health problems from future weather data. For example, if the weather forecast for two days from now predicts a drop in atmospheric pressure, it refers to past patterns to determine which users are more likely to experience headaches.

[0049] Step 6:

[0050] The server sends an alert to the terminal based on the forecast results. The terminal notifies the user of the received alert and prompts the user to take preventative measures in advance. This notification might, for example, inform the user that the atmospheric pressure will drop at a specific date and time, and that they should prepare preventative medication.

[0051] (Example 1)

[0052] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0053] Predicting the impact of changing weather conditions on human health and preventing health problems before they occur is difficult. This can lead to users being unable to respond quickly to health problems caused by weather fluctuations, potentially disrupting their daily lives. Therefore, there is a need for methods that effectively utilize weather data and user health information to accurately predict and notify users of health problems in advance.

[0054] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0055] In this invention, the server includes means for acquiring weather information, a device for inputting information about the user's health status, and a storage device for recording the weather information and the health status information. This allows the user to understand in advance the impact of weather changes on their health and take appropriate measures.

[0056] "Means for acquiring weather information" refers to a device or function for periodically acquiring current and future weather data from external weather data sources.

[0057] A "device for inputting information about a user's health status" is a device that has an interface that allows users to manually or automatically input information about their own health status or any health problems.

[0058] A "storage device" is a device that stores acquired weather information and user health information in storage such as a database, and manages the data so that it can be searched and used as needed.

[0059] A "generative artificial intelligence device" is a device that uses machine learning algorithms based on accumulated data to analyze the relationship between weather changes and health conditions and generate predictive models.

[0060] A "device for predicting physical ailments" is a device that utilizes a predictive model obtained from a generative artificial intelligence device to predict the impact of future weather conditions on the user's health.

[0061] A "warning transmission device" is a device that sends warnings to users based on predictions of physical ailments and proposes specific countermeasures.

[0062] The weather-related pain prediction system of the present invention mainly consists of three elements: a server, a terminal, and a user. These elements work together to analyze changes in weather conditions and health status and predict physical discomfort.

[0063] The server functions as a means of obtaining weather information from external weather data providers via the internet. Specifically, the server periodically receives information such as temperature, atmospheric pressure, and humidity using APIs. OpenWeatherMap and the Japan Meteorological Agency's data API can be used to obtain data. The server records this data in database systems such as MySQL® or PostgreSQL.

[0064] The terminal functions as a device for users to input information about their health status and provides a graphical user interface (GUI). Using this GUI, users can intuitively input symptoms such as headaches and joint pain, as well as their intensity and the time they occur. This data is sent to a server and stored in a database.

[0065] The server uses generative artificial intelligence devices to apply machine learning algorithms to the accumulated data, analyzing the relationship between weather changes and health conditions. By using machine learning frameworks such as Google's TENSORFLOW and Facebook's PyTorch, predictive models can be generated from the data.

[0066] Users receive predictions of physical ailments based on future weather conditions, using a predictive model generated by the server. For example, if the server detects a sudden change in atmospheric pressure, it will warn the user about the symptoms that may be caused by that change. The warning is sent as a push notification to the device, allowing the user to take specific countermeasures in advance.

[0067] For example, if the predictive model determines that "there will be a sudden drop in atmospheric pressure in two days, and there is a high probability of headaches," the user can take appropriate measures. An example of a prompt for the generating AI model would be text such as, "Please generate a model that predicts physical discomfort associated with changes in atmospheric pressure, based on the health data entered by the user."

[0068] This system will enable users to prevent health problems caused by weather changes and manage their health more efficiently.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server obtains weather information from an external weather data provider service. Specifically, the server sends API requests to receive data such as temperature, atmospheric pressure, and humidity as input. This data is stored in a database in preparation for subsequent processing.

[0072] Step 2:

[0073] The terminal provides an interface for users to input health information. Users input health information such as whether they have a headache, its severity, and the time it occurred, and this data is sent to the server. The server records the received data in a database.

[0074] Step 3:

[0075] The server uses a generative artificial intelligence system to analyze weather and health information stored in the database. Based on the input data, it extracts the relationship between weather conditions and health status and generates a predictive model using a machine learning framework. As an output of this process, a predictive model tailored to each individual user is obtained.

[0076] Step 4:

[0077] The server uses the generated prediction model to predict physical ailments based on future weather conditions. The server takes weather data as input, applies the prediction model to calculate the likelihood of physical ailments, and retrieves the prediction result as output.

[0078] Step 5:

[0079] The server generates an alert based on the forecast results and sends it to the user's device. The user receives a notification via the device that includes specific actions to take. For example, a warning might appear stating, "Tomorrow the atmospheric pressure will drop, which may cause headaches, so please consider taking preventative measures." This allows the user to plan appropriate actions in advance.

[0080] (Application Example 1)

[0081] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0082] In recent years, health problems caused by changes in weather conditions have affected many people, making it necessary to predict changes in health in advance and take appropriate measures. Furthermore, it is essential to support a more comfortable life by providing information on maintaining health and offering appropriate products tailored to individual users.

[0083] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0084] In this invention, the server includes means for acquiring weather information, input means for inputting user health information, and storage means for storing the weather information and health information. This enables health prediction based on the relationship between weather conditions and health status, and the recommendation of appropriate products.

[0085] "Means of obtaining weather information" refers to a function that collects current and predicted weather data from external weather data provision services via the internet.

[0086] "An input method for entering user health information" refers to an interface that allows users to input information about their health status and any health problems they may be experiencing.

[0087] A "storage device" is a device that stores collected weather information and user health information in a database, forming a foundation for data analysis.

[0088] A "knowledge processing tool" analyzes the relationship between weather conditions and health status based on accumulated data and generates a predictive model using a generative artificial intelligence model.

[0089] A "predictive structure" is a system that uses a model generated by a knowledge processing tool to predict the likelihood of health deterioration based on future weather conditions.

[0090] "Information transmission means" refers to a communication function that notifies the user of prediction results and provides appropriate countermeasures.

[0091] A "product recommendation method" is a system that suggests products suitable for a predicted deterioration in health to the user, thereby supporting health management.

[0092] The system for realizing this invention mainly consists of a server, terminals, and users. The server is responsible for periodically acquiring weather information from a weather data provision service via the internet. This includes a software module for acquiring weather data using an external API.

[0093] Users can input their health information through their device. This device refers to mobile devices such as smartphones and tablets, and provides an input interface through a dedicated application. This application utilizes front-end technology with excellent user interface design, making it intuitive for users to operate.

[0094] The server stores collected weather data and health information in a database and uses it for knowledge processing. Database management uses a configuration that allows for efficient data storage and retrieval, such as an SQL database.

[0095] The server further analyzes the accumulated data using generative artificial intelligence models. This includes machine learning techniques using the Python library scikit-learn. This analyzes the correlation between weather conditions and health status and generates a predictive model. The predictive structure can accurately predict health deterioration based on future weather conditions.

[0096] Based on the prediction results, the server sends notifications to users through information transmission methods. These include push notifications and email services, ensuring users receive information quickly. Specifically, it warns about the possibility of health deterioration due to weather changes and suggests countermeasures.

[0097] Furthermore, the system also suggests products that can help users maintain their health through product recommendation mechanisms. This incorporates recommendation algorithms based on past purchase history and current health status. For example, it might send a message such as, "We predict a possibility of headaches due to the drop in atmospheric pressure tomorrow. Please consider headache medication A, which you have purchased in the past."

[0098] An example of a prompt message might be: "Predict what symptoms this user is likely to experience based on the weather conditions, and suggest products that can address these issues based on past data."

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server obtains weather information from external weather data providers via the internet. It uses an API as input to collect current and forecast weather data. This data is received in JSON format, and temperature and atmospheric pressure information is extracted and stored in a database.

[0102] Step 2:

[0103] Users input health information through the terminal's interface. This input includes health data such as symptom type, severity, and date of onset, which is then manually entered using a GUI. The input data is sent from the terminal to the server. The server receives this information and stores it in individual user profiles.

[0104] Step 3:

[0105] The server trains a generative artificial intelligence model using accumulated weather and health data. Historical weather and health data are retrieved from the database as input, and the data is analyzed using the scikit-learn library. Data calculations, such as regression analysis and classification, are performed to learn the relationship between weather conditions and health status. A predictive model is then generated based on these learning results.

[0106] Step 4:

[0107] The server uses a predictive model to forecast the likelihood of health deterioration based on future weather conditions. The latest weather data is used as input and analyzed by the predictive model. The output provides a prediction of the impact of specific weather conditions on the user's health. For example, it can determine the likelihood of a headache due to a drop in atmospheric pressure on a particular day.

[0108] Step 5:

[0109] The server sends a notification to the user based on the prediction results. It references the prediction results as input and generates a notification message. It then sends the message to the user's device via push notification or email using an information transmission method. For example, the notification might say, "The atmospheric pressure will drop the day after tomorrow, which may cause headaches. Please consider taking preventative measures."

[0110] Step 6:

[0111] The server recommends products suitable for the predicted deterioration of the user's health. It uses a product recommendation algorithm by matching the user's past purchase history and current health information as input. The output is a list of appropriate products. Specifically, it notifies the user with suggestions such as, "Please reconsider headache medicine A, which you previously purchased."

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] The weather-related pain prediction system, which incorporates the emotion engine of the present invention, is realized through the interaction of a server, a terminal, and a user. The system is started by the server periodically acquiring weather information from an external weather data provision service and storing it in a database. User health status data is collected through the terminal interface. This interface provides a form for the user to input information about their health status, physical ailments, and emotions.

[0114] The health and emotional data entered by the user is sent to the server and stored in the corresponding profile in the database. The server feeds this data to a generative artificial intelligence system, which, along with weather data, analyzes the correlations between different weather conditions and health and emotional states. In this analysis, the emotion engine plays a crucial role, generating predictive models that take the user's emotional state into account, thereby more accurately evaluating the factors influencing individual users.

[0115] As a concrete example, suppose the server learns that user B experiences headaches in relation to a sudden drop in atmospheric pressure and has learned emotional data. Based on this information, the prediction system predicts the risk that future weather conditions will cause similar symptoms. Furthermore, if the emotional data is also associated with increased stress or anxiety, the alert system will suggest countermeasures to the user to mitigate that risk.

[0116] The emotion engine in this system analyzes acquired emotional data in real time, and the artificial intelligence contributes to making predictions that take into account the impact of emotional changes on physical condition. For example, if there is data that user C tends to feel anxious during low atmospheric pressure, it is possible to use this to notify the user in advance of appropriate preventative measures. In this way, the system can improve the overall accuracy of predicting physical ailments and provide users with specific and useful information.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server periodically retrieves weather data from an external weather data provider. This data includes information such as temperature, atmospheric pressure, humidity, and wind speed. This data is stored in a database.

[0120] Step 2:

[0121] The device displays an interface for the user to report their health and emotional state. The user enters any symptoms of illness they experienced on a specific date and time, their severity, and their emotions at that time (e.g., stress, joy, anxiety).

[0122] Step 3:

[0123] The data entered by the user is sent from the terminal to the server. The server organizes this information for each user and stores it in a database. This allows for a detailed record of the user's health and emotional history.

[0124] Step 4:

[0125] The server uses generative artificial intelligence to analyze weather data, health status data, and emotional data stored in the database. This analysis learns how specific weather conditions affect health and emotional states.

[0126] Step 5:

[0127] The server uses a pre-trained model to predict the occurrence of physical ailments and emotional states based on future weather data. This prediction is personalized for each user, based on their past emotional and health data.

