system

Abstract

We provide the system. [Solution] A means of collecting meteorological data using a high-altitude platform, A means of storing collected weather data in a database via communication means, A method using a generative model that analyzes stored data and performs weather forecasting, A means of notifying users of prediction results and receiving feedback, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, with climate change, the frequency of local heavy rainfall and linear precipitation bands has been increasing, and thus more accurate weather prediction is important. It is difficult for conventional meteorological satellites and ground radars to accurately predict local weather changes in a short time, which has emerged as a problem in disaster prevention measures. In particular, improving the prediction accuracy of linear precipitation bands is an essential technical issue for preventing local heavy rainfall disasters.

Means for Solving the Problems

[0005] This invention provides a means for collecting highly accurate weather data by utilizing a high-altitude platform to perform weather observations in real time. The collected data is stored in a database via communication means and analyzed by a generative model using machine learning algorithms. This allows for the prior identification of areas with a high risk of rainfall and the implementation of highly accurate weather forecasts. Furthermore, the forecast results are notified to the user, and the system provides a mechanism for continuous improvement of the model through feedback.

[0006] A "high-altitude platform" is a device such as an aircraft, balloon, or drone that flies or hovers in the stratosphere and is equipped with instruments to observe the conditions of the Earth's surface and the atmosphere.

[0007] "Meteorological data" refers to information used to measure weather phenomena, such as temperature, humidity, atmospheric pressure, wind speed, wind direction, and precipitation.

[0008] "Communication means" refers to the technologies and equipment used to send and receive data via wireless or wired communication networks.

[0009] A "database" is a system for storing collected data in an organized structure and managing it so that it can be efficiently searched and retrieved.

[0010] A "generative model" is a computer program that uses machine learning algorithms to generate specific patterns or predictions from new data.

[0011] A "machine learning algorithm" is a mathematical method or computational process used to analyze large amounts of data, identify hidden patterns, and make predictions about the future.

[0012] "Rainfall risk assessment" is the process of analyzing and predicting the likelihood and impact of rainfall in a specific area based on collected meteorological data.

[0013] A "user" is someone who utilizes this weather forecasting system to receive information or provide feedback. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a weather forecasting system that utilizes a high-altitude platform for collecting high-precision weather data and a generative model for analyzing that data, with the aim of improving the accuracy of disaster prediction. This system mainly consists of three main elements: a server, terminals, and users.

[0036] The server plays a central role in this system. First, it receives weather data collected in real time and stores it in a database. Next, it runs a generative model to analyze the stored data. This generative model is based on machine learning algorithms and predicts weather conditions several hours in advance based on the vast amount of data collected. The server also has the function of generating warnings based on the prediction results and automatically sending notifications to users.

[0037] The terminal controls the high-altitude platform and relays data transmission. Control software running on the terminal moves the high-altitude platform to a designated weather observation point, from which it measures detailed weather data. The observation data is transmitted to the server via the terminal. This transmission process uses secure and stable communication methods and verifies data integrity to ensure accurate data reaches the server.

[0038] Users can receive weather forecast information provided by the server and take appropriate action based on it. User feedback is also sent to the server and used to improve the generative model's algorithm. This allows the system to continuously improve its forecasting accuracy.

[0039] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and begins collecting weather data. A generative model then analyzes this data to predict changes in wind speed and precipitation several hours in advance. Based on the highly accurate predicted information, users can take prompt action to evacuate or prepare, thereby minimizing damage. Thus, this invention combines weather data collection using a high-altitude platform with analysis by a generative model to enable highly accurate and rapid weather forecasting.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects real-time data from the Japan Meteorological Agency and other weather organizations and determines where to begin analysis. It identifies high-risk areas and generates instructions to move high-altitude platforms to those areas.

[0043] Step 2:

[0044] The terminal receives instructions from the server on the high-altitude platform and controls its movement to precisely reach the designated area. It uses GPS to determine its current location and runs a control program to plan the route to its destination.

[0045] Step 3:

[0046] The device activates its built-in weather sensors at the observation point it reaches, measuring real-time temperature, humidity, atmospheric pressure, wind speed, precipitation, and other data. The observation data is temporarily stored within the device, and data integrity is checked.

[0047] Step 4:

[0048] The terminal compresses data that has been verified for integrity and sends it to the server using a secure communication protocol. During transmission, it provides a retransmission function to protect against data loss or errors.

[0049] Step 5:

[0050] The server saves the received data to a database. The saved data is then input into a generative model to perform weather forecasting. The generative model uses a specified algorithm based on machine learning algorithms to perform the analysis.

[0051] Step 6:

[0052] The server analyzes the generated prediction results and generates alerts as needed. If certain criteria or thresholds are exceeded, it automatically creates an alert and takes action to notify relevant parties.

[0053] Step 7:

[0054] Users receive prediction results and warning information from the server and prepare necessary disaster prevention measures. Furthermore, they send feedback on the prediction results to the server, contributing to improving the model's accuracy.

[0055] Step 8:

[0056] The server analyzes user feedback and uses it to optimize the parameters and algorithms of the generative model. This improves real-time prediction accuracy.

[0057] (Example 1)

[0058] 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."

[0059] Improving the accuracy of environmental forecasts requires advanced data collection and analysis. However, conventional technologies have faced challenges in terms of data collection accuracy and timely analysis, making it difficult to respond quickly to environmental changes. Furthermore, there has been a lack of effective means to utilize user feedback to improve forecast accuracy.

[0060] 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.

[0061] In this invention, the server includes means for collecting environmental data using a high-altitude platform, means for storing the collected data in a storage device via communication means, and means for analyzing the stored data using a generative model. This enables rapid and accurate environmental forecasting.

[0062] A "high-altitude platform" is a mobile base for collecting data in high-altitude airspace using devices such as balloons and drones.

[0063] "Environmental data" refers to information related to weather and the natural environment in a specific region, such as temperature, humidity, wind speed, and rainfall.

[0064] "Communication methods" refer to networks and protocols used to transmit collected data, such as wireless communication and data transfer technologies using the Internet.

[0065] A "storage device" refers to a physical or virtual data storage structure used by a server to store data for extended periods, such as a database or cloud storage.

[0066] A "generative model" is a computational model that analyzes large amounts of data based on machine learning algorithms to generate predictions and new insights.

[0067] A "user" is an individual or group that can receive predicted information and act based on it.

[0068] "Feedback" refers to evaluations and opinions from users regarding the information and services provided, and this information is used to improve the system.

[0069] A "detector" is a sensor device mounted on a high-altitude platform to acquire environmental data.

[0070] A "machine learning algorithm" is a set of computational methods that analyze large amounts of data, allowing a model to automatically learn patterns and rules, and then perform predictions and classifications.

[0071] An "alert" is a notification or warning that automatically alerts users when a specific risk is predicted.

[0072] This invention aims to collect environmental data using a high-altitude platform and make predictions based on that data using a generative model. The system mainly consists of three elements: a server, a terminal, and a user.

[0073] The server plays a central role in the system. It receives real-time environmental data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed using a generative model based on machine learning algorithms. This generative model has the ability to predict future weather conditions based on the environmental data, generates warnings based on the analysis results, and automatically notifies the user.

[0074] The terminal is responsible for controlling the high-altitude platform and transmitting environmental data to the server. Control software installed on the terminal moves the high-altitude platform to a designated observation position and uses sensors to acquire detailed environmental data. The terminal transmits the data to the server using a secure and stable communication method, and the data integrity is verified during the transmission process to ensure that accurate information reaches the server.

[0075] Users can receive predicted information provided by the server and take appropriate action. For example, they can take steps such as evacuating or preparing quickly based on predicted heavy rainfall information. They can also send feedback to the server, contributing to the improvement of the generative model's algorithm. This feedback process allows the system to continuously improve its prediction accuracy.

[0076] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and collects environmental data. A generative model then analyzes this data and predicts changes in wind speed and rainfall several hours in advance. Based on this, users can take early evacuation and necessary disaster prevention measures, thereby minimizing damage.

[0077] An example of a prompt message is, "Design a generative model to predict weather conditions based on highly accurate environmental data." This method enables rapid and accurate environmental forecasting.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The terminal moves the high-altitude platform to the designated observation point. Control software moves the high-altitude platform to reach the set position. This movement determines the observable range. It receives the coordinate information of the designated observation point as input and generates a confirmation signal upon completion of the movement as output.

[0081] Step 2:

[0082] The terminal acquires environmental data at the observation site. Sensors mounted on the high-altitude platform collect data such as temperature, humidity, wind speed, and precipitation. This data is acquired as initial analog signals and then digitized. It receives analog signals from sensors as input and generates digitized environmental data as output.

[0083] Step 3:

[0084] The terminal sends the collected environmental data to the server. The communication module uses a secure protocol to transmit the data while maintaining its integrity. During the communication process, algorithms are applied to detect and correct data errors. It receives digitized environmental data as input and generates a notification that data transmission to the server is complete as output.

[0085] Step 4:

[0086] The server stores the received environmental data in a database. The storage process verifies the accuracy of the data and adds timestamp information. This ensures the data is stored in a searchable state. It receives environmental data transmitted from the terminal as input and generates a confirmation signal upon completion of data storage as output.

[0087] Step 5:

[0088] The server performs generative model analysis using stored data. Machine learning algorithms predict future environmental conditions by referring to past data. During this process, anomalies are detected and corrected, generating highly accurate prediction results. It receives environmental data obtained from a database as input and generates prediction results and warning information as output.

[0089] Step 6:

[0090] The server notifies the user of the generated prediction results. The notification system utilizes alarm and alert information to send messages to the user's device. This allows the user to respond quickly. It receives prediction results from a generative model as input and generates a notification completion signal to the user's device as output.

[0091] Step 7:

[0092] Users take action based on information provided by the server. They can implement safety measures and adjust schedules based on predictive information and warnings. They also send feedback to the server regarding the accuracy of the predictive information. The system receives notification information from the server as input and generates a signal to send feedback results to the server as output.

[0093] Step 8:

[0094] The server improves the generative model based on user feedback. It adjusts the algorithm parameters as needed to improve prediction accuracy. This enables continuous system improvement. It receives user feedback as input and generates an improved generative model as output.

[0095] (Application Example 1)

[0096] 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."

[0097] Sudden changes in weather can cause delays and compromise safety in logistics deliveries, significantly impacting customer satisfaction in food delivery services. However, conventional weather information systems often struggle to provide real-time information and enable quick decision-making, hindering efficient deliveries. This presents challenges in optimizing deliveries and ensuring safety.

[0098] 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.

[0099] In this invention, the server includes means for collecting weather data using a high-altitude platform, means for storing the collected weather data in an information storage device via communication means, means for analyzing the stored data and using a generative model to perform weather forecasting, means for optimizing travel routes based on weather forecast information, and means for automatically adjusting affected routes in real time. This enables the planning and implementation of rapid and safe delivery plans based on weather forecast information in food delivery.

[0100] A "high-altitude platform" refers to devices such as flying vehicles or floating objects used to acquire weather data in the atmosphere.

[0101] "Weather data" refers to observational information necessary to understand weather conditions, such as temperature, humidity, wind speed, and precipitation.

[0102] "Communication means" refers to methods and technologies for transmitting collected data to information storage devices located in remote locations.

[0103] An "information storage device" refers to a device or system that securely and reliably stores data received via communication means.

[0104] A "generative model" refers to a program or system that uses machine learning algorithms to analyze weather data and predict future weather patterns.

[0105] "Optimizing travel routes" refers to the process of determining the safest and most efficient route based on current weather conditions and forecast weather.

[0106] "Automatic adjustment" refers to a function or process that modifies plans and schedules in real time in response to changes in weather or current conditions.

[0107] "Delivery process" refers to the entire process or flow of logistics, including the logistics steps from the origin of the goods to their destination.

[0108] To realize this invention, a system combining three elements—a server, a terminal, and a user—is required. The server is implemented using Python and the Django framework and has the function of receiving weather data from a high-altitude platform in real time and storing it in an information storage device. The server uses a machine learning algorithm based on TENSORFLOW® to take in the vast amount of collected weather data and performs detailed weather forecasts using a generative model. This forecast information is delivered to the terminal via an API.

[0109] The terminal controls a high-altitude platform equipped with sensors and collects data at designated weather observation points. This data is securely and accurately transmitted to a server, helping to understand weather conditions in real time.

[0110] Users receive weather forecast information from a server via their smartphone or other digital device. Based on this information, delivery routes can be optimized, and drivers can be instructed to move at the appropriate time. For example, food delivery companies can plan safe routes in advance for predicted bad weather, enabling them to deliver safely and efficiently.