[0128] Step 6:

[0129] Based on the prediction results, the server sends a notification to the user's device using an alert sending mechanism. The notification may include suggestions for countermeasures regarding the risk of physical ailments or emotional issues.

[0130] Step 7:

[0131] Users receive notifications on their devices and, based on the content of those notifications, take specific measures such as preparing preventative medication or planning actions to reduce stress.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] Conventional health prediction systems have been unable to adequately consider the impact of weather conditions on health status, making it difficult to provide highly accurate predictions that reflect the user's emotional state. As a result, there is a problem in that they cannot appropriately predict health problems caused by weather changes and provide countermeasures.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes information gathering means for acquiring and periodically storing weather information, an input device for the user to input their health and emotional state, and storage means for storing the weather information and health state data. This makes it possible to analyze the correlation between different weather conditions and health and emotional states, predict changes in physical condition with high accuracy using a predictive model that takes the user's emotional state into account, and propose specific countermeasures to the user.

[0137] "Weather information" refers to data related to weather conditions, such as temperature, humidity, and atmospheric pressure.

[0138] "Information gathering means" refers to a function that uses external data provision services to periodically acquire and store weather information.

[0139] An "input device" is an interface that a user uses to input information about their health and emotional state into a system.

[0140] The "memory means" refers to a database function for storing collected weather information and data on the user's health and emotional state.

[0141] "Data analysis means" refers to a function that uses stored data to analyze the correlation between different weather conditions and health and emotional states.

[0142] A "predictive model" is a computational model used to predict changes in a user's physical condition, taking into account the relationship between the user's health status, weather conditions, and emotional state.

[0143] A "notification method" is a function that suggests specific measures to mitigate risks to the user based on predicted changes in their physical condition.

[0144] The system of this invention is realized through the interaction of a server, a terminal, and a user. The server periodically acquires weather information from an external data provision service and stores it in its own database. A method via an API is often used to acquire weather information. As a specific example, a general weather API service that provides information such as temperature, humidity, and atmospheric pressure is used.

[0145] Users input their health and emotional states through a device. The device is equipped with a dedicated application and web interface, allowing users to easily input data. The entered data is then transmitted from the device to a server. The input device features an intuitive and user-friendly UI, enabling efficient data entry.

[0146] The server uses the transmitted health status and emotion data to analyze its correlation with weather information. Generative AI models are used for the analysis to generate predictive models that take into account the user's emotional state. Machine learning algorithms are used as the generative AI models. Specifically, deep learning frameworks such as TensorFlow and PyTorch are sometimes used.

[0147] Based on the analysis results, the server predicts changes in the user's physical condition and notifies the terminal. The notification then suggests specific measures to mitigate the predicted risks. This notification is delivered in real time, supporting the user in immediately implementing the countermeasures.

[0148] As a concrete example, the following prompt statement may be used as input to a generating AI model: "There is data showing that user C tends to become anxious during low-pressure periods. When the next low-pressure period is expected, what precautionary measures should be notified to user C?"

[0149] In this way, the system of the present invention provides highly accurate health predictions based on weather information and individual health and emotional data, and enables the suggestion of measures optimized for each individual user.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] The server obtains the latest weather information from an external weather data provider. It uses an API to retrieve data such as temperature, humidity, and atmospheric pressure. Parameters for the API call (such as geographical area and time) are used as input, and a set of weather data is obtained as output. This data is stored in a database on the server.

[0153] Step 2:

[0154] Users input their health and emotional states through a dedicated application on their device or a web interface. The input data includes health conditions (e.g., headache, fatigue) and emotional states (e.g., stress, anxiety). To maintain input consistency, the device validates the data and formats it appropriately as needed. The output is formatted health data.

[0155] Step 3:

[0156] The device transmits health and emotional state data collected from the user to the server. A secure protocol is used for this communication, and the data is delivered to the server in real time. Formatted user data is used as input, and the output is provided in a format that can be stored on the server.

[0157] Step 4:

[0158] The server feeds a generative AI model with user health data, sentiment data, and weather data stored in a database. Using these datasets as input, the generative AI model applies machine learning algorithms to analyze correlations. As output, a user-specific predictive model is constructed. This model is used to understand weather conditions that may affect an individual user's health.

[0159] Step 5:

[0160] The server predicts the user's health status under future weather conditions based on the generated prediction model. Specifically, it uses current weather conditions and the user's profile as input and calculates the user's specific risk of health changes as output. This allows for specific countermeasures to be taken for each individual user based on the predicted risk.

[0161] Step 6:

[0162] The server notifies the user based on the forecast. The notification system sends alerts to the user's device and suggests countermeasures. It uses the predicted risks and suggested countermeasures as input and provides instructions and advice that the user can immediately implement as output. This allows users to manage their health and mitigate health risks caused by weather changes.

[0163] (Application Example 2)

[0164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0165] Existing security systems struggle to predict user emotional and health conditions, making it particularly difficult to provide accurate countermeasures against health problems and security risks influenced by weather changes. There is a need for a system that addresses this challenge and comprehensively manages user health and security.

[0166] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0167] In this invention, the server includes data collection means for acquiring weather information, information input means for inputting the user's health information and emotional state, and information storage means for accumulating the weather information, health information, and emotional state. This enables risk assessment based on the user's emotional state and health information, and prediction of health and security risks associated with weather changes.

[0168] "Means of data collection for obtaining weather information" refers to technology for periodically acquiring weather data from external weather data provision services and making it available for use in the system.

[0169] "Information input means for inputting user health information and emotional state" refers to technology that provides an interface for users to input their own health status and emotional changes, and collects that data.

[0170] "Information storage means for accumulating weather information, health information, and emotional state" refers to a database system that efficiently stores acquired weather information, health information, and emotional state, and makes them accessible as needed.

[0171] "Generative intelligence analysis means" refers to artificial intelligence technology that analyzes the correlation between weather changes and accumulated weather information, health information, and emotional state data.

[0172] A "risk assessment tool" is a technology that uses correlation analysis results elucidated by generative intelligence analysis tools to predict health problems and security risks, and to take appropriate action based on those results.

[0173] "Information transmission means" refers to technology that notifies users of issues predicted by risk assessment means and provides necessary countermeasures.

[0174] This system performs risk assessments that take into account the user's emotions and health status in security situations. The server periodically collects weather data using a weather data provision service (e.g., OpenWeatherMap) to obtain weather information. The terminal provides a form for the user to input their emotions and health status via smart glasses or a head-mounted display, and transmits this information to the server.

[0175] The server has a means of storing information, including a database (specifically, MongoDB, etc.) that efficiently stores acquired weather data and user health and emotional state data. Based on this stored data, the server uses generative intelligence analysis to analyze the correlations between the stored data. This technology utilizes machine learning frameworks such as TensorFlow and PyTorch to build a model that evaluates the impact of weather changes on user health and emotions.

[0176] Furthermore, the risk assessment system includes a means of transmitting information that predicts risks related to the user's health and security status based on the analyzed data and notifies the user of these risks. This notification includes specific countermeasures so that preventative measures can be taken in advance. For example, if weather changes that could cause stress to security personnel are predicted for the night, the display will show a warning and a suggestion of relaxing music.

[0177] Examples of specific prompt messages are as follows:

[0178] "Based on weather data and user sentiment data, predict the stress risk during security patrols and propose appropriate countermeasures."

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The device receives emotional and health data from the user as input using smart glasses or a head-mounted display. This includes text and voice input via the wearable device's interface. The input data is converted into a digital format and sent to the server via a security token.

[0182] Step 2:

[0183] The server periodically retrieves weather information for a specific region from an external weather data provider. The weather data (temperature, humidity, atmospheric pressure, etc.) obtained via the API is stored in a database. MongoDB is used for the database, ensuring efficient data storage and management.

[0184] Step 3:

[0185] The server's generative intelligence analysis method uses accumulated health information, emotional states, and weather data as input and analyzes correlations using a machine learning model. A model built with TensorFlow evaluates how emotional states are affected by weather changes. As a result, a correlation matrix is ​​output.

[0186] Step 4:

[0187] The server risk assessment method predicts health and security risks based on the analysis results. Using a correlation matrix as input, it predicts the risk level in future situations. A risk score is calculated as the prediction result.

[0188] Step 5:

[0189] The server notifies the user of the predicted risk score based on the information transmission method. The notification to the user includes a message that contains specific countermeasures. The message is transmitted as a digital signal to the terminal's display and is displayed visually to the user.

[0190] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0191] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0192] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0193] [Second Embodiment]

[0194] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0195] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0196] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0197] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0198] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0200] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0201] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0202] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0203] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0204] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0205] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0206] The weather-related pain prediction system of the present invention operates based on the participation of a server, terminals, and users. The system first functions by having the server periodically acquire local weather and atmospheric pressure data from external weather data provision services via the internet. The server uses this data to store it in a database so that users can understand weather conditions at different locations and times.

[0207] The terminal provides an interface for users to input information about their health status and any health problems they may be experiencing. Through this interface, users input information such as the type, severity, and date and time of their symptoms, and send this information to the server. The server receives this data and stores it in a database, associating it with each user's profile.

[0208] The server uses weather data and health status data stored in the database and performs analysis using generative artificial intelligence. This AI learns patterns of health problems in response to weather changes and generates predictive models tailored to each user.

[0209] The key to this invention is a prediction method that predicts the occurrence of health problems based on future weather conditions, using a predictive model generated by a server using a machine learning algorithm. This makes it possible to predict the occurrence of health problems tailored to the user's health condition with high accuracy. For example, if there is a prediction of a sharp drop in atmospheric pressure in two days, and the server determines that there is a risk of headaches by referring to user A's past data, the server will issue a warning to user A.

[0210] Based on the forecast results, the server uses an alert sending mechanism to send notifications to the device, and the user receives the notification on their device. This notification includes specific countermeasures, such as, "The atmospheric pressure will drop sharply the afternoon after tomorrow, which may cause headaches. Please consider taking preventative medication." In this way, users can take precautions in advance and prepare for health problems caused by weather changes.

[0211] The following describes the processing flow.

[0212] Step 1:

[0213] The server periodically calls the API of a weather data service to retrieve the latest weather and atmospheric pressure data for a specified area. The retrieved data includes temperature, atmospheric pressure, humidity, and precipitation. This data is stored in a database within the system.

[0214] Step 2:

[0215] The device displays an interface prompting the user to report their health status. The user enters details of past health problems (e.g., date and time of occurrence, details of symptoms, and severity), and presses the submit button to send the data to the server.

[0216] Step 3:

[0217] The server stores health data received from users in a database and updates user-specific profiles. This allows for systematic management of past health history.

[0218] Step 4:

[0219] The server utilizes generative artificial intelligence to perform correlation analysis using weather data and health status data from a database. This allows it to learn the impact of specific weather conditions on health and build different predictive models for each user.

[0220] Step 5:

[0221] Based on a prediction model built by the server, the system predicts the occurrence of health problems from future weather data. For example, if the weather forecast for two days from now predicts a drop in atmospheric pressure, it refers to past patterns to determine which users are more likely to experience headaches.

[0222] Step 6:

[0223] The server sends an alert to the terminal based on the forecast results. The terminal notifies the user of the received alert and prompts the user to take preventative measures in advance. This notification might, for example, inform the user that the atmospheric pressure will drop at a specific date and time, and that they should prepare preventative medication.

[0224] (Example 1)

[0225] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0226] Predicting the impact of changing weather conditions on human health and preventing health problems before they occur is difficult. This can lead to users being unable to respond quickly to health problems caused by weather fluctuations, potentially disrupting their daily lives. Therefore, there is a need for methods that effectively utilize weather data and user health information to accurately predict and notify users of health problems in advance.

[0227] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0228] In this invention, the server includes means for acquiring weather information, a device for inputting information about the user's health status, and a storage device for recording the weather information and the health status information. This allows the user to understand in advance the impact of weather changes on their health and take appropriate measures.

[0229] "Means for acquiring weather information" refers to a device or function for periodically acquiring current and future weather data from external weather data sources.

[0230] A "device for inputting information about a user's health status" is a device that has an interface that allows users to manually or automatically input information about their own health status or any health problems.

[0231] A "storage device" is a device that stores acquired weather information and user health information in storage such as a database, and manages the data so that it can be searched and used as needed.

[0232] A "generative artificial intelligence device" is a device that uses machine learning algorithms based on accumulated data to analyze the relationship between weather changes and health conditions and generate predictive models.

[0233] A "device for predicting physical ailments" is a device that utilizes a predictive model obtained from a generative artificial intelligence device to predict the impact of future weather conditions on the user's health.

[0234] A "warning transmission device" is a device that sends warnings to users based on predictions of physical ailments and proposes specific countermeasures.

[0235] The weather-related pain prediction system of the present invention mainly consists of three elements: a server, a terminal, and a user. These elements work together to analyze changes in weather conditions and health status and predict physical discomfort.

[0236] The server functions as a means of obtaining weather information from external weather data providers via the internet. Specifically, the server periodically receives information such as temperature, atmospheric pressure, and humidity using APIs. OpenWeatherMap and the Japan Meteorological Agency's data API can be used for data acquisition. The server records this data in database systems such as MySQL and PostgreSQL.

[0237] The terminal functions as a device for users to input information about their health status and provides a graphical user interface (GUI). Using this GUI, users can intuitively input symptoms such as headaches and joint pain, as well as their intensity and the time they occur. This data is sent to a server and stored in a database.

[0238] The server uses generative artificial intelligence (AI) devices to apply machine learning algorithms to the accumulated data, analyzing the relationship between weather changes and health conditions. By using machine learning frameworks such as Google's TensorFlow and Facebook's PyTorch, predictive models can be generated from the data.

[0239] Users receive predictions of physical ailments based on future weather conditions, using a predictive model generated by the server. For example, if the server detects a sudden change in atmospheric pressure, it will warn the user about the symptoms that may be caused by that change. The warning is sent as a push notification to the device, allowing the user to take specific countermeasures in advance.

[0240] For example, if the predictive model determines that "there will be a sudden drop in atmospheric pressure in two days, and there is a high probability of headaches," the user can take appropriate measures. An example of a prompt for the generating AI model would be text such as, "Please generate a model that predicts physical discomfort associated with changes in atmospheric pressure, based on the health data entered by the user."

[0241] This system will enable users to prevent health problems caused by weather changes and manage their health more efficiently.

[0242] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0243] Step 1:

[0244] The server obtains weather information from an external weather data provider service. Specifically, the server sends API requests to receive data such as temperature, atmospheric pressure, and humidity as input. This data is stored in a database in preparation for subsequent processing.

[0245] Step 2:

[0246] The terminal provides an interface for users to input health information. Users input health information such as whether they have a headache, its severity, and the time it occurred, and this data is sent to the server. The server records the received data in a database.

[0247] Step 3:

[0248] The server uses a generative artificial intelligence system to analyze weather and health information stored in the database. Based on the input data, it extracts the relationship between weather conditions and health status and generates a predictive model using a machine learning framework. As an output of this process, a predictive model tailored to each individual user is obtained.

[0249] Step 4:

[0250] The server uses the generated prediction model to predict physical ailments based on future weather conditions. The server takes weather data as input, applies the prediction model to calculate the likelihood of physical ailments, and retrieves the prediction result as output.

[0251] Step 5:

[0252] The server generates an alert based on the forecast results and sends it to the user's device. The user receives a notification via the device that includes specific actions to take. For example, a warning might appear stating, "Tomorrow the atmospheric pressure will drop, which may cause headaches, so please consider taking preventative measures." This allows the user to plan appropriate actions in advance.

[0253] (Application Example 1)

[0254] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] In recent years, health problems caused by changes in weather conditions have affected many people, making it necessary to predict changes in health in advance and take appropriate measures. Furthermore, it is essential to support a more comfortable life by providing information on maintaining health and offering appropriate products tailored to individual users.

[0256] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0257] In this invention, the server includes means for acquiring weather information, input means for inputting user health information, and storage means for storing the weather information and health information. This enables health prediction based on the relationship between weather conditions and health status, and the recommendation of appropriate products.

[0258] "Means of obtaining weather information" refers to a function that collects current and predicted weather data from external weather data provision services via the internet.

[0259] "An input method for entering user health information" refers to an interface that allows users to input information about their health status and any health problems they may be experiencing.

[0260] A "storage device" is a device that stores collected weather information and user health information in a database, forming a foundation for data analysis.

[0261] A "knowledge processing tool" analyzes the relationship between weather conditions and health status based on accumulated data and generates a predictive model using a generative artificial intelligence model.

[0262] A "predictive structure" is a system that uses a model generated by a knowledge processing tool to predict the likelihood of health deterioration based on future weather conditions.

[0263] "Information transmission means" refers to a communication function that notifies the user of prediction results and provides appropriate countermeasures.

[0264] A "product recommendation method" is a system that suggests products suitable for a predicted deterioration in health to the user, thereby supporting health management.

[0265] The system for realizing this invention mainly consists of a server, terminals, and users. The server is responsible for periodically acquiring weather information from a weather data provision service via the internet. This includes a software module for acquiring weather data using an external API.

[0266] Users can input their health information through their device. This device refers to mobile devices such as smartphones and tablets, and provides an input interface through a dedicated application. This application utilizes front-end technology with excellent user interface design, making it intuitive for users to operate.

[0267] The server stores collected weather data and health information in a database and uses it for knowledge processing. Database management uses a configuration that allows for efficient data storage and retrieval, such as an SQL database.

[0268] The server further analyzes the accumulated data using generative artificial intelligence models. This includes machine learning techniques using the Python library scikit-learn. This analyzes the correlation between weather conditions and health status and generates a predictive model. The predictive structure can accurately predict health deterioration based on future weather conditions.

[0269] Based on the forecast results, the server sends notifications to users through information transmission methods. These include push notifications and email services, ensuring users receive information quickly. Specifically, it warns about the possibility of health deterioration due to weather changes and suggests countermeasures.

[0270] Furthermore, the system also suggests products that can help users maintain their health through product recommendation mechanisms. This incorporates recommendation algorithms based on past purchase history and current health status. For example, it might send a message such as, "We predict a possibility of headaches due to the drop in atmospheric pressure tomorrow. Please consider headache medication A, which you have purchased in the past."

[0271] An example of a prompt message might be: "Predict what symptoms this user is likely to experience based on the weather conditions, and suggest products that can address these issues based on past data."

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The server obtains weather information from external weather data providers via the internet. It uses an API as input to collect current and forecast weather data. This data is received in JSON format, and temperature and pressure information is extracted and stored in a database.

[0275] Step 2:

[0276] Users input health information through the terminal's interface. This input includes health data such as symptom type, severity, and date of onset, which is then manually entered using a GUI. The input data is sent from the terminal to the server. The server receives this information and stores it in individual user profiles.

[0277] Step 3:

[0278] The server trains a generative artificial intelligence model using the accumulated weather information and health data. As input, past weather data and health information are retrieved from the database, and the data is analyzed using the scikit-learn library. For data operations, regression analysis and classification are performed to learn the relationship between weather conditions and health status. A prediction model is generated based on the learning results.

[0279] Step 4:

[0280] The server uses the prediction model to predict the likelihood of a deterioration in physical condition based on future weather conditions. Using the latest weather data as input, it is analyzed with the prediction model. As output, a prediction result regarding the impact of specific weather conditions on the user's health status is obtained. For example, it determines the likelihood of a headache due to a drop in air pressure on a specific day.

[0281] Step 5:

[0282] The server sends a notification to the user based on the prediction result. Referring to the prediction result as input, a notification message is generated. Using the information transmission means, it is sent to the user's terminal via a Push notification or email. As a specific operation, it notifies the user with content such as "Since the air pressure will drop the day after tomorrow, there is a possibility of a headache. Please consider preventive measures."

[0283] Step 6:

[0284] The server recommends products suitable for the predicted physical deterioration. By matching the user's past purchase history and current health information as input and utilizing a product recommendation algorithm, an appropriate product list is generated as output. Specifically, it notifies the user with a proposal such as "Please consider purchasing headache medicine A again, which you have bought in the past."

[0285] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0286] The weather pain prediction system combined with the emotion engine of the present invention is realized through the interaction of a server, a terminal, and a user. The system starts with the server periodically acquiring weather information from an external weather data providing service and storing it in a database. The health status data of the user is collected through the interface of the terminal. This interface provides a form for the user to input information regarding their health status, physical discomfort, and emotions.

[0287] The health status data and emotion data input by the user are sent to the server and stored in the corresponding profile in the database. The server supplies this data to generative artificial intelligence means and analyzes the correlations that exist between different weather conditions, health statuses, and emotional states, together with the weather data. In this analysis, the role played by the emotion engine is important, and by generating a prediction model that takes into account the user's emotional state, the factors affecting individual users can be more accurately evaluated.

[0288] As a specific example, assume that the server has learned the headache occurrence and emotion data of user B in relation to a rapidly dropping atmospheric pressure. Based on this information, the prediction means predicts the risk of similar symptoms being caused by future weather conditions. Furthermore, if the emotion data is also related to increasing stress and anxiety, the alert sending means proposes countermeasures to the user to reduce that risk.

[0289] The emotion engine in this system analyzes the acquired emotion data in real time and contributes to the artificial intelligence making predictions considering the impact of emotional changes on physical condition. For example, if there is data indicating that user C is prone to feeling anxious during low atmospheric pressure, it is possible to notify appropriate preventive measures in advance based on this. In this way, the system can comprehensively improve the prediction accuracy of physical discomfort and provide information that is specifically effective for the user.

[0290] The processing flow will be described below.

[0291] Step 1:

[0292] The server periodically retrieves weather data from an external weather data provider. This data includes information such as temperature, atmospheric pressure, humidity, and wind speed. This data is stored in a database.

[0293] Step 2:

[0294] The device displays an interface for the user to report their health and emotional state. The user enters any symptoms of illness they experienced on a specific date and time, their severity, and their emotions at that time (e.g., stress, joy, anxiety).

[0295] Step 3:

[0296] The data entered by the user is sent from the terminal to the server. The server organizes this information for each user and stores it in a database. This allows for a detailed record of the user's health and emotional history.

[0297] Step 4:

[0298] The server uses generative artificial intelligence to analyze weather data, health status data, and emotional data stored in the database. This analysis learns how specific weather conditions affect health and emotional states.

[0299] Step 5:

[0300] The server uses a pre-trained model to predict the occurrence of physical ailments and emotional states based on future weather data. This prediction is personalized for each user, based on their past emotional and health data.

[0301] Step 6:

[0302] Based on the prediction results, the server uses the alert sending means to send notifications to the user's terminal. The notifications may include the risk of poor physical condition and countermeasure plans for emotions.

[0303] Step 7:

[0304] The user receives the notification on the terminal and takes specific countermeasures based on the content, such as preparing preventive medicine and planning actions for stress reduction.