[0111] For example, a delivery driver could receive a warning about an approaching storm via the application and pre-set a safe detour route, minimizing delays and enabling faster delivery to customers. An example of a prompt to implement such a use case would be: "Generate optimized delivery routes and recommended alternative routes based on the storm warning for the Tokyo area for the next 24 hours."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The terminal controls a high-altitude platform and performs sensing at designated weather observation points. Inputs are the platform's location information and the designated observation points, while output is the observed weather data. Specifically, it uses sensors to measure data such as temperature, humidity, and wind speed, and transmits this information to a server in real time.

[0115] Step 2:

[0116] The server stores weather data received from terminals in a database. The input is real-time weather data sent from terminals, and the output is structured data stored in the database. At this stage, the server checks the integrity of the data and cleans it to ensure there are no invalid or missing values.

[0117] Step 3:

[0118] The server uses a generative AI model to analyze stored weather data. The input is the contents of the accumulated weather database, and the output is detailed weather forecast information for several hours ahead. Specifically, TensorFlow is used to train the data with a machine learning algorithm and predict weather changes for the next 24 hours.

[0119] Step 4:

[0120] The server notifies food delivery drivers of predicted weather information. The input is weather forecast information obtained from a generating AI model, and the output is warnings and recommended routes displayed on the smartphone app used by the delivery drivers. Here, warnings are generated in real time, and safe delivery routes are suggested. An example of a prompt message is: "Generate optimized delivery routes and recommended alternative routes as a storm warning for the Tokyo area for the next 24 hours."

[0121] Step 5:

[0122] The user adjusts delivery schedules and routes appropriately based on the provided weather forecast information. Input is weather forecasts and warnings received from the server, and output is a real-time updated delivery route and schedule. Specifically, the user selects a recommended safe route within the app based on the received information and performs delivery tasks efficiently.

[0123] 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.

[0124] This invention is a system that combines a weather data collection device and an emotion engine, with the aim of improving the accuracy of weather forecasts and optimizing the user experience. This system consists of a server, terminals, and users, each of which works in cooperation with one another.

[0125] The server plays a central role in this system. It receives weather data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed by a generative model using machine learning algorithms, and weather forecasts are made. Based on these forecast results, the server automatically sends notifications and warnings to users. Furthermore, the system incorporates an emotion engine that analyzes user feedback and evaluates the user's emotional state. This makes it possible to optimize the forecast results and warning content according to the user's emotions.

[0126] The terminal controls the high-altitude platform and issues movement instructions to a designated area. The platform measures detailed weather data on-site and transmits that data to the server via the terminal. The terminal is responsible for checking the accuracy of the data and transmitting it using highly secure communication methods.

[0127] Users receive weather forecasts and warnings sent from the server and take disaster prevention measures as needed. Users also provide feedback to the system through an emotion engine. This feedback includes the user's understanding of and satisfaction with the weather information, as well as their reaction to warnings. This information is analyzed by the server and used to improve the generative model.

[0128] As a concrete example, after a heavy rain forecast is generated by a generative model, the server notifies the user of the results. At this time, the emotion engine analyzes the user's reaction, and if, for example, the user expresses concern about the forecast, it adjusts the system to provide more careful and detailed information next time. This improves the user experience and enables even more accurate weather forecasting.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server collects the latest weather information from sources such as the Japan Meteorological Agency to analyze weather data in high-risk areas. Next, it plans the deployment of high-altitude platforms in specific areas.

[0132] Step 2:

[0133] The terminal sends a movement instruction to the high-altitude platform, which controls the platform to reach the designated area. The platform continuously checks its current location via GPS on its way to the destination.

[0134] Step 3:

[0135] Once the platform reaches its destination, the terminal activates its weather sensors and begins measuring data. This includes real-time measurements of temperature, humidity, atmospheric pressure, wind speed, and precipitation.

[0136] Step 4:

[0137] The device temporarily stores the collected data and verifies its integrity and quality. After verification, the data is sent to the server using a communication method.

[0138] Step 5:

[0139] The server stores the received weather data in a database and inputs it into a generative model. A machine learning algorithm performs weather analysis and generates a weather forecast for several hours in advance.

[0140] Step 6:

[0141] The server evaluates weather risks, such as rainfall, based on the generated forecast data. Based on the evaluation results, it determines the content of the alerts to be sent to the user and generates notifications.

[0142] Step 7:

[0143] Users receive notifications from the server and consider disaster response measures as needed. For example, they can check their schedules and take actions to ensure their safety.

[0144] Step 8:

[0145] Users send feedback on weather forecasts and warnings to the server via an emotion engine. This feedback includes emotions such as satisfaction with the forecast and concerns.

[0146] Step 9:

[0147] The server analyzes user feedback using an emotion engine and evaluates the user's emotions. This information is then used to optimize the generative model and improve the method of future notifications.

[0148] (Example 2)

[0149] 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".

[0150] Current weather forecasting systems struggle to provide users with accurate and timely forecast information, and they lack optimization of information based on user sentiment and feedback. Therefore, improving forecast accuracy and optimizing the user experience are key challenges.

[0151] 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.

[0152] In this invention, the server includes means for collecting weather data using an atmospheric mobile device, means for storing the data in a data set using information transmission means, and means for analyzing user opinions using an emotion analysis engine and optimizing notification content. This enables highly accurate weather forecasting and improved user experience.

[0153] An "atmospheric mobile device" is a device that has the function of collecting weather data while moving within the Earth's atmosphere.

[0154] "Meteorological data" refers to numerical information about the atmosphere, such as temperature, humidity, wind speed, and precipitation, and is fundamental data used for weather forecasting.

[0155] "Information transmission means" refers to technologies and protocols for securely transmitting acquired data to a server or other system.

[0156] A "data set" refers to a collection of various data formats stored on a server, and is a set of fundamental data used for analysis.

[0157] A "generative algorithm" is a method that uses machine learning and artificial intelligence technologies to analyze weather data and predict future weather patterns.

[0158] "User" refers to an individual or organization that receives weather forecasts and warning information provided by the system.

[0159] "Opinions" refers to feedback and comments provided by users, including evaluations of the information provided by the system and suggestions for improvement.

[0160] An "emotion analysis engine" is a technology that analyzes user opinions from an emotional perspective and uses that analysis to optimize services and information provision.

[0161] This invention is a system aimed at improving the efficiency of weather forecasting and the user experience. The system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0162] The server plays a central role in this system. The server receives weather data transmitted from terminals and stores it in a database. The stored data is analyzed by advanced machine learning software using generative algorithms. This analysis process allows for the prediction of weather patterns such as precipitation, temperature, and humidity. Using generative AI models, the server provides real-time forecasts and automatically generates warnings and notifications for users.

[0163] Meanwhile, the terminal controls the atmospheric mobile device. This device moves to a designated area and uses sensors to measure detailed weather data. The collected data is securely transmitted to a server using an information transmission system. The terminal also has the function of checking the reliability of the data and filtering out inaccurate data.

[0164] The user is the recipient of predictive information provided by the system. Based on this information, the user can take appropriate actions and provide feedback to the system through a sentiment analysis engine. This feedback includes the accuracy and usefulness of the information, as well as emotional responses.

[0165] For example, heavy rainfall is predicted based on a generative AI model, and the server notifies the user of this information. The user then provides feedback to the system regarding this prediction, which the sentiment analysis engine analyzes to make future notifications more tailored to the user.

[0166] An example of a prompt message would be: "Generate a weather forecast for the specified area this weekend, optimize the notification content to be emotionally sensitive for the user, and create a report that takes user feedback into consideration." This would enable the system to achieve highly accurate predictions and improve user satisfaction.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The terminal controls a high-altitude platform and moves to a designated area. During this process, it uses GPS to determine its precise location and sensors to collect weather data in real time. Input is regional information, and output is weather data such as temperature, humidity, wind speed, and precipitation.

[0170] Step 2:

[0171] The terminal checks the accuracy of the collected weather data. This process executes algorithms to filter out outliers and noise. The input is the collected weather data, and the output is verified, clean data.

[0172] Step 3:

[0173] The terminal transmits weather data to the server using a secure communication method. During this process, the data is encrypted to ensure security. The input is clean weather data, and the output is encrypted data sent to the server.

[0174] Step 4:

[0175] The server receives data sent from the terminal and stores it in the database. The input is encrypted weather data, and the output is the stored data in the database.

[0176] Step 5:

[0177] The server analyzes data using a generative AI model to predict future weather. The input is weather data stored in a database, and the output is the weather forecast result. Machine learning algorithms are applied throughout this process.

[0178] Step 6:

[0179] The server creates a notification message for the user based on the generated weather forecast results. It optimizes the content using prompts. The input is the weather forecast results, and the output is the notification message.

[0180] Step 7:

[0181] Users receive notifications from the server and take action based on them. They also provide feedback to the system, including their opinions and impressions of the prediction results. The input is the notification message, and the output is the user's feedback.

[0182] Step 8:

[0183] The server analyzes user feedback using an emotion analysis engine to optimize the content of future notifications. The input is user feedback, and the output is the adjusted notification settings. This improves the quality of information delivery.

[0184] (Application Example 2)

[0185] 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".

[0186] There is a need to reduce risks and improve service quality by promptly and appropriately notifying both users and service providers of the impact of weather and environmental changes on services and transportation. However, conventional systems have limitations in the accuracy of predictions and the appropriateness of notifications, and they lack optimization of information based on user sentiment and feedback.

[0187] 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.

[0188] In this invention, the server includes means for using a generative model to analyze collected environmental information and perform environmental predictions, means for notifying the user of the prediction results and receiving responses, and means for analyzing the user's responses and adjusting the content of the notifications. This makes it possible to optimize notifications based on the user's emotions and provide more accurate and appropriate information.

[0189] A "high-altitude platform" is a device installed at a high altitude to collect environmental information at a designated location.

[0190] "Environmental information" refers to data related to weather and the surrounding environment, and is a record of specific numerical values ​​and conditions that are the subject of information collection.

[0191] "Communication means" refers to devices or methods used to transmit collected information to recording devices or servers.

[0192] A "recording device" refers to a database or storage device used to store collected information, and is a device that holds information for analysis.

[0193] A "generative model" is a model that includes machine learning algorithms for making predictions based on collected environmental information.

[0194] A "user" is an individual or legal entity that receives weather forecast results or notifications, and is the recipient of the information.

[0195] "Notification" refers to a means of communicating information predicted by a generative model to the user, and includes information visualization and alert functions.

[0196] "Reaction" refers to the response or emotional feedback that a user provides in response to a notification they receive.

[0197] "Means of adjusting notification content" refers to the process or method of changing the content and format of notifications based on user responses and feedback.

[0198] In an embodiment of this invention, the system consists of a server, a terminal, and a user. The server is primarily responsible for information analysis and notification management. The high-altitude platform collects environmental information at various locations using sensors and transmits this information to the terminal using communication means. The terminal receives and temporarily stores the data and transfers the information to the server via the communication line. The terminal also moves the high-altitude platform to a designated location in real time, enabling accurate information collection.

[0199] The server stores the received environmental information in a recording device and then performs information analysis using a generative model incorporating machine learning algorithms. This process yields detailed environmental predictions, which are then notified to the user. The server further utilizes an emotion analysis engine called EmotionEngine to analyze the response data returned by the user and collect feedback information. This allows it to generate optimal notification content tailored to each user's emotions, thereby improving the user experience.

[0200] As a concrete example, applying this system to a food delivery service would allow it to recommend the optimal route to delivery drivers based on predicted rainfall information, and to notify customers in advance of potential delivery delays. Based on user feedback, the delivery time estimates and notification content would be constantly improved. Leveraging the capabilities of the generative AI model, a prompt message could be used to request feedback in the form of, "Please advise on what weather-related information should be prioritized for customer delivery next time."

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The terminal collects environmental information from sensors installed on a high-altitude platform. The sensors measure information such as temperature, humidity, and atmospheric pressure in real time and transmit it to the terminal. As a result, the input to the terminal becomes numerical data representing the physical quantities of the environment.

[0204] Step 2:

[0205] The terminal transfers the collected environmental information to the server via a communication line. During this process, a protocol is used to verify the accuracy and integrity of the data, ensuring secure transmission. The output is the reliable environmental information received by the server.

[0206] Step 3:

[0207] The server stores the received environmental information in a recording device and performs data analysis using a generative model. This generative AI model uses machine learning algorithms to predict weather and environmental conditions. The input is the stored environmental information, and the output is detailed environmental change information as a prediction result.

[0208] Step 4:

[0209] The server notifies the user of the prediction results generated by the generative model. This notification is sent in a format suitable for the user's device and includes optimal route information for delivery drivers and an estimated delivery time for users. The input is the prediction result information, and the output is the notification to the user and delivery driver.

[0210] Step 5:

[0211] Users receive notifications from the server and provide feedback based on the results. This feedback includes evaluations of the notification content and suggestions for improvement. The input is the notification received by the user, and the output is the feedback data.