[0305] (Example 2)

[0306] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] In the conventional physical condition prediction system, it was difficult to fully consider the influence of weather conditions on the health state and to make highly accurate predictions reflecting the user's emotional state. As a result, there is a problem that it is impossible to appropriately predict and provide countermeasures for physical discomfort caused by weather changes.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0309] In this invention, the server includes an information collection means for acquiring and periodically storing weather information, an input device for the user to input the health state and emotional state, and a storage means for storing the weather information and the health state data. Thereby, it becomes possible to analyze the correlation between different weather situations, health states, and emotional states, use a prediction model considering the user's emotional state, accurately predict physical condition changes, and propose specific countermeasures to the user.

[0310] "Weather information" refers to data related to weather conditions such as temperature, humidity, and atmospheric pressure.

[0311] "Information gathering means" refers to a function that uses external data provision services to periodically acquire and store weather information.

[0312] An "input device" is an interface that a user uses to input information about their health and emotional state into a system.

[0313] The "memory means" refers to a database function for storing collected weather information and data on the user's health and emotional state.

[0314] "Data analysis means" refers to a function that uses stored data to analyze the correlation between different weather conditions and health and emotional states.

[0315] A "predictive model" is a computational model used to predict changes in a user's physical condition, taking into account the relationship between the user's health status, weather conditions, and emotional state.

[0316] A "notification method" is a function that suggests specific measures to mitigate risks to the user based on predicted changes in their physical condition.

[0317] The system of this invention is realized through the interaction of a server, a terminal, and a user. The server periodically acquires weather information from an external data provision service and stores it in its own database. A method via an API is often used to acquire weather information. As a specific example, a general weather API service that provides information such as temperature, humidity, and atmospheric pressure is used.

[0318] Users input their health and emotional states through a device. The device is equipped with a dedicated application and web interface, allowing users to easily input data. The entered data is then transmitted from the device to a server. The input device features an intuitive and user-friendly UI, enabling efficient data entry.

[0319] The server uses the transmitted health status and emotion data to analyze its correlation with weather information. Generative AI models are used for the analysis to generate predictive models that take into account the user's emotional state. Machine learning algorithms are used as the generative AI models. Specifically, deep learning frameworks such as TensorFlow and PyTorch are sometimes used.

[0320] Based on the analysis results, the server predicts changes in the user's physical condition and notifies the terminal. The notification then suggests specific measures to mitigate the predicted risks. This notification is delivered in real time, supporting the user in immediately implementing the countermeasures.

[0321] As a concrete example, the following prompt statement may be used as input to a generating AI model: "There is data showing that user C tends to become anxious during low-pressure periods. When the next low-pressure period is expected, what precautionary measures should be notified to user C?"

[0322] In this way, the system of the present invention provides highly accurate health predictions based on weather information and individual health and emotional data, and enables the suggestion of measures optimized for each individual user.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The server obtains the latest weather information from an external weather data provider. It uses an API to retrieve data such as temperature, humidity, and atmospheric pressure. Parameters for the API call (such as geographical area and time) are used as input, and a set of weather data is obtained as output. This data is stored in a database on the server.

[0326] Step 2:

[0327] Users input their health and emotional states through a dedicated application on their device or a web interface. The input data includes health conditions (e.g., headache, fatigue) and emotional states (e.g., stress, anxiety). To maintain input consistency, the device validates the data and formats it appropriately as needed. The output is formatted health data.

[0328] Step 3:

[0329] The device transmits health and emotional state data collected from the user to the server. A secure protocol is used for this communication, and the data is delivered to the server in real time. Formatted user data is used as input, and the output is provided in a format that can be stored on the server.

[0330] Step 4:

[0331] The server feeds a generative AI model with user health data, sentiment data, and weather data stored in a database. Using these datasets as input, the generative AI model applies machine learning algorithms to analyze correlations. As output, a user-specific predictive model is constructed. This model is used to understand weather conditions that may affect an individual user's health.

[0332] Step 5:

[0333] The server predicts the user's health status under future weather conditions based on the generated prediction model. Specifically, it uses current weather conditions and the user's profile as input and calculates the user's specific risk of health changes as output. This allows for specific countermeasures to be taken for each individual user based on the predicted risk.

[0334] Step 6:

[0335] The server notifies the user based on the forecast. The notification system sends alerts to the user's device and suggests countermeasures. It uses the predicted risks and suggested countermeasures as input and provides instructions and advice that the user can immediately implement as output. This allows users to manage their health and mitigate health risks caused by weather changes.

[0336] (Application Example 2)

[0337] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0338] Existing security systems struggle to predict user emotional and health conditions, making it particularly difficult to provide accurate countermeasures against health problems and security risks influenced by weather changes. There is a need for a system that addresses this challenge and comprehensively manages user health and security.

[0339] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0340] In this invention, the server includes data collection means for acquiring weather information, information input means for inputting the user's health information and emotional state, and information storage means for accumulating the weather information, health information, and emotional state. This enables risk assessment based on the user's emotional state and health information, and prediction of health and security risks associated with weather changes.

[0341] "Means of data collection for obtaining weather information" refers to technology for periodically acquiring weather data from external weather data provision services and making it available for use in the system.

[0342] "Information input means for inputting user health information and emotional state" refers to technology that provides an interface for users to input their own health status and emotional changes, and collects that data.

[0343] "Information storage means for accumulating weather information, health information, and emotional state" refers to a database system that efficiently stores acquired weather information, health information, and emotional state, and makes them accessible as needed.

[0344] "Generative intelligence analysis means" refers to artificial intelligence technology that analyzes the correlation between weather changes and accumulated weather information, health information, and emotional state data.

[0345] A "risk assessment tool" is a technology that uses correlation analysis results elucidated by generative intelligence analysis tools to predict health problems and security risks, and to take appropriate action based on those results.

[0346] "Information transmission means" refers to technology that notifies users of issues predicted by risk assessment means and provides necessary countermeasures.

[0347] This system performs risk assessments that take into account the user's emotions and health status in security situations. The server periodically collects weather data using a weather data provision service (e.g., OpenWeatherMap) to obtain weather information. The terminal provides a form for the user to input their emotions and health status via smart glasses or a head-mounted display, and transmits this information to the server.

[0348] The server has a means of storing information, including a database (specifically, MongoDB, etc.) that efficiently stores acquired weather data and user health and emotional state data. Based on this stored data, the server uses generative intelligence analysis to analyze the correlations between the stored data. This technology utilizes machine learning frameworks such as TensorFlow and PyTorch to build a model that evaluates the impact of weather changes on user health and emotions.

[0349] Furthermore, the risk assessment system includes a means of transmitting information that predicts risks related to the user's health and security status based on the analyzed data and notifies the user of these risks. This notification includes specific countermeasures so that preventative measures can be taken in advance. For example, if weather changes that could cause stress to security personnel are predicted for the night, the display will show a warning and a suggestion of relaxing music.

[0350] Examples of specific prompt messages are as follows:

[0351] "Based on weather data and user sentiment data, predict the stress risk during security patrols and propose appropriate countermeasures."

[0352] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0353] Step 1:

[0354] The device receives emotional and health data from the user as input using smart glasses or a head-mounted display. This includes text and voice input via the wearable device's interface. The input data is converted into a digital format and sent to the server via a security token.

[0355] Step 2:

[0356] The server periodically retrieves weather information for a specific region from an external weather data provider. The weather data (temperature, humidity, atmospheric pressure, etc.) obtained via the API is stored in a database. MongoDB is used for the database, ensuring efficient data storage and management.

[0357] Step 3:

[0358] The server's generative intelligence analysis method uses accumulated health information, emotional states, and weather data as input and analyzes correlations using a machine learning model. A model built with TensorFlow evaluates how emotional states are affected by weather changes. As a result, a correlation matrix is ​​output.

[0359] Step 4:

[0360] The server risk assessment method predicts health and security risks based on the analysis results. Using a correlation matrix as input, it predicts the risk level in future situations. A risk score is calculated as the prediction result.

[0361] Step 5:

[0362] The server notifies the user of the predicted risk score based on the information transmission method. The notification to the user includes a message that contains specific countermeasures. The message is transmitted as a digital signal to the terminal's display and is displayed visually to the user.

[0363] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0364] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0365] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0366] [Third Embodiment]

[0367] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0368] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0369] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0370] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0371] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0373] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0374] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0375] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0376] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0377] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0378] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0379] The weather-related pain prediction system of the present invention operates based on the participation of a server, terminals, and users. The system first functions by having the server periodically acquire local weather and atmospheric pressure data from external weather data provision services via the internet. The server uses this data to store it in a database so that users can understand weather conditions at different locations and times.

[0380] The terminal provides an interface for users to input information about their health status and any health problems they may be experiencing. Through this interface, users input information such as the type, severity, and date and time of their symptoms, and send this information to the server. The server receives this data and stores it in a database, associating it with each user's profile.

[0381] The server uses weather data and health status data stored in the database and performs analysis using generative artificial intelligence. This AI learns patterns of health problems in response to weather changes and generates predictive models tailored to each user.

[0382] The key to this invention is a prediction method that predicts the occurrence of health problems based on future weather conditions, using a predictive model generated by a server using a machine learning algorithm. This makes it possible to predict the occurrence of health problems tailored to the user's health condition with high accuracy. For example, if there is a prediction of a sharp drop in atmospheric pressure in two days, and the server determines that there is a risk of headaches by referring to user A's past data, the server will issue a warning to user A.

[0383] Based on the forecast results, the server uses an alert sending mechanism to send notifications to the device, and the user receives the notification on their device. This notification includes specific countermeasures, such as, "The atmospheric pressure will drop sharply the afternoon after tomorrow, which may cause headaches. Please consider taking preventative medication." In this way, users can take precautions in advance and prepare for health problems caused by weather changes.

[0384] The following describes the processing flow.

[0385] Step 1:

[0386] The server periodically calls the API of a weather data service to retrieve the latest weather and atmospheric pressure data for a specified area. The retrieved data includes temperature, atmospheric pressure, humidity, and precipitation. This data is stored in a database within the system.

[0387] Step 2:

[0388] The device displays an interface prompting the user to report their health status. The user enters details of past health problems (e.g., date and time of occurrence, details of symptoms, and severity), and presses the submit button to send the data to the server.

[0389] Step 3:

[0390] The server stores health data received from users in a database and updates user-specific profiles. This allows for systematic management of past health history.

[0391] Step 4:

[0392] The server utilizes generative artificial intelligence to perform correlation analysis using weather data and health status data from a database. This allows it to learn the impact of specific weather conditions on health and build different predictive models for each user.

[0393] Step 5:

[0394] Based on a prediction model built by the server, the system predicts the occurrence of health problems from future weather data. For example, if the weather forecast for two days from now predicts a drop in atmospheric pressure, it refers to past patterns to determine which users are more likely to experience headaches.

[0395] Step 6:

[0396] The server sends an alert to the terminal based on the forecast results. The terminal notifies the user of the received alert and prompts the user to take preventative measures in advance. This notification might, for example, inform the user that the atmospheric pressure will drop at a specific date and time, and that they should prepare preventative medication.

[0397] (Example 1)

[0398] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0399] Predicting the impact of changing weather conditions on human health and preventing health problems before they occur is difficult. This can lead to users being unable to respond quickly to health problems caused by weather fluctuations, potentially disrupting their daily lives. Therefore, there is a need for methods that effectively utilize weather data and user health information to accurately predict and notify users of health problems in advance.

[0400] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0401] In this invention, the server includes means for acquiring weather information, a device for inputting information about the user's health status, and a storage device for recording the weather information and the health status information. This allows the user to understand in advance the impact of weather changes on their health and take appropriate measures.

[0402] "Means for acquiring weather information" refers to a device or function for periodically acquiring current and future weather data from external weather data sources.