[0212] Step 6:

[0213] The server uses EmotionEngine to analyze user feedback and evaluate the emotional state. This analysis optimizes the notification content. The input is user feedback, and the output is the improved notification plan.

[0214] Step 7:

[0215] The server adjusts the notification plan for subsequent deliveries based on the analysis, contributing to improved accuracy of the generative model. Feedback such as, "Please advise on how to improve the system by providing prompts such as, 'For the next delivery, what weather-related information should I prioritize communicating to the customer?'" helps to strengthen the model.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention is a weather forecasting system that utilizes a high-altitude platform for collecting high-precision weather data and a generative model for analyzing that data, with the aim of improving the accuracy of disaster prediction. This system mainly consists of three main elements: a server, terminals, and users.

[0233] The server plays a central role in this system. First, it receives weather data collected in real time and stores it in a database. Next, it runs a generative model to analyze the stored data. This generative model is based on machine learning algorithms and predicts weather conditions several hours in advance based on the vast amount of data collected. The server also has the function of generating warnings based on the prediction results and automatically sending notifications to users.

[0234] The terminal controls the high-altitude platform and relays data transmission. Control software running on the terminal moves the high-altitude platform to a designated weather observation point, from which it measures detailed weather data. The observation data is transmitted to the server via the terminal. This transmission process uses secure and stable communication methods and verifies data integrity to ensure accurate data reaches the server.

[0235] Users can receive weather forecast information provided by the server and take appropriate action based on it. User feedback is also sent to the server and used to improve the generative model's algorithm. This allows the system to continuously improve its forecasting accuracy.

[0236] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and begins collecting weather data. A generative model then analyzes this data to predict changes in wind speed and precipitation several hours in advance. Based on the highly accurate predicted information, users can take prompt action to evacuate or prepare, thereby minimizing damage. Thus, this invention combines weather data collection using a high-altitude platform with analysis by a generative model to enable highly accurate and rapid weather forecasting.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server collects real-time data from the Japan Meteorological Agency and other weather organizations and determines where to begin analysis. It identifies high-risk areas and generates instructions to move high-altitude platforms to those areas.

[0240] Step 2:

[0241] The terminal receives instructions from the server on the high-altitude platform and controls its movement to precisely reach the designated area. It uses GPS to determine its current location and runs a control program to plan the route to its destination.

[0242] Step 3:

[0243] The device activates its built-in weather sensors at the observation point it reaches, measuring real-time temperature, humidity, atmospheric pressure, wind speed, precipitation, and other data. The observation data is temporarily stored within the device, and data integrity is checked.

[0244] Step 4:

[0245] The terminal compresses data that has been verified for integrity and sends it to the server using a secure communication protocol. During transmission, it provides a retransmission function to protect against data loss or errors.

[0246] Step 5:

[0247] The server saves the received data to a database. The saved data is then input into a generative model to perform weather forecasting. The generative model uses a specified algorithm based on machine learning algorithms to perform the analysis.

[0248] Step 6:

[0249] The server analyzes the generated prediction results and generates alerts as needed. If certain criteria or thresholds are exceeded, it automatically creates an alert and takes action to notify relevant parties.

[0250] Step 7:

[0251] Users receive prediction results and warning information from the server and prepare necessary disaster prevention measures. Furthermore, they send feedback on the prediction results to the server, contributing to improving the model's accuracy.

[0252] Step 8:

[0253] The server analyzes user feedback and uses it to optimize the parameters and algorithms of the generative model. This improves real-time prediction accuracy.

[0254] (Example 1)

[0255] 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."

[0256] Improving the accuracy of environmental forecasts requires advanced data collection and analysis. However, conventional technologies have faced challenges in terms of data collection accuracy and timely analysis, making it difficult to respond quickly to environmental changes. Furthermore, there has been a lack of effective means to utilize user feedback to improve forecast accuracy.

[0257] 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.

[0258] In this invention, the server includes means for collecting environmental data using a high-altitude platform, means for storing the collected data in a storage device via communication means, and means for analyzing the stored data using a generative model. This enables rapid and accurate environmental forecasting.

[0259] A "high-altitude platform" is a mobile base for collecting data in high-altitude airspace using devices such as balloons and drones.

[0260] "Environmental data" refers to information related to weather and the natural environment in a specific region, such as temperature, humidity, wind speed, and rainfall.

[0261] "Communication methods" refer to networks and protocols used to transmit collected data, such as wireless communication and data transfer technologies using the Internet.

[0262] A "storage device" refers to a physical or virtual data storage structure used by a server to store data for extended periods, such as a database or cloud storage.

[0263] A "generative model" is a computational model that analyzes large amounts of data based on machine learning algorithms to generate predictions and new insights.

[0264] A "user" is an individual or group that can receive predicted information and act based on it.

[0265] "Feedback" refers to evaluations and opinions from users regarding the information and services provided, and this information is used to improve the system.

[0266] A "detector" is a sensor device mounted on a high-altitude platform to acquire environmental data.

[0267] A "machine learning algorithm" is a set of computational methods that analyze large amounts of data, allowing a model to automatically learn patterns and rules, and then perform predictions and classifications.

[0268] An "alert" is a notification or warning that automatically alerts users when a specific risk is predicted.

[0269] This invention aims to collect environmental data using a high-altitude platform and make predictions based on that data using a generative model. The system mainly consists of three elements: a server, a terminal, and a user.

[0270] The server plays a central role in the system. It receives real-time environmental data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed using a generative model based on machine learning algorithms. This generative model has the ability to predict future weather conditions based on the environmental data, generates warnings based on the analysis results, and automatically notifies the user.

[0271] The terminal is responsible for controlling the high-altitude platform and transmitting environmental data to the server. Control software installed on the terminal moves the high-altitude platform to a designated observation position and uses sensors to acquire detailed environmental data. The terminal transmits the data to the server using a secure and stable communication method, and the data integrity is verified during the transmission process to ensure that accurate information reaches the server.

[0272] Users can receive predicted information provided by the server and take appropriate action. For example, they can take steps such as evacuating or preparing quickly based on predicted heavy rainfall information. They can also send feedback to the server, contributing to the improvement of the generative model's algorithm. This feedback process allows the system to continuously improve its prediction accuracy.

[0273] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and collects environmental data. A generative model then analyzes this data and predicts changes in wind speed and rainfall several hours in advance. Based on this, users can take early evacuation and necessary disaster prevention measures, thereby minimizing damage.

[0274] An example of a prompt message is, "Design a generative model to predict weather conditions based on highly accurate environmental data." This method enables rapid and accurate environmental forecasting.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The terminal moves the high-altitude platform to the designated observation location. The control software operates the high-altitude platform to reach the set position. This movement determines the observable range. As input, it receives the coordinate information of the designated observation location, and as output, it generates a confirmation signal after the movement is completed.

[0278] Step 2:

[0279] The terminal acquires environmental data at the observation location. The sensors installed on the high-altitude platform collect data such as temperature, humidity, wind speed, and precipitation. These data are acquired as initial analog signals and digitized. As input, it receives the analog signal from the sensor, and as output, it generates digitized environmental data.

[0280] Step 3:

[0281] The terminal transmits the collected environmental data to the server. The communication module uses a secure protocol to perform the transmission while maintaining data integrity. During the communication process, an algorithm for detecting and correcting data errors is applied. As input, it receives the digitized environmental data, and as output, it generates a notification of the completion of data transmission to the server.

[0282] Step 4:

[0283] The server stores the received environmental data in the database. During the storage process, the accuracy of the data is verified and timestamp information is added. As a result, the data is accumulated in the storage device in a searchable state. As input, it receives the environmental data transmitted from the terminal, and as output, it generates a confirmation signal of the completion of data storage.

[0284] Step 5:

[0285] The server performs analysis by a generation model using the stored data. The machine learning algorithm predicts future environmental conditions while referring to past data. In this process, detection and correction of outliers are performed to generate highly accurate prediction results. As input, it receives environmental data obtained from a database, and as output, it generates prediction results and warning information.

[0286] Step 6:

[0287] The server notifies the user of the generated prediction results. The notification system utilizes warnings and cautionary information to send a message to the device used by the user. Thereby, the user can take prompt action. As input, it receives the prediction results by the generation model, and as output, it generates a notification completion signal to the user device.

[0288] Step 7:

[0289] The user acts based on the information provided by the server. Based on the prediction information and warning content, safety measures and schedule adjustments can be made. Also, feedback on the accuracy of the prediction information is sent to the server. As input, it receives the notification information from the server, and as output, it generates a signal to send the feedback result to the server.

[0290] Step 8:

[0291] The server improves the generation model based on the feedback from the user. The parameters of the algorithm are appropriately adjusted to improve the prediction accuracy. Thereby, continuous improvement of the system is possible. As input, it receives the feedback from the user, and as output, it generates an improved generation model.

[0292] (Application Example 1)

[0293] Next, Application Example 1 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".

[0294] Sudden changes in weather can cause delays and compromise safety in logistics deliveries, significantly impacting customer satisfaction in food delivery services. However, conventional weather information systems often struggle to provide real-time information and enable quick decision-making, hindering efficient deliveries. This presents challenges in optimizing deliveries and ensuring safety.

[0295] 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.

[0296] In this invention, the server includes means for collecting weather data using a high-altitude platform, means for storing the collected weather data in an information storage device via communication means, means for analyzing the stored data and using a generative model to perform weather forecasting, means for optimizing travel routes based on weather forecast information, and means for automatically adjusting affected routes in real time. This enables the planning and implementation of rapid and safe delivery plans based on weather forecast information in food delivery.

[0297] A "high-altitude platform" refers to devices such as flying vehicles or floating objects used to acquire weather data in the atmosphere.

[0298] "Weather data" refers to observational information necessary to understand weather conditions, such as temperature, humidity, wind speed, and precipitation.

[0299] "Communication means" refers to methods and technologies for transmitting collected data to information storage devices located in remote locations.

[0300] An "information storage device" refers to a device or system that securely and reliably stores data received via communication means.

[0301] A "generative model" refers to a program or system that uses machine learning algorithms to analyze weather data and predict future weather patterns.

[0302] "Optimization of the movement route" refers to the process of determining the safest and most efficient operation route based on the current weather conditions and the predicted weather.

[0303] "Automatic adjustment" refers to the function or process of modifying the plan or schedule in real time according to changes in the weather and the current situation.

[0304] "Delivery itinerary" refers to a series of processes and flows including the logistics process from the place of origin to the destination of the goods.

[0305] To implement this invention, a system combining three elements: a server, a terminal, and a user is required. The server is implemented using Python and the Django framework and has the function of receiving weather data from a high-altitude platform in real time and storing it in an information storage device. In the server, a machine learning algorithm using TensorFlow is used to capture the vast amount of weather data collected and perform detailed weather predictions using a generation model. This prediction information is distributed to the terminal through an API.

[0306] The terminal controls a high-altitude platform equipped with sensors and collects data at designated weather observation points. By safely and accurately transmitting this data to the server, it helps to grasp the real-time weather situation.

[0307] The user receives the weather prediction information provided by the server via a smartphone or other digital device. Based on this information, it becomes possible to optimize the delivery itinerary and instruct movement at an appropriate timing. For example, food delivery operators can plan a safe route in advance for the predicted bad weather and perform deliveries safely and efficiently.

[0308] For example, a delivery driver could receive a warning about an approaching storm via the application and pre-set a safe detour route, minimizing delays and enabling faster delivery to customers. An example of a prompt to implement such a use case would be: "Generate optimized delivery routes and recommended alternative routes based on the storm warning for the Tokyo area for the next 24 hours."

[0309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0310] Step 1:

[0311] The terminal controls a high-altitude platform and performs sensing at designated weather observation points. Inputs are the platform's location information and the designated observation points, while output is the observed weather data. Specifically, it uses sensors to measure data such as temperature, humidity, and wind speed, and transmits this information to a server in real time.

[0312] Step 2:

[0313] The server stores weather data received from terminals in a database. The input is real-time weather data sent from terminals, and the output is structured data stored in the database. At this stage, the server checks the integrity of the data and cleans it to ensure there are no invalid or missing values.

[0314] Step 3:

[0315] The server uses a generative AI model to analyze stored weather data. The input is the contents of the accumulated weather database, and the output is detailed weather forecast information for several hours ahead. Specifically, TensorFlow is used to train the data with a machine learning algorithm and predict weather changes for the next 24 hours.

[0316] Step 4:

[0317] The server notifies food delivery drivers of predicted weather information. The input is weather forecast information obtained from a generating AI model, and the output is warnings and recommended routes displayed on the smartphone app used by the delivery drivers. Here, warnings are generated in real time, and safe delivery routes are suggested. An example of a prompt message is: "Generate optimized delivery routes and recommended alternative routes as a storm warning for the Tokyo area for the next 24 hours."