[0403] A "device for inputting information about a user's health status" is a device that has an interface that allows users to manually or automatically input information about their own health status or any health problems.

[0404] A "storage device" is a device that stores acquired weather information and user health information in storage such as a database, and manages the data so that it can be searched and used as needed.

[0405] A "generative artificial intelligence device" is a device that uses machine learning algorithms based on accumulated data to analyze the relationship between weather changes and health conditions and generate predictive models.

[0406] A "device for predicting physical ailments" is a device that utilizes a predictive model obtained from a generative artificial intelligence device to predict the impact of future weather conditions on the user's health.

[0407] A "warning transmission device" is a device that sends warnings to users based on predictions of physical ailments and proposes specific countermeasures.

[0408] The weather-related pain prediction system of the present invention mainly consists of three elements: a server, a terminal, and a user. These elements work together to analyze changes in weather conditions and health status and predict physical discomfort.

[0409] The server functions as a means of obtaining weather information from external weather data providers via the internet. Specifically, the server periodically receives information such as temperature, atmospheric pressure, and humidity using APIs. OpenWeatherMap and the Japan Meteorological Agency's data API can be used for data acquisition. The server records this data in database systems such as MySQL and PostgreSQL.

[0410] The terminal functions as a device for users to input information about their health status and provides a graphical user interface (GUI). Using this GUI, users can intuitively input symptoms such as headaches and joint pain, as well as their intensity and the time they occur. This data is sent to a server and stored in a database.

[0411] The server uses generative artificial intelligence (AI) devices to apply machine learning algorithms to the accumulated data, analyzing the relationship between weather changes and health conditions. By using machine learning frameworks such as Google's TensorFlow and Facebook's PyTorch, predictive models can be generated from the data.

[0412] Users receive predictions of physical ailments based on future weather conditions, using a predictive model generated by the server. For example, if the server detects a sudden change in atmospheric pressure, it will warn the user about the symptoms that may be caused by that change. The warning is sent as a push notification to the device, allowing the user to take specific countermeasures in advance.

[0413] For example, if the predictive model determines that "there will be a sudden drop in atmospheric pressure in two days, and there is a high probability of headaches," the user can take appropriate measures. An example of a prompt for the generating AI model would be text such as, "Please generate a model that predicts physical discomfort associated with changes in atmospheric pressure, based on the health data entered by the user."

[0414] This system will enable users to prevent health problems caused by weather changes and manage their health more efficiently.

[0415] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0416] Step 1:

[0417] The server obtains weather information from an external weather data provider service. Specifically, the server sends API requests to receive data such as temperature, atmospheric pressure, and humidity as input. This data is stored in a database in preparation for subsequent processing.

[0418] Step 2:

[0419] The terminal provides an interface for users to input health information. Users input health information such as whether they have a headache, its severity, and the time it occurred, and this data is sent to the server. The server records the received data in a database.

[0420] Step 3:

[0421] The server uses a generative artificial intelligence system to analyze weather and health information stored in the database. Based on the input data, it extracts the relationship between weather conditions and health status and generates a predictive model using a machine learning framework. As an output of this process, a predictive model tailored to each individual user is obtained.

[0422] Step 4:

[0423] The server uses the generated prediction model to predict physical ailments based on future weather conditions. The server takes weather data as input, applies the prediction model to calculate the likelihood of physical ailments, and retrieves the prediction result as output.

[0424] Step 5:

[0425] The server generates an alert based on the forecast results and sends it to the user's device. The user receives a notification via the device that includes specific actions to take. For example, a warning might appear stating, "Tomorrow the atmospheric pressure will drop, which may cause headaches, so please consider taking preventative measures." This allows the user to plan appropriate actions in advance.

[0426] (Application Example 1)

[0427] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0428] In recent years, health problems caused by changes in weather conditions have affected many people, making it necessary to predict changes in health in advance and take appropriate measures. Furthermore, it is essential to support a more comfortable life by providing information on maintaining health and offering appropriate products tailored to individual users.

[0429] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0430] In this invention, the server includes means for acquiring weather information, input means for inputting user health information, and storage means for storing the weather information and health information. This enables health prediction based on the relationship between weather conditions and health status, and the recommendation of appropriate products.

[0431] "Means of obtaining weather information" refers to a function that collects current and predicted weather data from external weather data provision services via the internet.

[0432] "An input method for entering user health information" refers to an interface that allows users to input information about their health status and any health problems they may be experiencing.

[0433] A "storage device" is a device that stores collected weather information and user health information in a database, forming a foundation for data analysis.

[0434] A "knowledge processing tool" analyzes the relationship between weather conditions and health status based on accumulated data and generates a predictive model using a generative artificial intelligence model.

[0435] A "predictive structure" is a system that uses a model generated by a knowledge processing tool to predict the likelihood of health deterioration based on future weather conditions.

[0436] "Information transmission means" refers to a communication function that notifies the user of prediction results and provides appropriate countermeasures.

[0437] A "product recommendation method" is a system that suggests products suitable for a predicted deterioration in health to the user, thereby supporting health management.

[0438] The system for realizing this invention mainly consists of a server, terminals, and users. The server is responsible for periodically acquiring weather information from a weather data provision service via the internet. This includes a software module for acquiring weather data using an external API.

[0439] Users can input their health information through their device. This device refers to mobile devices such as smartphones and tablets, and provides an input interface through a dedicated application. This application utilizes front-end technology with excellent user interface design, making it intuitive for users to operate.

[0440] The server stores collected weather data and health information in a database and uses it for knowledge processing. Database management uses a configuration that allows for efficient data storage and retrieval, such as an SQL database.

[0441] The server further analyzes the accumulated data using generative artificial intelligence models. This includes machine learning techniques using the Python library scikit-learn. This analyzes the correlation between weather conditions and health status and generates a predictive model. The predictive structure can accurately predict health deterioration based on future weather conditions.

[0442] Based on the forecast results, the server sends notifications to users through information transmission methods. These include push notifications and email services, ensuring users receive information quickly. Specifically, it warns about the possibility of health deterioration due to weather changes and suggests countermeasures.

[0443] Furthermore, the system also suggests products that can help users maintain their health through product recommendation mechanisms. This incorporates recommendation algorithms based on past purchase history and current health status. For example, it might send a message such as, "We predict a possibility of headaches due to the drop in atmospheric pressure tomorrow. Please consider headache medication A, which you have purchased in the past."

[0444] An example of a prompt message might be: "Predict what symptoms this user is likely to experience based on the weather conditions, and suggest products that can address these issues based on past data."

[0445] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0446] Step 1:

[0447] The server obtains weather information from external weather data providers via the internet. It uses an API as input to collect current and forecast weather data. This data is received in JSON format, and temperature and pressure information is extracted and stored in a database.

[0448] Step 2:

[0449] Users input health information through the terminal's interface. This input includes health data such as symptom type, severity, and date of onset, which is then manually entered using a GUI. The input data is sent from the terminal to the server. The server receives this information and stores it in individual user profiles.

[0450] Step 3:

[0451] The server trains a generative artificial intelligence model using accumulated weather and health data. Historical weather and health data are retrieved from the database as input, and the data is analyzed using the scikit-learn library. Data calculations, such as regression analysis and classification, are performed to learn the relationship between weather conditions and health status. A predictive model is then generated based on these learning results.

[0452] Step 4:

[0453] The server uses a predictive model to forecast the likelihood of health deterioration based on future weather conditions. The latest weather data is used as input and analyzed by the predictive model. The output provides a prediction of the impact of specific weather conditions on the user's health. For example, it can determine the likelihood of a headache due to a drop in atmospheric pressure on a particular day.

[0454] Step 5:

[0455] The server sends a notification to the user based on the prediction results. It references the prediction results as input and generates a notification message. It then sends the message to the user's device via push notification or email using an information transmission method. For example, the notification might say, "The atmospheric pressure will drop the day after tomorrow, which may cause headaches. Please consider taking preventative measures."

[0456] Step 6:

[0457] The server recommends products suitable for the predicted deterioration of the user's health. It uses a product recommendation algorithm by matching the user's past purchase history and current health information as input. The output is a list of appropriate products. Specifically, it notifies the user with suggestions such as, "Please reconsider headache medicine A, which you previously purchased."

[0458] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0459] The weather-related pain prediction system, which incorporates the emotion engine of the present invention, is realized through the interaction of a server, a terminal, and a user. The system is started by the server periodically acquiring weather information from an external weather data provision service and storing it in a database. User health status data is collected through the terminal interface. This interface provides a form for the user to input information about their health status, physical ailments, and emotions.

[0460] The health and emotional data entered by the user is sent to the server and stored in the corresponding profile in the database. The server feeds this data to a generative artificial intelligence system, which, along with weather data, analyzes the correlations between different weather conditions and health and emotional states. In this analysis, the emotion engine plays a crucial role, generating predictive models that take the user's emotional state into account, thereby more accurately evaluating the factors influencing individual users.

[0461] As a concrete example, suppose the server learns that user B experiences headaches in relation to a sudden drop in atmospheric pressure and has learned emotional data. Based on this information, the prediction system predicts the risk that future weather conditions will cause similar symptoms. Furthermore, if the emotional data is also associated with increased stress or anxiety, the alert system will suggest countermeasures to the user to mitigate that risk.

[0462] The emotion engine in this system analyzes acquired emotional data in real time, and the artificial intelligence contributes to making predictions that take into account the impact of emotional changes on physical condition. For example, if there is data that user C tends to feel anxious during low atmospheric pressure, it is possible to use this to notify the user in advance of appropriate preventative measures. In this way, the system can improve the overall accuracy of predicting physical ailments and provide users with specific and useful information.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The server periodically retrieves weather data from an external weather data provider. This data includes information such as temperature, atmospheric pressure, humidity, and wind speed. This data is stored in a database.

[0466] Step 2:

[0467] The device displays an interface for the user to report their health and emotional state. The user enters any symptoms of illness they experienced on a specific date and time, their severity, and their emotions at that time (e.g., stress, joy, anxiety).

[0468] Step 3:

[0469] The data entered by the user is sent from the terminal to the server. The server organizes this information for each user and stores it in a database. This allows for a detailed record of the user's health and emotional history.

[0470] Step 4:

[0471] The server uses generative artificial intelligence to analyze weather data, health status data, and emotional data stored in the database. This analysis learns how specific weather conditions affect health and emotional states.

[0472] Step 5:

[0473] The server uses a pre-trained model to predict the occurrence of physical ailments and emotional states based on future weather data. This prediction is personalized for each user, based on their past emotional and health data.

[0474] Step 6:

[0475] Based on the prediction results, the server sends a notification to the user's device using an alert sending mechanism. The notification may include suggestions for countermeasures regarding the risk of physical ailments or emotional issues.

[0476] Step 7:

[0477] Users receive notifications on their devices and, based on the content of those notifications, take specific measures such as preparing preventative medication or planning actions to reduce stress.

[0478] (Example 2)

[0479] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] Conventional health prediction systems have been unable to adequately consider the impact of weather conditions on health status, making it difficult to provide highly accurate predictions that reflect the user's emotional state. As a result, there is a problem in that they cannot appropriately predict health problems caused by weather changes and provide countermeasures.

[0481] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0482] In this invention, the server includes information gathering means for acquiring and periodically storing weather information, an input device for the user to input their health and emotional state, and storage means for storing the weather information and health state data. This makes it possible to analyze the correlation between different weather conditions and health and emotional states, predict changes in physical condition with high accuracy using a predictive model that takes the user's emotional state into account, and propose specific countermeasures to the user.

[0483] "Weather information" refers to data related to weather conditions, such as temperature, humidity, and atmospheric pressure.