[0318] Step 5:

[0319] The user adjusts delivery schedules and routes appropriately based on the provided weather forecast information. Input is weather forecasts and warnings received from the server, and output is a real-time updated delivery route and schedule. Specifically, the user selects a recommended safe route within the app based on the received information and performs delivery tasks efficiently.

[0320] 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.

[0321] This invention is a system that combines a weather data collection device and an emotion engine, with the aim of improving the accuracy of weather forecasts and optimizing the user experience. This system consists of a server, terminals, and users, each of which works in cooperation with one another.

[0322] The server plays a central role in this system. It receives weather data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed by a generative model using machine learning algorithms, and weather forecasts are made. Based on these forecast results, the server automatically sends notifications and warnings to users. Furthermore, the system incorporates an emotion engine that analyzes user feedback and evaluates the user's emotional state. This makes it possible to optimize the forecast results and warning content according to the user's emotions.

[0323] The terminal controls the high-altitude platform and issues movement instructions to a designated area. The platform measures detailed weather data on-site and transmits that data to the server via the terminal. The terminal is responsible for checking the accuracy of the data and transmitting it using highly secure communication methods.

[0324] Users receive weather forecasts and warnings sent from the server and take disaster prevention measures as needed. Users also provide feedback to the system through an emotion engine. This feedback includes the user's understanding of and satisfaction with the weather information, as well as their reaction to warnings. This information is analyzed by the server and used to improve the generative model.

[0325] As a concrete example, after a heavy rain forecast is generated by a generative model, the server notifies the user of the results. At this time, the emotion engine analyzes the user's reaction, and if, for example, the user expresses concern about the forecast, it adjusts the system to provide more careful and detailed information next time. This improves the user experience and enables even more accurate weather forecasting.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The server collects the latest weather information from sources such as the Japan Meteorological Agency to analyze weather data in high-risk areas. Next, it plans the deployment of high-altitude platforms in specific areas.

[0329] Step 2:

[0330] The terminal sends a movement instruction to the high-altitude platform, which controls the platform to reach the designated area. The platform continuously checks its current location via GPS on its way to the destination.

[0331] Step 3:

[0332] Once the platform reaches its destination, the terminal activates its weather sensors and begins measuring data. This includes real-time measurements of temperature, humidity, atmospheric pressure, wind speed, and precipitation.

[0333] Step 4:

[0334] The device temporarily stores the collected data and verifies its integrity and quality. After verification, the data is sent to the server using a communication method.

[0335] Step 5:

[0336] The server stores the received weather data in a database and inputs it into a generative model. A machine learning algorithm performs weather analysis and generates a weather forecast for several hours in advance.

[0337] Step 6:

[0338] The server evaluates weather risks, such as rainfall, based on the generated forecast data. Based on the evaluation results, it determines the content of the alerts to be sent to the user and generates notifications.

[0339] Step 7:

[0340] Users receive notifications from the server and consider disaster response measures as needed. For example, they can check their schedules and take actions to ensure their safety.

[0341] Step 8:

[0342] Users send feedback on weather forecasts and warnings to the server via an emotion engine. This feedback includes emotions such as satisfaction with the forecast and concerns.

[0343] Step 9:

[0344] The server analyzes user feedback using an emotion engine and evaluates the user's emotions. This information is then used to optimize the generative model and improve the method of future notifications.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] Current weather forecasting systems struggle to provide users with accurate and timely forecast information, and they lack optimization of information based on user sentiment and feedback. Therefore, improving forecast accuracy and optimizing the user experience are key challenges.

[0348] 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.

[0349] In this invention, the server includes means for collecting weather data using an atmospheric mobile device, means for storing the data in a data set using information transmission means, and means for analyzing user opinions using an emotion analysis engine and optimizing notification content. This enables highly accurate weather forecasting and improved user experience.

[0350] An "atmospheric mobile device" is a device that has the function of collecting weather data while moving within the Earth's atmosphere.

[0351] "Meteorological data" refers to numerical information about the atmosphere, such as temperature, humidity, wind speed, and precipitation, and is fundamental data used for weather forecasting.

[0352] "Information transmission means" refers to technologies and protocols for securely transmitting acquired data to a server or other system.

[0353] A "data set" refers to a collection of various data formats stored on a server, and is a set of fundamental data used for analysis.

[0354] A "generative algorithm" is a method that uses machine learning and artificial intelligence technologies to analyze weather data and predict future weather patterns.

[0355] "User" refers to an individual or organization that receives weather forecasts and warning information provided by the system.

[0356] "Opinions" refers to feedback and comments provided by users, including evaluations of the information provided by the system and suggestions for improvement.

[0357] An "emotion analysis engine" is a technology that analyzes user opinions from an emotional perspective and uses that analysis to optimize services and information provision.

[0358] This invention is a system aimed at improving the efficiency of weather forecasting and the user experience. The system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0359] The server plays a central role in this system. The server receives weather data transmitted from terminals and stores it in a database. The stored data is analyzed by advanced machine learning software using generative algorithms. This analysis process allows for the prediction of weather patterns such as precipitation, temperature, and humidity. Using generative AI models, the server provides real-time forecasts and automatically generates warnings and notifications for users.

[0360] Meanwhile, the terminal controls the atmospheric mobile device. This device moves to a designated area and uses sensors to measure detailed weather data. The collected data is securely transmitted to a server using an information transmission system. The terminal also has the function of checking the reliability of the data and filtering out inaccurate data.

[0361] The user is the recipient of predictive information provided by the system. Based on this information, the user can take appropriate actions and provide feedback to the system through a sentiment analysis engine. This feedback includes the accuracy and usefulness of the information, as well as emotional responses.

[0362] For example, heavy rainfall is predicted based on a generative AI model, and the server notifies the user of this information. The user then provides feedback to the system regarding this prediction, which the sentiment analysis engine analyzes to make future notifications more tailored to the user.

[0363] An example of a prompt message would be: "Generate a weather forecast for the specified area this weekend, optimize the notification content to be emotionally sensitive for the user, and create a report that takes user feedback into consideration." This would enable the system to achieve highly accurate predictions and improve user satisfaction.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The terminal controls a high-altitude platform and moves to a designated area. During this process, it uses GPS to determine its precise location and sensors to collect weather data in real time. Input is regional information, and output is weather data such as temperature, humidity, wind speed, and precipitation.

[0367] Step 2:

[0368] The terminal checks the accuracy of the collected weather data. This process executes algorithms to filter out outliers and noise. The input is the collected weather data, and the output is verified, clean data.

[0369] Step 3:

[0370] The terminal transmits weather data to the server using a secure communication method. During this process, the data is encrypted to ensure security. The input is clean weather data, and the output is encrypted data sent to the server.

[0371] Step 4:

[0372] The server receives data sent from the terminal and stores it in the database. The input is encrypted weather data, and the output is the stored data in the database.

[0373] Step 5:

[0374] The server analyzes data using a generative AI model to predict future weather. The input is weather data stored in a database, and the output is the weather forecast result. Machine learning algorithms are applied throughout this process.

[0375] Step 6:

[0376] The server creates a notification message for the user based on the generated weather forecast results. It optimizes the content using prompts. The input is the weather forecast results, and the output is the notification message.

[0377] Step 7:

[0378] Users receive notifications from the server and take action based on them. They also provide feedback to the system, including their opinions and impressions of the prediction results. The input is the notification message, and the output is the user's feedback.

[0379] Step 8:

[0380] The server analyzes user feedback using an emotion analysis engine to optimize the content of future notifications. The input is user feedback, and the output is the adjusted notification settings. This improves the quality of information delivery.

[0381] (Application Example 2)

[0382] 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."

[0383] There is a need to reduce risks and improve service quality by promptly and appropriately notifying both users and service providers of the impact of weather and environmental changes on services and transportation. However, conventional systems have limitations in the accuracy of predictions and the appropriateness of notifications, and they lack optimization of information based on user sentiment and feedback.

[0384] 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.

[0385] In this invention, the server includes means for using a generative model to analyze collected environmental information and perform environmental predictions, means for notifying the user of the prediction results and receiving responses, and means for analyzing the user's responses and adjusting the content of the notifications. This makes it possible to optimize notifications based on the user's emotions and provide more accurate and appropriate information.

[0386] A "high-altitude platform" is a device installed at a high altitude to collect environmental information at a designated location.

[0387] "Environmental information" refers to data related to weather and the surrounding environment, and is a record of specific numerical values ​​and conditions that are the subject of information collection.

[0388] "Communication means" refers to devices or methods used to transmit collected information to recording devices or servers.

[0389] A "recording device" refers to a database or storage device used to store collected information, and is a device that holds information for analysis.

[0390] A "generative model" is a model that includes machine learning algorithms for making predictions based on collected environmental information.

[0391] A "user" is an individual or legal entity that receives weather forecast results or notifications, and is the recipient of the information.

[0392] "Notification" refers to a means of communicating information predicted by a generative model to the user, and includes information visualization and alert functions.

[0393] "Reaction" refers to the response or emotional feedback that a user provides in response to a notification they receive.

[0394] "Means of adjusting notification content" refers to the process or method of changing the content and format of notifications based on user responses and feedback.

[0395] In an embodiment of this invention, the system consists of a server, a terminal, and a user. The server is primarily responsible for information analysis and notification management. The high-altitude platform collects environmental information at various locations using sensors and transmits this information to the terminal using communication means. The terminal receives and temporarily stores the data and transfers the information to the server via the communication line. The terminal also moves the high-altitude platform to a designated location in real time, enabling accurate information collection.

[0396] The server stores the received environmental information in a recording device and then performs information analysis using a generative model incorporating machine learning algorithms. This process yields detailed environmental predictions, which are then notified to the user. The server further utilizes an emotion analysis engine called EmotionEngine to analyze the response data returned by the user and collect feedback information. This allows it to generate optimal notification content tailored to each user's emotions, thereby improving the user experience.

[0397] As a concrete example, applying this system to a food delivery service would allow it to recommend the optimal route to delivery drivers based on predicted rainfall information, and to notify customers in advance of potential delivery delays. Based on user feedback, the delivery time estimates and notification content would be constantly improved. Leveraging the capabilities of the generative AI model, a prompt message could be used to request feedback in the form of, "Please advise on what weather-related information should be prioritized for customer delivery next time."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The terminal collects environmental information from sensors installed on a high-altitude platform. The sensors measure information such as temperature, humidity, and atmospheric pressure in real time and transmit it to the terminal. As a result, the input to the terminal becomes numerical data representing the physical quantities of the environment.

[0401] Step 2:

[0402] The terminal transfers the collected environmental information to the server via a communication line. During this process, a protocol is used to verify the accuracy and integrity of the data, ensuring secure transmission. The output is the reliable environmental information received by the server.

[0403] Step 3:

[0404] The server stores the received environmental information in a recording device and performs data analysis using a generative model. This generative AI model uses machine learning algorithms to predict weather and environmental conditions. The input is the stored environmental information, and the output is detailed environmental change information as a prediction result.

[0405] Step 4:

[0406] The server notifies the user of the prediction results generated by the generative model. This notification is sent in a format suitable for the user's device and includes optimal route information for delivery drivers and an estimated delivery time for users. The input is the prediction result information, and the output is the notification to the user and delivery driver.

[0407] Step 5:

[0408] Users receive notifications from the server and provide feedback based on the results. This feedback includes evaluations of the notification content and suggestions for improvement. The input is the notification received by the user, and the output is the feedback data.

[0409] Step 6:

[0410] The server uses EmotionEngine to analyze user feedback and evaluate the emotional state. This analysis optimizes the notification content. The input is user feedback, and the output is the improved notification plan.

[0411] Step 7:

[0412] The server adjusts the notification plan for subsequent deliveries based on the analysis, contributing to improved accuracy of the generative model. Feedback such as, "Please advise on how to improve the system by providing prompts such as, 'For the next delivery, what weather-related information should I prioritize communicating to the customer?'" helps to strengthen the model.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] This invention is a weather forecasting system that utilizes a high-altitude platform for high-precision weather data collection and a generative model for analyzing that data, with the aim of improving the accuracy of disaster prediction. This system mainly consists of three main elements: a server, terminals, and users.

[0430] The server plays a central role in this system. First, it receives weather data collected in real time and stores it in a database. Next, it runs a generative model to analyze the stored data. This generative model is based on machine learning algorithms and predicts weather conditions several hours in advance based on the vast amount of data collected. The server also has the function of generating warnings based on the prediction results and automatically sending notifications to users.

[0431] The terminal controls the high-altitude platform and relays data transmission. Control software running on the terminal moves the high-altitude platform to a designated weather observation point, from which it measures detailed weather data. The observation data is transmitted to the server via the terminal. This transmission process uses secure and stable communication methods and verifies data integrity to ensure accurate data reaches the server.

[0432] Users can receive weather forecast information provided by the server and take appropriate action based on it. User feedback is also sent to the server and used to improve the generative model's algorithm. This allows the system to continuously improve its forecasting accuracy.

[0433] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and begins collecting weather data. A generative model then analyzes this data to predict changes in wind speed and precipitation several hours in advance. Based on the highly accurate predicted information, users can take prompt action to evacuate or prepare, thereby minimizing damage. Thus, this invention combines weather data collection using a high-altitude platform with analysis by a generative model to enable highly accurate and rapid weather forecasting.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The server collects real-time data from the Japan Meteorological Agency and other weather organizations and determines where to begin analysis. It identifies high-risk areas and generates instructions to move high-altitude platforms to those areas.

[0437] Step 2:

[0438] The terminal receives instructions from the server on the high-altitude platform and controls its movement to precisely reach the designated area. It uses GPS to determine its current location and runs a control program to plan the route to its destination.

[0439] Step 3:

[0440] The device activates its built-in weather sensors at the observation point it reaches, measuring real-time temperature, humidity, atmospheric pressure, wind speed, precipitation, and other data. The observation data is temporarily stored within the device, and data integrity is checked.

[0441] Step 4:

[0442] The terminal compresses data that has been verified for integrity and sends it to the server using a secure communication protocol. During transmission, it provides a retransmission function to protect against data loss or errors.

[0443] Step 5:

[0444] The server saves the received data to a database. The saved data is then input into a generative model to perform weather forecasting. The generative model uses a specified algorithm based on machine learning algorithms to perform the analysis.

[0445] Step 6:

[0446] The server analyzes the generated prediction results and generates alerts as needed. If certain criteria or thresholds are exceeded, it automatically creates an alert and takes action to notify relevant parties.

[0447] Step 7:

[0448] Users receive prediction results and warning information from the server and prepare necessary disaster prevention measures. Furthermore, they send feedback on the prediction results to the server, contributing to improving the model's accuracy.

[0449] Step 8:

[0450] The server analyzes user feedback and uses it to optimize the parameters and algorithms of the generative model. This improves real-time prediction accuracy.

[0451] (Example 1)

[0452] 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."

[0453] Improving the accuracy of environmental forecasts requires advanced data collection and analysis. However, conventional technologies have faced challenges in terms of data collection accuracy and timely analysis, making it difficult to respond quickly to environmental changes. Furthermore, there has been a lack of effective means to utilize user feedback to improve forecast accuracy.

[0454] 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.

[0455] In this invention, the server includes means for collecting environmental data using a high-altitude platform, means for storing the collected data in a storage device via communication means, and means for analyzing the stored data using a generative model. This enables rapid and accurate environmental forecasting.

[0456] A "high-altitude platform" is a mobile base for collecting data in high-altitude airspace using devices such as balloons and drones.

[0457] "Environmental data" refers to information related to weather and the natural environment in a specific region, such as temperature, humidity, wind speed, and rainfall.

[0458] "Communication methods" refer to networks and protocols used to transmit collected data, such as wireless communication and data transfer technologies using the Internet.

[0459] A "storage device" refers to a physical or virtual data storage structure used by a server to store data for extended periods, such as a database or cloud storage.

[0460] A "generative model" is a computational model that analyzes large amounts of data based on machine learning algorithms to generate predictions and new insights.

[0461] A "user" is an individual or group that can receive predicted information and act based on it.

[0462] "Feedback" refers to evaluations and opinions from users regarding the information and services provided, and this information is used to improve the system.

[0463] A "detector" is a sensor device mounted on a high-altitude platform to acquire environmental data.

[0464] A "machine learning algorithm" is a set of computational methods that analyze large amounts of data, allowing a model to automatically learn patterns and rules, and then perform predictions and classifications.

[0465] An "alert" is a notification or warning that automatically alerts users when a specific risk is predicted.

[0466] This invention aims to collect environmental data using a high-altitude platform and make predictions based on that data using a generative model. The system mainly consists of three elements: a server, a terminal, and a user.

[0467] The server plays a central role in the system. It receives real-time environmental data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed using a generative model based on machine learning algorithms. This generative model has the ability to predict future weather conditions based on the environmental data, generates warnings based on the analysis results, and automatically notifies the user.

[0468] The terminal is responsible for controlling the high-altitude platform and transmitting environmental data to the server. Control software installed on the terminal moves the high-altitude platform to a designated observation position and uses sensors to acquire detailed environmental data. The terminal transmits the data to the server using a secure and stable communication method, and the data integrity is verified during the transmission process to ensure that accurate information reaches the server.

[0469] Users can receive predicted information provided by the server and take appropriate action. For example, they can take steps such as evacuating or preparing quickly based on predicted heavy rainfall information. They can also send feedback to the server, contributing to the improvement of the generative model's algorithm. This feedback process allows the system to continuously improve its prediction accuracy.

[0470] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and collects environmental data. A generative model then analyzes this data and predicts changes in wind speed and rainfall several hours in advance. Based on this, users can take early evacuation and necessary disaster prevention measures, thereby minimizing damage.

[0471] An example of a prompt message is, "Design a generative model to predict weather conditions based on highly accurate environmental data." This method enables rapid and accurate environmental forecasting.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The terminal moves the high-altitude platform to the designated observation point. Control software moves the high-altitude platform to reach the set position. This movement determines the observable range. It receives the coordinate information of the designated observation point as input and generates a confirmation signal upon completion of the movement as output.

[0475] Step 2:

[0476] The terminal acquires environmental data at the observation site. Sensors mounted on the high-altitude platform collect data such as temperature, humidity, wind speed, and precipitation. This data is acquired as initial analog signals and then digitized. It receives analog signals from sensors as input and generates digitized environmental data as output.

[0477] Step 3:

[0478] The terminal sends the collected environmental data to the server. The communication module uses a secure protocol to transmit the data while maintaining its integrity. During the communication process, algorithms are applied to detect and correct data errors. It receives digitized environmental data as input and generates a notification that data transmission to the server is complete as output.

[0479] Step 4:

[0480] The server stores the received environmental data in a database. The storage process verifies the accuracy of the data and adds timestamp information. This ensures the data is stored in a searchable state. It receives environmental data transmitted from the terminal as input and generates a confirmation signal upon completion of data storage as output.

[0481] Step 5:

[0482] The server performs generative model analysis using stored data. Machine learning algorithms predict future environmental conditions by referring to past data. During this process, anomalies are detected and corrected, generating highly accurate prediction results. It receives environmental data obtained from a database as input and generates prediction results and warning information as output.

[0483] Step 6:

[0484] The server notifies the user of the generated prediction results. The notification system utilizes alarm and alert information to send messages to the user's device. This allows the user to respond quickly. It receives prediction results from a generative model as input and generates a notification completion signal to the user's device as output.

[0485] Step 7:

[0486] Users take action based on information provided by the server. They can implement safety measures and adjust schedules based on predictive information and warnings. They also send feedback to the server regarding the accuracy of the predictive information. The system receives notification information from the server as input and generates a signal to send feedback results to the server as output.

[0487] Step 8:

[0488] The server improves the generative model based on user feedback. It adjusts the algorithm parameters as needed to improve prediction accuracy. This enables continuous system improvement. It receives user feedback as input and generates an improved generative model as output.

[0489] (Application Example 1)

[0490] 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."

[0491] Sudden changes in weather can cause delays and compromise safety in logistics deliveries, significantly impacting customer satisfaction in food delivery services. However, conventional weather information systems often struggle to provide real-time information and enable quick decision-making, hindering efficient deliveries. This presents challenges in optimizing deliveries and ensuring safety.

[0492] 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.

[0493] In this invention, the server includes means for collecting weather data using a high-altitude platform, means for storing the collected weather data in an information storage device via communication means, means for analyzing the stored data and using a generative model to perform weather forecasting, means for optimizing travel routes based on weather forecast information, and means for automatically adjusting affected routes in real time. This enables the planning and implementation of rapid and safe delivery plans based on weather forecast information in food delivery.

[0494] A "high-altitude platform" refers to devices such as flying vehicles or floating objects used to acquire weather data in the atmosphere.

[0495] "Weather data" refers to observational information necessary to understand weather conditions, such as temperature, humidity, wind speed, and precipitation.

[0496] "Communication means" refers to methods and technologies for transmitting collected data to information storage devices located in remote locations.

[0497] An "information storage device" refers to a device or system that securely and reliably stores data received via communication means.

[0498] A "generative model" refers to a program or system that uses machine learning algorithms to analyze weather data and predict future weather patterns.

[0499] "Optimizing travel routes" refers to the process of determining the safest and most efficient route based on current weather conditions and forecast weather.

[0500] "Automatic adjustment" refers to a function or process that modifies plans and schedules in real time in response to changes in weather or current conditions.

[0501] "Delivery process" refers to the entire process or flow of logistics, including the logistics steps from the origin of the goods to their destination.

[0502] To realize this invention, a system combining three elements—a server, a terminal, and a user—is required. The server is implemented using Python and the Django framework and has the function of receiving weather data from a high-altitude platform in real time and storing it in an information storage device. The server uses a machine learning algorithm based on TensorFlow to take in the vast amount of collected weather data and performs detailed weather forecasts using a generative model. This forecast information is delivered to the terminal via an API.

[0503] The terminal controls a high-altitude platform equipped with sensors and collects data at designated weather observation points. This data is securely and accurately transmitted to a server, helping to understand weather conditions in real time.

[0504] Users receive weather forecast information from a server via their smartphone or other digital device. Based on this information, delivery routes can be optimized, and drivers can be instructed to move at the appropriate time. For example, food delivery companies can plan safe routes in advance for predicted bad weather, enabling them to deliver safely and efficiently.

[0505] For example, a delivery driver could receive a warning about an approaching storm via the application and pre-set a safe detour route, minimizing delays and enabling faster delivery to customers. An example of a prompt to implement such a use case would be: "Generate optimized delivery routes and recommended alternative routes based on the storm warning for the Tokyo area for the next 24 hours."

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The terminal controls a high-altitude platform and performs sensing at designated weather observation points. Inputs are the platform's location information and the designated observation points, while output is the observed weather data. Specifically, it uses sensors to measure data such as temperature, humidity, and wind speed, and transmits this information to a server in real time.

[0509] Step 2:

[0510] The server stores weather data received from terminals in a database. The input is real-time weather data sent from terminals, and the output is structured data stored in the database. At this stage, the server checks the integrity of the data and cleans it to ensure there are no invalid or missing values.

[0511] Step 3:

[0512] The server uses a generative AI model to analyze stored weather data. The input is the contents of the accumulated weather database, and the output is detailed weather forecast information for several hours ahead. Specifically, TensorFlow is used to train the data with a machine learning algorithm and predict weather changes for the next 24 hours.

[0513] Step 4:

[0514] The server notifies food delivery drivers of predicted weather information. The input is weather forecast information obtained from a generating AI model, and the output is warnings and recommended routes displayed on the smartphone app used by the delivery drivers. Here, warnings are generated in real time, and safe delivery routes are suggested. An example of a prompt message is: "Generate optimized delivery routes and recommended alternative routes as a storm warning for the Tokyo area for the next 24 hours."

[0515] Step 5:

[0516] The user adjusts delivery schedules and routes appropriately based on the provided weather forecast information. Input is weather forecasts and warnings received from the server, and output is a real-time updated delivery route and schedule. Specifically, the user selects a recommended safe route within the app based on the received information and performs delivery tasks efficiently.

[0517] 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.

[0518] This invention is a system that combines a weather data collection device and an emotion engine, with the aim of improving the accuracy of weather forecasts and optimizing the user experience. This system consists of a server, terminals, and users, each of which works in cooperation with one another.

[0519] The server plays a central role in this system. It receives weather data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed by a generative model using machine learning algorithms, and weather forecasts are made. Based on these forecast results, the server automatically sends notifications and warnings to users. Furthermore, the system incorporates an emotion engine that analyzes user feedback and evaluates the user's emotional state. This makes it possible to optimize the forecast results and warning content according to the user's emotions.

[0520] The terminal controls the high-altitude platform and issues movement instructions to a designated area. The platform measures detailed weather data on-site and transmits that data to the server via the terminal. The terminal is responsible for checking the accuracy of the data and transmitting it using highly secure communication methods.

[0521] Users receive weather forecasts and warnings sent from the server and take disaster prevention measures as needed. Users also provide feedback to the system through an emotion engine. This feedback includes the user's understanding of and satisfaction with the weather information, as well as their reaction to warnings. This information is analyzed by the server and used to improve the generative model.