[0484] "Information gathering means" refers to a function that uses external data provision services to periodically acquire and store weather information.

[0485] An "input device" is an interface that a user uses to input information about their health and emotional state into a system.

[0486] The "memory means" refers to a database function for storing collected weather information and data on the user's health and emotional state.

[0487] "Data analysis means" refers to a function that uses stored data to analyze the correlation between different weather conditions and health and emotional states.

[0488] A "predictive model" is a computational model used to predict changes in a user's physical condition, taking into account the relationship between the user's health status, weather conditions, and emotional state.

[0489] A "notification method" is a function that suggests specific measures to mitigate risks to the user based on predicted changes in their physical condition.

[0490] The system of this invention is realized through the interaction of a server, a terminal, and a user. The server periodically acquires weather information from an external data provision service and stores it in its own database. A method via an API is often used to acquire weather information. As a specific example, a general weather API service that provides information such as temperature, humidity, and atmospheric pressure is used.

[0491] Users input their health and emotional states through a device. The device is equipped with a dedicated application and web interface, allowing users to easily input data. The entered data is then transmitted from the device to a server. The input device features an intuitive and user-friendly UI, enabling efficient data entry.

[0492] The server uses the transmitted health status and emotion data to analyze its correlation with weather information. Generative AI models are used for the analysis to generate predictive models that take into account the user's emotional state. Machine learning algorithms are used as the generative AI models. Specifically, deep learning frameworks such as TensorFlow and PyTorch are sometimes used.

[0493] Based on the analysis results, the server predicts changes in the user's physical condition and notifies the terminal. The notification then suggests specific measures to mitigate the predicted risks. This notification is delivered in real time, supporting the user in immediately implementing the countermeasures.

[0494] As a concrete example, the following prompt statement may be used as input to a generating AI model: "There is data showing that user C tends to become anxious during low-pressure periods. When the next low-pressure period is expected, what precautionary measures should be notified to user C?"

[0495] In this way, the system of the present invention provides highly accurate health predictions based on weather information and individual health and emotional data, and enables the suggestion of measures optimized for each individual user.

[0496] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0497] Step 1:

[0498] The server obtains the latest weather information from an external weather data provider. It uses an API to retrieve data such as temperature, humidity, and atmospheric pressure. Parameters for the API call (such as geographical area and time) are used as input, and a set of weather data is obtained as output. This data is stored in a database on the server.

[0499] Step 2:

[0500] Users input their health and emotional states through a dedicated application on their device or a web interface. The input data includes health conditions (e.g., headache, fatigue) and emotional states (e.g., stress, anxiety). To maintain input consistency, the device validates the data and formats it appropriately as needed. The output is formatted health data.

[0501] Step 3:

[0502] The device transmits health and emotional state data collected from the user to the server. A secure protocol is used for this communication, and the data is delivered to the server in real time. Formatted user data is used as input, and the output is provided in a format that can be stored on the server.

[0503] Step 4:

[0504] The server feeds a generative AI model with user health data, sentiment data, and weather data stored in a database. Using these datasets as input, the generative AI model applies machine learning algorithms to analyze correlations. As output, a user-specific predictive model is constructed. This model is used to understand weather conditions that may affect an individual user's health.

[0505] Step 5:

[0506] The server predicts the user's health status under future weather conditions based on the generated prediction model. Specifically, it uses current weather conditions and the user's profile as input and calculates the user's specific risk of health changes as output. This allows for specific countermeasures to be taken for each individual user based on the predicted risk.

[0507] Step 6:

[0508] The server notifies the user based on the forecast. The notification system sends alerts to the user's device and suggests countermeasures. It uses the predicted risks and suggested countermeasures as input and provides instructions and advice that the user can immediately implement as output. This allows users to manage their health and mitigate health risks caused by weather changes.

[0509] (Application Example 2)

[0510] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] Existing security systems struggle to predict user emotional and health conditions, making it particularly difficult to provide accurate countermeasures against health problems and security risks influenced by weather changes. There is a need for a system that addresses this challenge and comprehensively manages user health and security.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0513] In this invention, the server includes data collection means for acquiring weather information, information input means for inputting the user's health information and emotional state, and information storage means for accumulating the weather information, health information, and emotional state. This enables risk assessment based on the user's emotional state and health information, and prediction of health and security risks associated with weather changes.

[0514] "Means of data collection for obtaining weather information" refers to technology for periodically acquiring weather data from external weather data provision services and making it available for use in the system.

[0515] "Information input means for inputting user health information and emotional state" refers to technology that provides an interface for users to input their own health status and emotional changes, and collects that data.

[0516] "Information storage means for accumulating weather information, health information, and emotional state" refers to a database system that efficiently stores acquired weather information, health information, and emotional state, and makes them accessible as needed.

[0517] "Generative intelligence analysis means" refers to artificial intelligence technology that analyzes the correlation between weather changes and accumulated weather information, health information, and emotional state data.

[0518] A "risk assessment tool" is a technology that uses correlation analysis results elucidated by generative intelligence analysis tools to predict health problems and security risks, and to take appropriate action based on those results.

[0519] "Information transmission means" refers to technology that notifies users of issues predicted by risk assessment means and provides necessary countermeasures.

[0520] This system performs risk assessments that take into account the user's emotions and health status in security situations. The server periodically collects weather data using a weather data provision service (e.g., OpenWeatherMap) to obtain weather information. The terminal provides a form for the user to input their emotions and health status via smart glasses or a head-mounted display, and transmits this information to the server.

[0521] The server has a means of storing information, including a database (specifically, MongoDB, etc.) that efficiently stores acquired weather data and user health and emotional state data. Based on this stored data, the server uses generative intelligence analysis to analyze the correlations between the stored data. This technology utilizes machine learning frameworks such as TensorFlow and PyTorch to build a model that evaluates the impact of weather changes on user health and emotions.

[0522] Furthermore, the risk assessment system includes a means of transmitting information that predicts risks related to the user's health and security status based on the analyzed data and notifies the user of these risks. This notification includes specific countermeasures so that preventative measures can be taken in advance. For example, if weather changes that could cause stress to security personnel are predicted for the night, the display will show a warning and a suggestion of relaxing music.

[0523] Examples of specific prompt messages are as follows:

[0524] "Based on weather data and user sentiment data, predict the stress risk during security patrols and propose appropriate countermeasures."

[0525] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0526] Step 1:

[0527] The device receives emotional and health data from the user as input using smart glasses or a head-mounted display. This includes text and voice input via the wearable device's interface. The input data is converted into a digital format and sent to the server via a security token.

[0528] Step 2:

[0529] The server periodically retrieves weather information for a specific region from an external weather data provider. The weather data (temperature, humidity, atmospheric pressure, etc.) obtained via the API is stored in a database. MongoDB is used for the database, ensuring efficient data storage and management.

[0530] Step 3:

[0531] The server's generative intelligence analysis system uses accumulated health information, emotional states, and weather data as input and analyzes correlations using a machine learning model. A model built with TensorFlow evaluates how emotional states are affected by weather changes. As a result, a correlation matrix is ​​output.

[0532] Step 4:

[0533] The server risk assessment method predicts health and security risks based on the analysis results. Using a correlation matrix as input, it predicts the risk level in future situations. A risk score is calculated as the prediction result.

[0534] Step 5:

[0535] The server notifies the user of the predicted risk score based on the information transmission method. The notification to the user includes a message that contains specific countermeasures. The message is transmitted as a digital signal to the terminal's display and is displayed visually to the user.

[0536] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0537] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0538] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0539] [Fourth Embodiment]

[0540] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0541] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0542] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0543] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0544] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0545] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0546] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0547] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0548] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0549] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0550] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0551] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0552] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0553] The weather-related pain prediction system of the present invention operates based on the participation of a server, terminals, and users. The system first functions by having the server periodically acquire local weather and atmospheric pressure data from external weather data provision services via the internet. The server uses this data to store it in a database so that users can understand weather conditions at different locations and times.

[0554] The terminal provides an interface for users to input information about their health status and any health problems they may be experiencing. Through this interface, users input information such as the type, severity, and date and time of their symptoms, and send this information to the server. The server receives this data and stores it in a database, associating it with each user's profile.

[0555] The server uses weather data and health status data stored in the database and performs analysis using generative artificial intelligence. This AI learns patterns of health problems in response to weather changes and generates predictive models tailored to each user.

[0556] The key to this invention is a prediction method that predicts the occurrence of health problems based on future weather conditions, using a predictive model generated by a server using a machine learning algorithm. This makes it possible to predict the occurrence of health problems tailored to the user's health condition with high accuracy. For example, if there is a prediction of a sharp drop in atmospheric pressure in two days, and the server determines that there is a risk of headaches by referring to user A's past data, the server will issue a warning to user A.

[0557] Based on the forecast results, the server uses an alert sending mechanism to send notifications to the device, and the user receives the notification on their device. This notification includes specific countermeasures, such as, "The atmospheric pressure will drop sharply the afternoon after tomorrow, which may cause headaches. Please consider taking preventative medication." In this way, users can take precautions in advance and prepare for health problems caused by weather changes.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server periodically calls the API of a weather data service to retrieve the latest weather and atmospheric pressure data for a specified area. The retrieved data includes temperature, atmospheric pressure, humidity, and precipitation. This data is stored in a database within the system.

[0561] Step 2:

[0562] The device displays an interface prompting the user to report their health status. The user enters details of past health problems (e.g., date and time of occurrence, details of symptoms, and severity), and presses the submit button to send the data to the server.

[0563] Step 3:

[0564] The server stores health data received from users in a database and updates user-specific profiles. This allows for systematic management of past health history.

[0565] Step 4:

[0566] The server utilizes generative artificial intelligence to perform correlation analysis using weather data and health status data from a database. This allows it to learn the impact of specific weather conditions on health and build different predictive models for each user.

[0567] Step 5:

[0568] Based on a prediction model built by the server, the system predicts the occurrence of health problems from future weather data. For example, if the weather forecast for two days from now predicts a drop in atmospheric pressure, it refers to past patterns to determine which users are more likely to experience headaches.

[0569] Step 6:

[0570] The server sends an alert to the terminal based on the forecast results. The terminal notifies the user of the received alert and prompts the user to take preventative measures in advance. This notification might, for example, inform the user that the atmospheric pressure will drop at a specific date and time, and that they should prepare preventative medication.

[0571] (Example 1)

[0572] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0573] Predicting the impact of changing weather conditions on human health and preventing health problems before they occur is difficult. This can lead to users being unable to respond quickly to health problems caused by weather fluctuations, potentially disrupting their daily lives. Therefore, there is a need for methods that effectively utilize weather data and user health information to accurately predict and notify users of health problems in advance.

[0574] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0575] In this invention, the server includes means for acquiring weather information, a device for inputting information about the user's health status, and a storage device for recording the weather information and the health status information. This allows the user to understand in advance the impact of weather changes on their health and take appropriate measures.

[0576] "Means for acquiring weather information" refers to a device or function for periodically acquiring current and future weather data from external weather data sources.

[0577] A "device for inputting information about a user's health status" is a device that has an interface that allows users to manually or automatically input information about their own health status or any health problems.

[0578] A "storage device" is a device that stores acquired weather information and user health information in storage such as a database, and manages the data so that it can be searched and used as needed.

[0579] A "generative artificial intelligence device" is a device that uses machine learning algorithms based on accumulated data to analyze the relationship between weather changes and health conditions and generate predictive models.

[0580] A "device for predicting physical ailments" is a device that utilizes a predictive model obtained from a generative artificial intelligence device to predict the impact of future weather conditions on the user's health.