[0522] As a concrete example, after a heavy rain forecast is generated by a generative model, the server notifies the user of the results. At this time, the emotion engine analyzes the user's reaction, and if, for example, the user expresses concern about the forecast, it adjusts the system to provide more careful and detailed information next time. This improves the user experience and enables even more accurate weather forecasting.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The server collects the latest weather information from sources such as the Japan Meteorological Agency to analyze weather data in high-risk areas. Next, it plans the deployment of high-altitude platforms in specific areas.

[0526] Step 2:

[0527] The terminal sends a movement instruction to the high-altitude platform, which controls the platform to reach the designated area. The platform continuously checks its current location via GPS on its way to the destination.

[0528] Step 3:

[0529] Once the platform reaches its destination, the terminal activates its weather sensors and begins measuring data. This includes real-time measurements of temperature, humidity, atmospheric pressure, wind speed, and precipitation.

[0530] Step 4:

[0531] The device temporarily stores the collected data and verifies its integrity and quality. After verification, the data is sent to the server using a communication method.

[0532] Step 5:

[0533] The server stores the received weather data in a database and inputs it into a generative model. A machine learning algorithm performs weather analysis and generates a weather forecast for several hours in advance.

[0534] Step 6:

[0535] The server evaluates weather risks, such as rainfall, based on the generated forecast data. Based on the evaluation results, it determines the content of the alerts to be sent to the user and generates notifications.

[0536] Step 7:

[0537] Users receive notifications from the server and consider disaster response measures as needed. For example, they can check their schedules and take actions to ensure their safety.

[0538] Step 8:

[0539] Users send feedback on weather forecasts and warnings to the server via an emotion engine. This feedback includes emotions such as satisfaction with the forecast and concerns.

[0540] Step 9:

[0541] The server analyzes user feedback using an emotion engine and evaluates the user's emotions. This information is then used to optimize the generative model and improve the method of future notifications.

[0542] (Example 2)

[0543] 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."

[0544] Current weather forecasting systems struggle to provide users with accurate and timely forecast information, and they lack optimization of information based on user sentiment and feedback. Therefore, improving forecast accuracy and optimizing the user experience are key challenges.

[0545] 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.

[0546] In this invention, the server includes means for collecting weather data using an atmospheric mobile device, means for storing the data in a data set using information transmission means, and means for analyzing user opinions using an emotion analysis engine and optimizing notification content. This enables highly accurate weather forecasting and improved user experience.

[0547] An "atmospheric mobile device" is a device that has the function of collecting weather data while moving within the Earth's atmosphere.

[0548] "Meteorological data" refers to numerical information about the atmosphere, such as temperature, humidity, wind speed, and precipitation, and is fundamental data used for weather forecasting.

[0549] "Information transmission means" refers to technologies and protocols for securely transmitting acquired data to a server or other system.

[0550] A "data set" refers to a collection of various data formats stored on a server, and is a set of fundamental data used for analysis.

[0551] A "generative algorithm" is a method that uses machine learning and artificial intelligence technologies to analyze weather data and predict future weather patterns.

[0552] "User" refers to an individual or organization that receives weather forecasts and warning information provided by the system.

[0553] "Opinions" refers to feedback and comments provided by users, including evaluations of the information provided by the system and suggestions for improvement.

[0554] An "emotion analysis engine" is a technology that analyzes user opinions from an emotional perspective and uses that analysis to optimize services and information provision.

[0555] This invention is a system aimed at improving the efficiency of weather forecasting and the user experience. The system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0556] The server plays a central role in this system. The server receives weather data transmitted from terminals and stores it in a database. The stored data is analyzed by advanced machine learning software using generative algorithms. This analysis process allows for the prediction of weather patterns such as precipitation, temperature, and humidity. Using generative AI models, the server provides real-time forecasts and automatically generates warnings and notifications for users.

[0557] Meanwhile, the terminal controls the atmospheric mobile device. This device moves to a designated area and uses sensors to measure detailed weather data. The collected data is securely transmitted to a server using an information transmission system. The terminal also has the function of checking the reliability of the data and filtering out inaccurate data.

[0558] The user is the recipient of predictive information provided by the system. Based on this information, the user can take appropriate actions and provide feedback to the system through a sentiment analysis engine. This feedback includes the accuracy and usefulness of the information, as well as emotional responses.

[0559] For example, heavy rainfall is predicted based on a generative AI model, and the server notifies the user of this information. The user then provides feedback to the system regarding this prediction, which the sentiment analysis engine analyzes to make future notifications more tailored to the user.

[0560] An example of a prompt message would be: "Generate a weather forecast for the specified area this weekend, optimize the notification content to be emotionally sensitive for the user, and create a report that takes user feedback into consideration." This would enable the system to achieve highly accurate predictions and improve user satisfaction.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The terminal controls a high-altitude platform and moves to a designated area. During this process, it uses GPS to determine its precise location and sensors to collect weather data in real time. Input is regional information, and output is weather data such as temperature, humidity, wind speed, and precipitation.

[0564] Step 2:

[0565] The terminal checks the accuracy of the collected weather data. This process executes algorithms to filter out outliers and noise. The input is the collected weather data, and the output is verified, clean data.

[0566] Step 3:

[0567] The terminal transmits weather data to the server using a secure communication method. During this process, the data is encrypted to ensure security. The input is clean weather data, and the output is encrypted data sent to the server.

[0568] Step 4:

[0569] The server receives data sent from the terminal and stores it in the database. The input is encrypted weather data, and the output is the stored data in the database.

[0570] Step 5:

[0571] The server analyzes data using a generative AI model to predict future weather. The input is weather data stored in a database, and the output is the weather forecast result. Machine learning algorithms are applied throughout this process.

[0572] Step 6:

[0573] The server creates a notification message for the user based on the generated weather forecast results. It optimizes the content using prompts. The input is the weather forecast results, and the output is the notification message.

[0574] Step 7:

[0575] Users receive notifications from the server and take action based on them. They also provide feedback to the system, including their opinions and impressions of the prediction results. The input is the notification message, and the output is the user's feedback.

[0576] Step 8:

[0577] The server analyzes user feedback using an emotion analysis engine to optimize the content of future notifications. The input is user feedback, and the output is the adjusted notification settings. This improves the quality of information delivery.

[0578] (Application Example 2)

[0579] 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."

[0580] There is a need to reduce risks and improve service quality by promptly and appropriately notifying both users and service providers of the impact of weather and environmental changes on services and transportation. However, conventional systems have limitations in the accuracy of predictions and the appropriateness of notifications, and they lack optimization of information based on user sentiment and feedback.

[0581] 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.

[0582] In this invention, the server includes means for using a generative model to analyze collected environmental information and perform environmental predictions, means for notifying the user of the prediction results and receiving responses, and means for analyzing the user's responses and adjusting the content of the notifications. This makes it possible to optimize notifications based on the user's emotions and provide more accurate and appropriate information.

[0583] A "high-altitude platform" is a device installed at a high altitude to collect environmental information at a designated location.

[0584] "Environmental information" refers to data related to weather and the surrounding environment, and is a record of specific numerical values ​​and conditions that are the subject of information collection.

[0585] "Communication means" refers to devices or methods used to transmit collected information to recording devices or servers.

[0586] A "recording device" refers to a database or storage device used to store collected information, and is a device that holds information for analysis.

[0587] A "generative model" is a model that includes machine learning algorithms for making predictions based on collected environmental information.

[0588] A "user" is an individual or legal entity that receives weather forecast results or notifications, and is the recipient of the information.

[0589] "Notification" refers to a means of communicating information predicted by a generative model to the user, and includes information visualization and alert functions.

[0590] "Reaction" refers to the response or emotional feedback that a user provides in response to a notification they receive.

[0591] "Means of adjusting notification content" refers to the process or method of changing the content and format of notifications based on user responses and feedback.

[0592] In an embodiment of this invention, the system consists of a server, a terminal, and a user. The server is primarily responsible for information analysis and notification management. The high-altitude platform collects environmental information at various locations using sensors and transmits this information to the terminal using communication means. The terminal receives and temporarily stores the data and transfers the information to the server via the communication line. The terminal also moves the high-altitude platform to a designated location in real time, enabling accurate information collection.

[0593] The server stores the received environmental information in a recording device and then performs information analysis using a generative model incorporating machine learning algorithms. This process yields detailed environmental predictions, which are then notified to the user. The server further utilizes an emotion analysis engine called EmotionEngine to analyze the response data returned by the user and collect feedback information. This allows it to generate optimal notification content tailored to each user's emotions, thereby improving the user experience.

[0594] As a concrete example, applying this system to a food delivery service would allow it to recommend the optimal route to delivery drivers based on predicted rainfall information, and to notify customers in advance of potential delivery delays. Based on user feedback, the delivery time estimates and notification content would be constantly improved. Leveraging the capabilities of the generative AI model, a prompt message could be used to request feedback in the form of, "Please advise on what weather-related information should be prioritized for customer delivery next time."

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The terminal collects environmental information from sensors installed on a high-altitude platform. The sensors measure information such as temperature, humidity, and atmospheric pressure in real time and transmit it to the terminal. As a result, the input to the terminal becomes numerical data representing the physical quantities of the environment.

[0598] Step 2:

[0599] The terminal transfers the collected environmental information to the server via a communication line. During this process, a protocol is used to verify the accuracy and integrity of the data, ensuring secure transmission. The output is the reliable environmental information received by the server.

[0600] Step 3:

[0601] The server stores the received environmental information in a recording device and performs data analysis using a generative model. This generative AI model uses machine learning algorithms to predict weather and environmental conditions. The input is the stored environmental information, and the output is detailed environmental change information as a prediction result.

[0602] Step 4:

[0603] The server notifies the user of the prediction results generated by the generative model. This notification is sent in a format suitable for the user's device and includes optimal route information for delivery drivers and an estimated delivery time for users. The input is the prediction result information, and the output is the notification to the user and delivery driver.

[0604] Step 5:

[0605] Users receive notifications from the server and provide feedback based on the results. This feedback includes evaluations of the notification content and suggestions for improvement. The input is the notification received by the user, and the output is the feedback data.

[0606] Step 6:

[0607] The server uses EmotionEngine to analyze user feedback and evaluate the emotional state. This analysis optimizes the notification content. The input is user feedback, and the output is the improved notification plan.

[0608] Step 7:

[0609] The server adjusts the notification plan for subsequent deliveries based on the analysis, contributing to improved accuracy of the generative model. Feedback such as, "Please advise on how to improve the system by providing prompts such as, 'For the next delivery, what weather-related information should I prioritize communicating to the customer?'" helps to strengthen the model.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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".

[0627] This invention is a weather forecasting system that utilizes a high-altitude platform for collecting high-precision weather data and a generative model for analyzing that data, with the aim of improving the accuracy of disaster prediction. This system mainly consists of three main elements: a server, terminals, and users.

[0628] The server plays a central role in this system. First, it receives weather data collected in real time and stores it in a database. Next, it runs a generative model to analyze the stored data. This generative model is based on machine learning algorithms and predicts weather conditions several hours in advance based on the vast amount of data collected. The server also has the function of generating warnings based on the prediction results and automatically sending notifications to users.

[0629] The terminal controls the high-altitude platform and relays data transmission. Control software running on the terminal moves the high-altitude platform to a designated weather observation point, from which it measures detailed weather data. The observation data is transmitted to the server via the terminal. This transmission process uses secure and stable communication methods and verifies data integrity to ensure accurate data reaches the server.

[0630] Users can receive weather forecast information provided by the server and take appropriate action based on it. User feedback is also sent to the server and used to improve the generative model's algorithm. This allows the system to continuously improve its forecasting accuracy.

[0631] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and begins collecting weather data. A generative model then analyzes this data to predict changes in wind speed and precipitation several hours in advance. Based on the highly accurate predicted information, users can take prompt action to evacuate or prepare, thereby minimizing damage. Thus, this invention combines weather data collection using a high-altitude platform with analysis by a generative model to enable highly accurate and rapid weather forecasting.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The server collects real-time data from the Japan Meteorological Agency and other weather organizations and determines where to begin analysis. It identifies high-risk areas and generates instructions to move high-altitude platforms to those areas.

[0635] Step 2:

[0636] The terminal receives instructions from the server on the high-altitude platform and controls its movement to precisely reach the designated area. It uses GPS to determine its current location and runs a control program to plan the route to its destination.

[0637] Step 3:

[0638] The device activates its built-in weather sensors at the observation point it reaches, measuring real-time temperature, humidity, atmospheric pressure, wind speed, precipitation, and other data. The observation data is temporarily stored within the device, and data integrity is checked.

[0639] Step 4:

[0640] The terminal compresses data that has been verified for integrity and sends it to the server using a secure communication protocol. During transmission, it provides a retransmission function to protect against data loss or errors.