[0581] A "warning transmission device" is a device that sends warnings to users based on predictions of physical ailments and proposes specific countermeasures.

[0582] The weather-related pain prediction system of the present invention mainly consists of three elements: a server, a terminal, and a user. These elements work together to analyze changes in weather conditions and health status and predict physical discomfort.

[0583] The server functions as a means of obtaining weather information from external weather data providers via the internet. Specifically, the server periodically receives information such as temperature, atmospheric pressure, and humidity using APIs. OpenWeatherMap and the Japan Meteorological Agency's data API can be used for data acquisition. The server records this data in database systems such as MySQL and PostgreSQL.

[0584] The terminal functions as a device for users to input information about their health status and provides a graphical user interface (GUI). Using this GUI, users can intuitively input symptoms such as headaches and joint pain, as well as their intensity and the time they occur. This data is sent to a server and stored in a database.

[0585] The server uses generative artificial intelligence (AI) devices to apply machine learning algorithms to the accumulated data, analyzing the relationship between weather changes and health conditions. By using machine learning frameworks such as Google's TensorFlow and Facebook's PyTorch, predictive models can be generated from the data.

[0586] Users receive predictions of physical ailments based on future weather conditions, using a predictive model generated by the server. For example, if the server detects a sudden change in atmospheric pressure, it will warn the user about the symptoms that may be caused by that change. The warning is sent as a push notification to the device, allowing the user to take specific countermeasures in advance.

[0587] For example, if the predictive model determines that "there will be a sudden drop in atmospheric pressure in two days, and there is a high probability of headaches," the user can take appropriate measures. An example of a prompt for the generating AI model would be text such as, "Please generate a model that predicts physical discomfort associated with changes in atmospheric pressure, based on the health data entered by the user."

[0588] This system will enable users to prevent health problems caused by weather changes and manage their health more efficiently.

[0589] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0590] Step 1:

[0591] The server obtains weather information from an external weather data provider service. Specifically, the server sends API requests to receive data such as temperature, atmospheric pressure, and humidity as input. This data is stored in a database in preparation for subsequent processing.

[0592] Step 2:

[0593] The terminal provides an interface for users to input health information. Users input health information such as whether they have a headache, its severity, and the time it occurred, and this data is sent to the server. The server records the received data in a database.

[0594] Step 3:

[0595] The server uses a generative artificial intelligence system to analyze weather and health information stored in the database. Based on the input data, it extracts the relationship between weather conditions and health status and generates a predictive model using a machine learning framework. As an output of this process, a predictive model tailored to each individual user is obtained.

[0596] Step 4:

[0597] The server uses the generated prediction model to predict physical ailments based on future weather conditions. The server takes weather data as input, applies the prediction model to calculate the likelihood of physical ailments, and retrieves the prediction result as output.

[0598] Step 5:

[0599] The server generates an alert based on the forecast results and sends it to the user's device. The user receives a notification via the device that includes specific actions to take. For example, a warning might appear stating, "Tomorrow the atmospheric pressure will drop, which may cause headaches, so please consider taking preventative measures." This allows the user to plan appropriate actions in advance.

[0600] (Application Example 1)

[0601] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] In recent years, health problems caused by changes in weather conditions have affected many people, making it necessary to predict changes in health in advance and take appropriate measures. Furthermore, it is essential to support a more comfortable life by providing information on maintaining health and offering appropriate products tailored to individual users.

[0603] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0604] In this invention, the server includes means for acquiring weather information, input means for inputting user health information, and storage means for storing the weather information and health information. This enables health prediction based on the relationship between weather conditions and health status, and the recommendation of appropriate products.

[0605] "Means of obtaining weather information" refers to a function that collects current and predicted weather data from external weather data provision services via the internet.

[0606] "An input method for entering user health information" refers to an interface that allows users to input information about their health status and any health problems they may be experiencing.

[0607] A "storage device" is a device that stores collected weather information and user health information in a database, forming a foundation for data analysis.

[0608] A "knowledge processing tool" analyzes the relationship between weather conditions and health status based on accumulated data and generates a predictive model using a generative artificial intelligence model.

[0609] A "predictive structure" is a system that uses a model generated by a knowledge processing tool to predict the likelihood of health deterioration based on future weather conditions.

[0610] "Information transmission means" refers to a communication function that notifies the user of prediction results and provides appropriate countermeasures.

[0611] A "product recommendation method" is a system that suggests products suitable for a predicted deterioration in health to the user, thereby supporting health management.

[0612] The system for realizing this invention mainly consists of a server, terminals, and users. The server is responsible for periodically acquiring weather information from a weather data provision service via the internet. This includes a software module for acquiring weather data using an external API.

[0613] Users can input their health information through their device. This device refers to mobile devices such as smartphones and tablets, and provides an input interface through a dedicated application. This application utilizes front-end technology with excellent user interface design, making it intuitive for users to operate.

[0614] The server stores collected weather data and health information in a database and uses it for knowledge processing. Database management uses a configuration that allows for efficient data storage and retrieval, such as an SQL database.

[0615] The server further analyzes the accumulated data using generative artificial intelligence models. This includes machine learning techniques using the Python library scikit-learn. This analyzes the correlation between weather conditions and health status and generates a predictive model. The predictive structure can accurately predict health deterioration based on future weather conditions.

[0616] Based on the forecast results, the server sends notifications to users through information transmission methods. These include push notifications and email services, ensuring users receive information quickly. Specifically, it warns about the possibility of health deterioration due to weather changes and suggests countermeasures.

[0617] Furthermore, the system also suggests products that can help users maintain their health through product recommendation mechanisms. This incorporates recommendation algorithms based on past purchase history and current health status. For example, it might send a message such as, "We predict a possibility of headaches due to the drop in atmospheric pressure tomorrow. Please consider headache medication A, which you have purchased in the past."

[0618] An example of a prompt message might be: "Predict what symptoms this user is likely to experience based on the weather conditions, and suggest products that can address these issues based on past data."

[0619] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0620] Step 1:

[0621] The server obtains weather information from external weather data providers via the internet. It uses an API as input to collect current and forecast weather data. This data is received in JSON format, and temperature and pressure information is extracted and stored in a database.

[0622] Step 2:

[0623] Users input health information through the terminal's interface. This input includes health data such as symptom type, severity, and date of onset, which is then manually entered using a GUI. The input data is sent from the terminal to the server. The server receives this information and stores it in individual user profiles.

[0624] Step 3:

[0625] The server trains a generative artificial intelligence model using accumulated weather and health data. Historical weather and health data are retrieved from the database as input, and the data is analyzed using the scikit-learn library. Data calculations, such as regression analysis and classification, are performed to learn the relationship between weather conditions and health status. A predictive model is then generated based on these learning results.

[0626] Step 4:

[0627] The server uses a predictive model to forecast the likelihood of health deterioration based on future weather conditions. The latest weather data is used as input and analyzed by the predictive model. The output provides a prediction of the impact of specific weather conditions on the user's health. For example, it can determine the likelihood of a headache due to a drop in atmospheric pressure on a particular day.

[0628] Step 5:

[0629] The server sends a notification to the user based on the prediction results. It references the prediction results as input and generates a notification message. It then sends the message to the user's device via push notification or email using an information transmission method. For example, the notification might say, "The atmospheric pressure will drop the day after tomorrow, which may cause headaches. Please consider taking preventative measures."

[0630] Step 6:

[0631] The server recommends products suitable for the predicted deterioration of the user's health. It uses a product recommendation algorithm by matching the user's past purchase history and current health information as input. The output is a list of appropriate products. Specifically, it notifies the user with suggestions such as, "Please reconsider headache medicine A, which you previously purchased."

[0632] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0633] The weather-related pain prediction system, which incorporates the emotion engine of the present invention, is realized through the interaction of a server, a terminal, and a user. The system is started by the server periodically acquiring weather information from an external weather data provision service and storing it in a database. User health status data is collected through the terminal interface. This interface provides a form for the user to input information about their health status, physical ailments, and emotions.

[0634] The health and emotional data entered by the user is sent to the server and stored in the corresponding profile in the database. The server feeds this data to a generative artificial intelligence system, which, along with weather data, analyzes the correlations between different weather conditions and health and emotional states. In this analysis, the emotion engine plays a crucial role, generating predictive models that take the user's emotional state into account, thereby more accurately evaluating the factors influencing individual users.

[0635] As a concrete example, suppose the server learns that user B experiences headaches in relation to a sudden drop in atmospheric pressure and has learned emotional data. Based on this information, the prediction system predicts the risk that future weather conditions will cause similar symptoms. Furthermore, if the emotional data is also associated with increased stress or anxiety, the alert system will suggest countermeasures to the user to mitigate that risk.

[0636] The emotion engine in this system analyzes acquired emotional data in real time, and the artificial intelligence contributes to making predictions that take into account the impact of emotional changes on physical condition. For example, if there is data that user C tends to feel anxious during low atmospheric pressure, it is possible to use this to notify the user in advance of appropriate preventative measures. In this way, the system can improve the overall accuracy of predicting physical ailments and provide users with specific and useful information.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The server periodically retrieves weather data from an external weather data provider. This data includes information such as temperature, atmospheric pressure, humidity, and wind speed. This data is stored in a database.

[0640] Step 2:

[0641] The device displays an interface for the user to report their health and emotional state. The user enters any symptoms of illness they experienced on a specific date and time, their severity, and their emotions at that time (e.g., stress, joy, anxiety).

[0642] Step 3:

[0643] The data entered by the user is sent from the terminal to the server. The server organizes this information for each user and stores it in a database. This allows for a detailed record of the user's health and emotional history.

[0644] Step 4:

[0645] The server uses generative artificial intelligence to analyze weather data, health status data, and emotional data stored in the database. This analysis learns how specific weather conditions affect health and emotional states.

[0646] Step 5:

[0647] The server uses a pre-trained model to predict the occurrence of physical ailments and emotional states based on future weather data. This prediction is personalized for each user, based on their past emotional and health data.

[0648] Step 6:

[0649] Based on the prediction results, the server sends a notification to the user's device using an alert sending mechanism. The notification may include suggestions for countermeasures regarding the risk of physical ailments or emotional issues.

[0650] Step 7:

[0651] Users receive notifications on their devices and, based on the content of those notifications, take specific measures such as preparing preventative medication or planning actions to reduce stress.

[0652] (Example 2)

[0653] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] Conventional health prediction systems have been unable to adequately consider the impact of weather conditions on health status, making it difficult to provide highly accurate predictions that reflect the user's emotional state. As a result, there is a problem in that they cannot appropriately predict health problems caused by weather changes and provide countermeasures.

[0655] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0656] In this invention, the server includes information gathering means for acquiring and periodically storing weather information, an input device for the user to input their health and emotional state, and storage means for storing the weather information and health state data. This makes it possible to analyze the correlation between different weather conditions and health and emotional states, predict changes in physical condition with high accuracy using a predictive model that takes the user's emotional state into account, and propose specific countermeasures to the user.

[0657] "Weather information" refers to data related to weather conditions, such as temperature, humidity, and atmospheric pressure.

[0658] "Information gathering means" refers to a function that uses external data provision services to periodically acquire and store weather information.

[0659] An "input device" is an interface that a user uses to input information about their health and emotional state into a system.

[0660] The "memory means" refers to a database function for storing collected weather information and data on the user's health and emotional state.

[0661] "Data analysis means" refers to a function that uses stored data to analyze the correlation between different weather conditions and health and emotional states.

[0662] A "predictive model" is a computational model used to predict changes in a user's physical condition, taking into account the relationship between the user's health status, weather conditions, and emotional state.