[0641] Step 5:

[0642] The server saves the received data to a database. The saved data is then input into a generative model to perform weather forecasting. The generative model uses a specified algorithm based on machine learning algorithms to perform the analysis.

[0643] Step 6:

[0644] The server analyzes the generated prediction results and generates alerts as needed. If certain criteria or thresholds are exceeded, it automatically creates an alert and takes action to notify relevant parties.

[0645] Step 7:

[0646] Users receive prediction results and warning information from the server and prepare necessary disaster prevention measures. Furthermore, they send feedback on the prediction results to the server, contributing to improving the model's accuracy.

[0647] Step 8:

[0648] The server analyzes user feedback and uses it to optimize the parameters and algorithms of the generative model. This improves real-time prediction accuracy.

[0649] (Example 1)

[0650] 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".

[0651] Improving the accuracy of environmental forecasts requires advanced data collection and analysis. However, conventional technologies have faced challenges in terms of data collection accuracy and timely analysis, making it difficult to respond quickly to environmental changes. Furthermore, there has been a lack of effective means to utilize user feedback to improve forecast accuracy.

[0652] 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.

[0653] In this invention, the server includes means for collecting environmental data using a high-altitude platform, means for storing the collected data in a storage device via communication means, and means for analyzing the stored data using a generative model. This enables rapid and accurate environmental forecasting.

[0654] A "high-altitude platform" is a mobile base for collecting data in high-altitude airspace using devices such as balloons and drones.

[0655] "Environmental data" refers to information related to weather and the natural environment in a specific region, such as temperature, humidity, wind speed, and rainfall.

[0656] "Communication methods" refer to networks and protocols used to transmit collected data, such as wireless communication and data transfer technologies using the Internet.

[0657] A "storage device" refers to a physical or virtual data storage structure used by a server to store data for extended periods, such as a database or cloud storage.

[0658] A "generative model" is a computational model that analyzes large amounts of data based on machine learning algorithms to generate predictions and new insights.

[0659] A "user" is an individual or group that can receive predicted information and act based on it.

[0660] "Feedback" refers to evaluations and opinions from users regarding the information and services provided, and this information is used to improve the system.

[0661] A "detector" is a sensor device mounted on a high-altitude platform to acquire environmental data.

[0662] A "machine learning algorithm" is a set of computational methods that analyze large amounts of data, allowing a model to automatically learn patterns and rules, and then perform predictions and classifications.

[0663] An "alert" is a notification or warning that automatically alerts users when a specific risk is predicted.

[0664] This invention aims to collect environmental data using a high-altitude platform and make predictions based on that data using a generative model. The system mainly consists of three elements: a server, a terminal, and a user.

[0665] The server plays a central role in the system. It receives real-time environmental data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed using a generative model based on machine learning algorithms. This generative model has the ability to predict future weather conditions based on the environmental data, generates warnings based on the analysis results, and automatically notifies the user.

[0666] The terminal is responsible for controlling the high-altitude platform and transmitting environmental data to the server. Control software installed on the terminal moves the high-altitude platform to a designated observation position and uses sensors to acquire detailed environmental data. The terminal transmits the data to the server using a secure and stable communication method, and the data integrity is verified during the transmission process to ensure that accurate information reaches the server.

[0667] Users can receive predicted information provided by the server and take appropriate action. For example, they can take steps such as evacuating or preparing quickly based on predicted heavy rainfall information. They can also send feedback to the server, contributing to the improvement of the generative model's algorithm. This feedback process allows the system to continuously improve its prediction accuracy.

[0668] As a concrete example, a server deploys a high-altitude platform near an area where a typhoon is predicted to approach and collects environmental data. A generative model then analyzes this data and predicts changes in wind speed and rainfall several hours in advance. Based on this, users can take early evacuation and necessary disaster prevention measures, thereby minimizing damage.

[0669] An example of a prompt message is, "Design a generative model to predict weather conditions based on highly accurate environmental data." This method enables rapid and accurate environmental forecasting.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The terminal moves the high-altitude platform to the designated observation point. Control software moves the high-altitude platform to reach the set position. This movement determines the observable range. It receives the coordinate information of the designated observation point as input and generates a confirmation signal upon completion of the movement as output.

[0673] Step 2:

[0674] The terminal acquires environmental data at the observation site. Sensors mounted on the high-altitude platform collect data such as temperature, humidity, wind speed, and precipitation. This data is acquired as initial analog signals and then digitized. It receives analog signals from sensors as input and generates digitized environmental data as output.

[0675] Step 3:

[0676] The terminal sends the collected environmental data to the server. The communication module uses a secure protocol to transmit the data while maintaining its integrity. During the communication process, algorithms are applied to detect and correct data errors. It receives digitized environmental data as input and generates a notification that data transmission to the server is complete as output.

[0677] Step 4:

[0678] The server stores the received environmental data in a database. The storage process verifies the accuracy of the data and adds timestamp information. This ensures the data is stored in a searchable state. It receives environmental data transmitted from the terminal as input and generates a confirmation signal upon completion of data storage as output.

[0679] Step 5:

[0680] The server performs generative model analysis using stored data. Machine learning algorithms predict future environmental conditions by referring to past data. During this process, anomalies are detected and corrected, generating highly accurate prediction results. It receives environmental data obtained from a database as input and generates prediction results and warning information as output.

[0681] Step 6:

[0682] The server notifies the user of the generated prediction results. The notification system utilizes alarm and alert information to send messages to the user's device. This allows the user to respond quickly. It receives prediction results from a generative model as input and generates a notification completion signal to the user's device as output.

[0683] Step 7:

[0684] Users take action based on information provided by the server. They can implement safety measures and adjust schedules based on predictive information and warnings. They also send feedback to the server regarding the accuracy of the predictive information. The system receives notification information from the server as input and generates a signal to send feedback results to the server as output.

[0685] Step 8:

[0686] The server improves the generative model based on user feedback. It adjusts the algorithm parameters as needed to improve prediction accuracy. This enables continuous system improvement. It receives user feedback as input and generates an improved generative model as output.

[0687] (Application Example 1)

[0688] 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".

[0689] Sudden changes in weather can cause delays and compromise safety in logistics deliveries, significantly impacting customer satisfaction in food delivery services. However, conventional weather information systems often struggle to provide real-time information and enable quick decision-making, hindering efficient deliveries. This presents challenges in optimizing deliveries and ensuring safety.

[0690] 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.

[0691] In this invention, the server includes means for collecting weather data using a high-altitude platform, means for storing the collected weather data in an information storage device via communication means, means for analyzing the stored data and using a generative model to perform weather forecasting, means for optimizing travel routes based on weather forecast information, and means for automatically adjusting affected routes in real time. This enables the planning and implementation of rapid and safe delivery plans based on weather forecast information in food delivery.

[0692] A "high-altitude platform" refers to devices such as flying vehicles or floating objects used to acquire weather data in the atmosphere.

[0693] "Weather data" refers to observational information necessary to understand weather conditions, such as temperature, humidity, wind speed, and precipitation.

[0694] "Communication means" refers to methods and technologies for transmitting collected data to information storage devices located in remote locations.

[0695] An "information storage device" refers to a device or system that securely and reliably stores data received via communication means.

[0696] A "generative model" refers to a program or system that uses machine learning algorithms to analyze weather data and predict future weather patterns.

[0697] "Optimizing travel routes" refers to the process of determining the safest and most efficient route based on current weather conditions and forecast weather.

[0698] "Automatic adjustment" refers to a function or process that modifies plans and schedules in real time in response to changes in weather or current conditions.

[0699] "Delivery process" refers to the entire process or flow of logistics, including the logistics steps from the origin of the goods to their destination.

[0700] To realize this invention, a system combining three elements—a server, a terminal, and a user—is required. The server is implemented using Python and the Django framework and has the function of receiving weather data from a high-altitude platform in real time and storing it in an information storage device. The server uses a machine learning algorithm based on TensorFlow to take in the vast amount of collected weather data and performs detailed weather forecasts using a generative model. This forecast information is delivered to the terminal via an API.

[0701] The terminal controls a high-altitude platform equipped with sensors and collects data at designated weather observation points. This data is securely and accurately transmitted to a server, helping to understand weather conditions in real time.

[0702] Users receive weather forecast information from a server via their smartphone or other digital device. Based on this information, delivery routes can be optimized, and drivers can be instructed to move at the appropriate time. For example, food delivery companies can plan safe routes in advance for predicted bad weather, enabling them to deliver safely and efficiently.

[0703] For example, a delivery driver could receive a warning about an approaching storm via the application and pre-set a safe detour route, minimizing delays and enabling faster delivery to customers. An example of a prompt to implement such a use case would be: "Generate optimized delivery routes and recommended alternative routes based on the storm warning for the Tokyo area for the next 24 hours."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The terminal controls a high-altitude platform and performs sensing at designated weather observation points. Inputs are the platform's location information and the designated observation points, while output is the observed weather data. Specifically, it uses sensors to measure data such as temperature, humidity, and wind speed, and transmits this information to a server in real time.

[0707] Step 2:

[0708] The server stores weather data received from terminals in a database. The input is real-time weather data sent from terminals, and the output is structured data stored in the database. At this stage, the server checks the integrity of the data and cleans it to ensure there are no invalid or missing values.

[0709] Step 3:

[0710] The server uses a generative AI model to analyze stored weather data. The input is the contents of the accumulated weather database, and the output is detailed weather forecast information for several hours ahead. Specifically, TensorFlow is used to train the data with a machine learning algorithm and predict weather changes for the next 24 hours.

[0711] Step 4:

[0712] The server notifies food delivery drivers of predicted weather information. The input is weather forecast information obtained from a generating AI model, and the output is warnings and recommended routes displayed on the smartphone app used by the delivery drivers. Here, warnings are generated in real time, and safe delivery routes are suggested. An example of a prompt message is: "Generate optimized delivery routes and recommended alternative routes as a storm warning for the Tokyo area for the next 24 hours."

[0713] Step 5:

[0714] The user adjusts delivery schedules and routes appropriately based on the provided weather forecast information. Input is weather forecasts and warnings received from the server, and output is a real-time updated delivery route and schedule. Specifically, the user selects a recommended safe route within the app based on the received information and performs delivery tasks efficiently.

[0715] 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.

[0716] This invention is a system that combines a weather data collection device and an emotion engine, with the aim of improving the accuracy of weather forecasts and optimizing the user experience. This system consists of a server, terminals, and users, each of which works in cooperation with one another.

[0717] The server plays a central role in this system. It receives weather data transmitted from the high-altitude platform and stores it in a database. The stored data is analyzed by a generative model using machine learning algorithms, and weather forecasts are made. Based on these forecast results, the server automatically sends notifications and warnings to users. Furthermore, the system incorporates an emotion engine that analyzes user feedback and evaluates the user's emotional state. This makes it possible to optimize the forecast results and warning content according to the user's emotions.

[0718] The terminal controls the high-altitude platform and issues movement instructions to a designated area. The platform measures detailed weather data on-site and transmits that data to the server via the terminal. The terminal is responsible for checking the accuracy of the data and transmitting it using highly secure communication methods.

[0719] Users receive weather forecasts and warnings sent from the server and take disaster prevention measures as needed. Users also provide feedback to the system through an emotion engine. This feedback includes the user's understanding of and satisfaction with the weather information, as well as their reaction to warnings. This information is analyzed by the server and used to improve the generative model.

[0720] As a concrete example, after a heavy rain forecast is generated by a generative model, the server notifies the user of the results. At this time, the emotion engine analyzes the user's reaction, and if, for example, the user expresses concern about the forecast, it adjusts the system to provide more careful and detailed information next time. This improves the user experience and enables even more accurate weather forecasting.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The server collects the latest weather information from sources such as the Japan Meteorological Agency to analyze weather data in high-risk areas. Next, it plans the deployment of high-altitude platforms in specific areas.

[0724] Step 2:

[0725] The terminal sends a movement instruction to the high-altitude platform, which controls the platform to reach the designated area. The platform continuously checks its current location via GPS on its way to the destination.

[0726] Step 3:

[0727] Once the platform reaches its destination, the terminal activates its weather sensors and begins measuring data. This includes real-time measurements of temperature, humidity, atmospheric pressure, wind speed, and precipitation.

[0728] Step 4:

[0729] The device temporarily stores the collected data and verifies its integrity and quality. After verification, the data is sent to the server using a communication method.

[0730] Step 5:

[0731] The server stores the received weather data in a database and inputs it into a generative model. A machine learning algorithm performs weather analysis and generates a weather forecast for several hours in advance.

[0732] Step 6:

[0733] The server evaluates weather risks, such as rainfall, based on the generated forecast data. Based on the evaluation results, it determines the content of the alerts to be sent to the user and generates notifications.

[0734] Step 7:

[0735] Users receive notifications from the server and consider disaster response measures as needed. For example, they can check their schedules and take actions to ensure their safety.