[0663] A "notification method" is a function that suggests specific measures to mitigate risks to the user based on predicted changes in their physical condition.

[0664] The system of this invention is realized through the interaction of a server, a terminal, and a user. The server periodically acquires weather information from an external data provision service and stores it in its own database. A method via an API is often used to acquire weather information. As a specific example, a general weather API service that provides information such as temperature, humidity, and atmospheric pressure is used.

[0665] Users input their health and emotional states through a device. The device is equipped with a dedicated application and web interface, allowing users to easily input data. The entered data is then transmitted from the device to a server. The input device features an intuitive and user-friendly UI, enabling efficient data entry.

[0666] The server uses the transmitted health status and emotion data to analyze its correlation with weather information. Generative AI models are used for the analysis to generate predictive models that take into account the user's emotional state. Machine learning algorithms are used as the generative AI models. Specifically, deep learning frameworks such as TensorFlow and PyTorch are sometimes used.

[0667] Based on the analysis results, the server predicts changes in the user's physical condition and notifies the terminal. The notification then suggests specific measures to mitigate the predicted risks. This notification is delivered in real time, supporting the user in immediately implementing the countermeasures.

[0668] As a concrete example, the following prompt statement may be used as input to a generating AI model: "There is data showing that user C tends to become anxious during low-pressure periods. When the next low-pressure period is expected, what precautionary measures should be notified to user C?"

[0669] In this way, the system of the present invention provides highly accurate health predictions based on weather information and individual health and emotional data, and enables the suggestion of measures optimized for each individual user.

[0670] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0671] Step 1:

[0672] The server obtains the latest weather information from an external weather data provider. It uses an API to retrieve data such as temperature, humidity, and atmospheric pressure. Parameters for the API call (such as geographical area and time) are used as input, and a set of weather data is obtained as output. This data is stored in a database on the server.

[0673] Step 2:

[0674] Users input their health and emotional states through a dedicated application on their device or a web interface. The input data includes health conditions (e.g., headache, fatigue) and emotional states (e.g., stress, anxiety). To maintain input consistency, the device validates the data and formats it appropriately as needed. The output is formatted health data.

[0675] Step 3:

[0676] The device transmits health and emotional state data collected from the user to the server. A secure protocol is used for this communication, and the data is delivered to the server in real time. Formatted user data is used as input, and the output is provided in a format that can be stored on the server.

[0677] Step 4:

[0678] The server feeds a generative AI model with user health data, sentiment data, and weather data stored in a database. Using these datasets as input, the generative AI model applies machine learning algorithms to analyze correlations. As output, a user-specific predictive model is constructed. This model is used to understand weather conditions that may affect an individual user's health.

[0679] Step 5:

[0680] The server predicts the user's health status under future weather conditions based on the generated prediction model. Specifically, it uses current weather conditions and the user's profile as input and calculates the user's specific risk of health changes as output. This allows for specific countermeasures to be taken for each individual user based on the predicted risk.

[0681] Step 6:

[0682] The server notifies the user based on the forecast. The notification system sends alerts to the user's device and suggests countermeasures. It uses the predicted risks and suggested countermeasures as input and provides instructions and advice that the user can immediately implement as output. This allows users to manage their health and mitigate health risks caused by weather changes.

[0683] (Application Example 2)

[0684] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] Existing security systems struggle to predict user emotional and health conditions, making it particularly difficult to provide accurate countermeasures against health problems and security risks influenced by weather changes. There is a need for a system that addresses this challenge and comprehensively manages user health and security.

[0686] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0687] In this invention, the server includes data collection means for acquiring weather information, information input means for inputting the user's health information and emotional state, and information storage means for accumulating the weather information, health information, and emotional state. This enables risk assessment based on the user's emotional state and health information, and prediction of health and security risks associated with weather changes.

[0688] "Means of data collection for obtaining weather information" refers to technology for periodically acquiring weather data from external weather data provision services and making it available for use in the system.

[0689] "Information input means for inputting user health information and emotional state" refers to technology that provides an interface for users to input their own health status and emotional changes, and collects that data.

[0690] "Information storage means for accumulating weather information, health information, and emotional state" refers to a database system that efficiently stores acquired weather information, health information, and emotional state, and makes them accessible as needed.

[0691] "Generative intelligence analysis means" refers to artificial intelligence technology that analyzes the correlation between weather changes and accumulated weather information, health information, and emotional state data.

[0692] A "risk assessment tool" is a technology that uses correlation analysis results elucidated by generative intelligence analysis tools to predict health problems and security risks, and to take appropriate action based on those results.

[0693] "Information transmission means" refers to technology that notifies users of issues predicted by risk assessment means and provides necessary countermeasures.

[0694] This system performs risk assessments that take into account the user's emotions and health status in security situations. The server periodically collects weather data using a weather data provision service (e.g., OpenWeatherMap) to obtain weather information. The terminal provides a form for the user to input their emotions and health status via smart glasses or a head-mounted display, and transmits this information to the server.

[0695] The server has a means of storing information, including a database (specifically, MongoDB, etc.) that efficiently stores acquired weather data and user health and emotional state data. Based on this stored data, the server uses generative intelligence analysis to analyze the correlations between the stored data. This technology utilizes machine learning frameworks such as TensorFlow and PyTorch to build a model that evaluates the impact of weather changes on user health and emotions.

[0696] Furthermore, the risk assessment system includes a means of transmitting information that predicts risks related to the user's health and security status based on the analyzed data and notifies the user of these risks. This notification includes specific countermeasures so that preventative measures can be taken in advance. For example, if weather changes that could cause stress to security personnel are predicted for the night, the display will show a warning and a suggestion of relaxing music.

[0697] Examples of specific prompt messages are as follows:

[0698] "Based on weather data and user sentiment data, predict the stress risk during security patrols and propose appropriate countermeasures."

[0699] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0700] Step 1:

[0701] The device receives emotional and health data from the user as input using smart glasses or a head-mounted display. This includes text and voice input via the wearable device's interface. The input data is converted into a digital format and sent to the server via a security token.

[0702] Step 2:

[0703] The server periodically retrieves weather information for a specific region from an external weather data provider. The weather data (temperature, humidity, atmospheric pressure, etc.) obtained via the API is stored in a database. MongoDB is used for the database, ensuring efficient data storage and management.

[0704] Step 3:

[0705] The server's generative intelligence analysis system uses accumulated health information, emotional states, and weather data as input and analyzes correlations using a machine learning model. A model built with TensorFlow evaluates how emotional states are affected by weather changes. As a result, a correlation matrix is ​​output.

[0706] Step 4:

[0707] The server risk assessment method predicts health and security risks based on the analysis results. Using a correlation matrix as input, it predicts the risk level in future situations. A risk score is calculated as the prediction result.

[0708] Step 5:

[0709] The server notifies the user of the predicted risk score based on the information transmission method. The notification to the user includes a message that contains specific countermeasures. The message is transmitted as a digital signal to the terminal's display and is displayed visually to the user.

[0710] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0711] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0712] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0713] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0714] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0715] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0716] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0717] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0718] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0719] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0720] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0721] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0722] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0723] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0724] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0725] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0726] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0727] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0728] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0729] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0730] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0731] The following is further disclosed regarding the embodiments described above.

[0732] (Claim 1)

[0733] Meteorological data collection methods,

[0734] An interface means for inputting user health status data,

[0735] A database means for storing the aforementioned weather data and the aforementioned health status data,

[0736] A generative artificial intelligence means for analyzing the correlation between weather changes and health status based on the aforementioned database,

[0737] A prediction means for predicting the occurrence of poor health based on the aforementioned correlation analysis,

[0738] An alert sending means that notifies the user based on the prediction results,

[0739] A system that includes this.

[0740] (Claim 2)

[0741] The system according to claim 1, wherein the generative artificial intelligence means is periodically retrained using a machine learning algorithm.

[0742] (Claim 3)

[0743] The system according to claim 1, wherein the alert transmission means provides an alert that includes specific countermeasures to be taken when a predicted illness occurs.

[0744] "Example 1"

[0745] (Claim 1)

[0746] Means of obtaining weather information,

[0747] A device for inputting information about the user's health status,

[0748] A storage device for recording the aforementioned weather information and the aforementioned health status information,

[0749] Based on the aforementioned storage device, a generative artificial intelligence device analyzes the relationship between weather changes and health conditions,

[0750] Based on the aforementioned analysis results, a device for predicting physical ailments,

[0751] A device for sending warnings to the user based on prediction results,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, wherein the generative artificial intelligence device is periodically trained using a learning algorithm.

[0755] (Claim 3)

[0756] The system according to claim 1, wherein the warning transmitting device provides a warning that includes specific measures to be taken when an expected physical ailment occurs.

[0757] "Application Example 1"

[0758] (Claim 1)

[0759] Means of obtaining weather information,

[0760] An input method for entering user health information,

[0761] A storage means for storing the aforementioned weather information and the aforementioned health information,

[0762] A knowledge processing means for analyzing the relationship between weather conditions and health status based on the aforementioned storage means,

[0763] A predictive structure that predicts a deterioration in physical condition based on the aforementioned analysis results,

[0764] Information transmission means for transmitting information to the user based on the prediction results,

[0765] A product recommendation means that recommends appropriate products based on the aforementioned prediction results,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, wherein the knowledge processing means is periodically retrained using a machine learning method.

[0769] (Claim 3)

[0770] The system according to claim 1, wherein the information transmission means provides information including specific countermeasures for when health deterioration is predicted, and further proposes target products.

[0771] "Example 2 of combining an emotion engine"

[0772] (Claim 1)

[0773] A means of collecting information to acquire and periodically store weather information,

[0774] An input device for users to input their health status and emotional state,

[0775] A storage means for storing the aforementioned weather information and the aforementioned health status data,

[0776] A data analysis means for analyzing the correlation between different weather conditions and health and emotional states based on the aforementioned memory means,

[0777] A predictive means that generates a predictive model that takes into account the user's emotional state and predicts changes in physical condition,

[0778] A notification mechanism that proposes specific countermeasures to the user based on predicted risks,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, wherein the data analysis means is periodically retrained using a machine learning algorithm.

[0782] (Claim 3)

[0783] The system according to claim 1, wherein the notification means provides a notification including countermeasures to mitigate the risk of changes in physical condition.

[0784] "Application example 2 when combining with an emotional engine"

[0785] (Claim 1)

[0786] A means of collecting data to obtain weather information,

[0787] Information input means for inputting user health information and emotional state,

[0788] Information storage means for storing the aforementioned weather information and the aforementioned health information and emotional state,

[0789] A generative intelligence analysis means for analyzing the correlation between weather changes, health information, and emotional states based on the aforementioned information storage means,

[0790] A risk assessment means that predicts the occurrence of poor health and security risks based on the aforementioned correlation analysis,

[0791] Information transmission means for notifying the user based on the prediction results,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, wherein the generative intelligence analysis means is periodically trained using a learning algorithm.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the information transmission means provides notifications including specific countermeasures in the event of anticipated health problems and security risks. [Explanation of symbols]

[0797] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Meteorological data collection methods, An interface means for inputting user health status data, A database means for storing the aforementioned weather data and the aforementioned health status data, A generative artificial intelligence means for analyzing the correlation between weather changes and health status based on the aforementioned database, A prediction means for predicting the occurrence of poor health based on the aforementioned correlation analysis, An alert sending means that notifies the user based on the prediction results, A system that includes this.

2. The system according to claim 1, wherein the generative artificial intelligence means is periodically retrained using a machine learning algorithm.

3. The system according to claim 1, wherein the alert transmission means provides an alert that includes specific countermeasures to be taken when a predicted illness occurs.

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