[0736] Step 8:

[0737] Users send feedback on weather forecasts and warnings to the server via an emotion engine. This feedback includes emotions such as satisfaction with the forecast and concerns.

[0738] Step 9:

[0739] The server analyzes user feedback using an emotion engine and evaluates the user's emotions. This information is then used to optimize the generative model and improve the method of future notifications.

[0740] (Example 2)

[0741] 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".

[0742] Current weather forecasting systems struggle to provide users with accurate and timely forecast information, and they lack optimization of information based on user sentiment and feedback. Therefore, improving forecast accuracy and optimizing the user experience are key challenges.

[0743] 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.

[0744] In this invention, the server includes means for collecting weather data using an atmospheric mobile device, means for storing the data in a data set using information transmission means, and means for analyzing user opinions using an emotion analysis engine and optimizing notification content. This enables highly accurate weather forecasting and improved user experience.

[0745] An "atmospheric mobile device" is a device that has the function of collecting weather data while moving within the Earth's atmosphere.

[0746] "Meteorological data" refers to numerical information about the atmosphere, such as temperature, humidity, wind speed, and precipitation, and is fundamental data used for weather forecasting.

[0747] "Information transmission means" refers to technologies and protocols for securely transmitting acquired data to a server or other system.

[0748] A "data set" refers to a collection of various data formats stored on a server, and is a set of fundamental data used for analysis.

[0749] A "generative algorithm" is a method that uses machine learning and artificial intelligence technologies to analyze weather data and predict future weather patterns.

[0750] "User" refers to an individual or organization that receives weather forecasts and warning information provided by the system.

[0751] "Opinions" refers to feedback and comments provided by users, including evaluations of the information provided by the system and suggestions for improvement.

[0752] An "emotion analysis engine" is a technology that analyzes user opinions from an emotional perspective and uses that analysis to optimize services and information provision.

[0753] This invention is a system aimed at improving the efficiency of weather forecasting and the user experience. The system consists of three components: a server, a terminal, and a user, each playing a specific role.

[0754] The server plays a central role in this system. The server receives weather data transmitted from terminals and stores it in a database. The stored data is analyzed by advanced machine learning software using generative algorithms. This analysis process allows for the prediction of weather patterns such as precipitation, temperature, and humidity. Using generative AI models, the server provides real-time forecasts and automatically generates warnings and notifications for users.

[0755] Meanwhile, the terminal controls the atmospheric mobile device. This device moves to a designated area and uses sensors to measure detailed weather data. The collected data is securely transmitted to a server using an information transmission system. The terminal also has the function of checking the reliability of the data and filtering out inaccurate data.

[0756] The user is the recipient of predictive information provided by the system. Based on this information, the user can take appropriate actions and provide feedback to the system through a sentiment analysis engine. This feedback includes the accuracy and usefulness of the information, as well as emotional responses.

[0757] For example, heavy rainfall is predicted based on a generative AI model, and the server notifies the user of this information. The user then provides feedback to the system regarding this prediction, which the sentiment analysis engine analyzes to make future notifications more tailored to the user.

[0758] An example of a prompt message would be: "Generate a weather forecast for the specified area this weekend, optimize the notification content to be emotionally sensitive for the user, and create a report that takes user feedback into consideration." This would enable the system to achieve highly accurate predictions and improve user satisfaction.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The terminal controls a high-altitude platform and moves to a designated area. During this process, it uses GPS to determine its precise location and sensors to collect weather data in real time. Input is regional information, and output is weather data such as temperature, humidity, wind speed, and precipitation.

[0762] Step 2:

[0763] The terminal checks the accuracy of the collected weather data. This process executes algorithms to filter out outliers and noise. The input is the collected weather data, and the output is verified, clean data.

[0764] Step 3:

[0765] The terminal transmits weather data to the server using a secure communication method. During this process, the data is encrypted to ensure security. The input is clean weather data, and the output is encrypted data sent to the server.

[0766] Step 4:

[0767] The server receives data sent from the terminal and stores it in the database. The input is encrypted weather data, and the output is the stored data in the database.

[0768] Step 5:

[0769] The server analyzes data using a generative AI model to predict future weather. The input is weather data stored in a database, and the output is the weather forecast result. Machine learning algorithms are applied throughout this process.

[0770] Step 6:

[0771] The server creates a notification message for the user based on the generated weather forecast results. It optimizes the content using prompts. The input is the weather forecast results, and the output is the notification message.

[0772] Step 7:

[0773] Users receive notifications from the server and take action based on them. They also provide feedback to the system, including their opinions and impressions of the prediction results. The input is the notification message, and the output is the user's feedback.

[0774] Step 8:

[0775] The server analyzes user feedback using an emotion analysis engine to optimize the content of future notifications. The input is user feedback, and the output is the adjusted notification settings. This improves the quality of information delivery.

[0776] (Application Example 2)

[0777] 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".

[0778] There is a need to reduce risks and improve service quality by promptly and appropriately notifying both users and service providers of the impact of weather and environmental changes on services and transportation. However, conventional systems have limitations in the accuracy of predictions and the appropriateness of notifications, and they lack optimization of information based on user sentiment and feedback.

[0779] 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.

[0780] In this invention, the server includes means for using a generative model to analyze collected environmental information and perform environmental predictions, means for notifying the user of the prediction results and receiving responses, and means for analyzing the user's responses and adjusting the content of the notifications. This makes it possible to optimize notifications based on the user's emotions and provide more accurate and appropriate information.

[0781] A "high-altitude platform" is a device installed at a high altitude to collect environmental information at a designated location.

[0782] "Environmental information" refers to data related to weather and the surrounding environment, and is a record of specific numerical values ​​and conditions that are the subject of information collection.

[0783] "Communication means" refers to devices or methods used to transmit collected information to recording devices or servers.

[0784] A "recording device" refers to a database or storage device used to store collected information, and is a device that holds information for analysis.

[0785] A "generative model" is a model that includes machine learning algorithms for making predictions based on collected environmental information.

[0786] A "user" is an individual or legal entity that receives weather forecast results or notifications, and is the recipient of the information.

[0787] "Notification" refers to a means of communicating information predicted by a generative model to the user, and includes information visualization and alert functions.

[0788] "Reaction" refers to the response or emotional feedback that a user provides in response to a notification they receive.

[0789] "Means of adjusting notification content" refers to the process or method of changing the content and format of notifications based on user responses and feedback.

[0790] In an embodiment of this invention, the system consists of a server, a terminal, and a user. The server is primarily responsible for information analysis and notification management. The high-altitude platform collects environmental information at various locations using sensors and transmits this information to the terminal using communication means. The terminal receives and temporarily stores the data and transfers the information to the server via the communication line. The terminal also moves the high-altitude platform to a designated location in real time, enabling accurate information collection.

[0791] The server stores the received environmental information in a recording device and then performs information analysis using a generative model incorporating machine learning algorithms. This process yields detailed environmental predictions, which are then notified to the user. The server further utilizes an emotion analysis engine called EmotionEngine to analyze the response data returned by the user and collect feedback information. This allows it to generate optimal notification content tailored to each user's emotions, thereby improving the user experience.

[0792] As a concrete example, applying this system to a food delivery service would allow it to recommend the optimal route to delivery drivers based on predicted rainfall information, and to notify customers in advance of potential delivery delays. Based on user feedback, the delivery time estimates and notification content would be constantly improved. Leveraging the capabilities of the generative AI model, a prompt message could be used to request feedback in the form of, "Please advise on what weather-related information should be prioritized for customer delivery next time."

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The terminal collects environmental information from sensors installed on a high-altitude platform. The sensors measure information such as temperature, humidity, and atmospheric pressure in real time and transmit it to the terminal. As a result, the input to the terminal becomes numerical data representing the physical quantities of the environment.

[0796] Step 2:

[0797] The terminal transfers the collected environmental information to the server via a communication line. During this process, a protocol is used to verify the accuracy and integrity of the data, ensuring secure transmission. The output is the reliable environmental information received by the server.

[0798] Step 3:

[0799] The server stores the received environmental information in a recording device and performs data analysis using a generative model. This generative AI model uses machine learning algorithms to predict weather and environmental conditions. The input is the stored environmental information, and the output is detailed environmental change information as a prediction result.

[0800] Step 4:

[0801] The server notifies the user of the prediction results generated by the generative model. This notification is sent in a format suitable for the user's device and includes optimal route information for delivery drivers and an estimated delivery time for users. The input is the prediction result information, and the output is the notification to the user and delivery driver.

[0802] Step 5:

[0803] Users receive notifications from the server and provide feedback based on the results. This feedback includes evaluations of the notification content and suggestions for improvement. The input is the notification received by the user, and the output is the feedback data.

[0804] Step 6:

[0805] The server uses EmotionEngine to analyze user feedback and evaluate the emotional state. This analysis optimizes the notification content. The input is user feedback, and the output is the improved notification plan.

[0806] Step 7:

[0807] The server adjusts the notification plan for subsequent deliveries based on the analysis, contributing to improved accuracy of the generative model. Feedback such as, "Please advise on how to improve the system by providing prompts such as, 'For the next delivery, what weather-related information should I prioritize communicating to the customer?'" helps to strengthen the model.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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."

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] A means of collecting meteorological data using a high-altitude platform,

[0832] A means of storing collected weather data in a database via communication means,

[0833] A method using a generative model that analyzes stored data and performs weather forecasting,

[0834] A means of notifying users of prediction results and receiving feedback,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the high-altitude platform moves to a designated location in real time and has means for acquiring weather data using sensors.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the generation model comprises means for using a machine learning algorithm to assess the risk of rainfall based on collected weather data and automatically issue a warning.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means of collecting environmental data using a high-altitude platform,

[0843] A means for storing collected environmental data in a storage device via communication means,

[0844] A method using a generative model to analyze stored data and perform environmental predictions,

[0845] A means of notifying users of the prediction results and receiving their feedback,

[0846] A means for controlling the position of a high-altitude platform using a terminal,

[0847] A means of improving the generative model algorithm using user feedback,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, wherein the high-altitude platform is provided with means for immediately moving to a designated location and acquiring environmental data using a detector.

[0851] (Claim 3)

[0852] The system according to claim 1, wherein the generation model comprises means for using a machine learning algorithm to assess the risk of precipitation based on collected environmental data and automatically issue a warning.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] A means of collecting meteorological data using a high-altitude platform,

[0856] A means for storing collected weather data in an information storage device via communication means,

[0857] A method using a generative model that analyzes stored data and performs weather forecasting,

[0858] A means of notifying users of the prediction results and receiving their feedback,

[0859] A means for optimizing travel routes based on weather forecast information,

[0860] A means to automatically adjust the affected process in real time,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, wherein the high-altitude platform moves to a designated location in real time and has means for acquiring weather data using sensors.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the generation model includes means for performing a rainfall risk assessment based on collected weather data using a machine learning algorithm and automatically issuing warnings, and further includes means for providing information to improve the safety and efficiency of the delivery process.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] A means of collecting meteorological data using an atmospheric mobile device,

[0869] A means for storing collected weather data in a data set using an information transmission means,

[0870] A method using a generative algorithm that analyzes stored data and performs weather forecasting,

[0871] A means of notifying users of the prediction results and receiving their feedback,

[0872] A means of analyzing user opinions using an emotion analysis engine and optimizing notification content,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, wherein the atmospheric mobile device moves to a designated location in real time and has means for acquiring weather data using a sensing device.

[0876] (Claim 3)

[0877] The system according to claim 1, wherein the generation algorithm includes means for evaluating the risk of precipitation based on collected weather data using machine learning techniques and automatically issuing warnings.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] A means of collecting environmental information using a high-altitude platform,

[0881] A means for storing collected environmental information in a recording device via communication means,

[0882] A method using a generative model that analyzes stored information and performs environmental predictions,

[0883] A means of notifying users of the prediction results and receiving their responses,

[0884] A means of analyzing user responses and adjusting notification content,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, wherein the high-altitude platform moves to a designated location in real time and has means for acquiring environmental information using a detector.

[0888] (Claim 3)

[0889] The system according to claim 1, wherein the generation model comprises means for performing a rainfall risk assessment based on collected environmental information using a machine learning algorithm and automatically issuing a warning. [Explanation of Symbols]

[0890] 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. A means of collecting meteorological data using a high-altitude platform, A means of storing collected weather data in a database via communication means, A method using a generative model that analyzes stored data and performs weather forecasting, A means of notifying users of prediction results and receiving feedback, A system that includes this.

2. The system according to claim 1, wherein the high-altitude platform moves to a designated location in real time and has means for acquiring weather data using sensors.

3. The system according to claim 1, wherein the generation model comprises means for performing a rainfall risk assessment based on collected weather data using a machine learning algorithm and automatically issuing a warning.

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