System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving weather data transmitted from a plurality of weather observation devices installed in each home via a communication network; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on a result of the analysis means; means for visualizing the weather report on a map; and means for providing the weather report to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current weather forecasting systems target large areas, making it difficult to capture local weather fluctuations, resulting in poor forecast accuracy. Furthermore, the provision of highly accurate real-time weather information is limited, preventing sufficient decision-making support in industries such as agriculture, construction, and events, as well as in everyday life. The purpose of this invention is to solve these problems and provide more accurate, real-time weather information for specific regions. [Means for solving the problem]

[0005] The present invention is a system including: means for receiving weather data transmitted from a plurality of weather observation devices installed in each home via a communication network; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; and means for providing the weather report to a user. The weather observation devices measure atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and include means for storing the weather data obtained using the communication network in a database, thereby enabling local weather information to be provided with high accuracy and in real time.

[0006] A "communications network" is an infrastructure for transmitting and receiving data, and provides a mechanism for exchanging information between multiple devices.

[0007] A "weather observation device" is a device used to measure and collect meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0008] "Weather data" refers to measurements and information related to weather conditions such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0009] "Real-time" means that data is collected, analyzed, and information is provided almost simultaneously, meaning that the information is reflected immediately without delay.

[0010] "Generative artificial intelligence" is a technology that includes machine learning algorithms that automatically analyze collected data and build and operate predictive models.

[0011] "Data analysis means" refers to a processing system that analyzes received data using mathematical and statistical methods and converts the data into information.

[0012] A "weather report" is a document or display containing weather conditions and forecast information for a particular geographic area that is generated based on real-time analysis.

[0013] The "means for visualizing on a map" is a method for illustrating the generated weather report in conjunction with geographic information, and displaying it in a way that is easy for users to understand visually.

[0014] A "database" is an information management system that organizes and stores collected meteorological data in a format that can be later searched and analyzed. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[0037] System configuration

[0038] The system mainly consists of the following three elements:

[0039] 1. Terminal (weather observation equipment)

[0040] 2. Server

[0041] 3. User Interface

[0042] Terminal (weather observation equipment)

[0043] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0044] server

[0045] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0046] Data Transformation: The server transforms the data it receives into a consistent format.

[0047] Data storage: The formatted data is stored in a database.

[0048] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[0049] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0050] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[0051] User Interface

[0052] Users can check real-time weather information visualized on a map through the app or website, and can easily obtain detailed weather information not only for their own area but also for other areas.

[0053] Specific processing flow and example

[0054] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0055] 1. Device operation

[0056] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[0057] 2. Server-side behavior

[0058] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0059] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[0060] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[0061] 3. User Actions

[0062] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[0063] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and supports user decision-making in areas such as agriculture, construction, and event planning.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Data collection is done at regular intervals (for example, every 15 minutes).

[0067] Step 2:

[0068] The terminals then process the collected weather data and transmit it in real time to a server via a communications network. The data is transmitted as data packets.

[0069] Step 3:

[0070] The server receives weather data sent from each terminal via the communication network, and automatically starts the data reception process each time data arrives.

[0071] Step 4:

[0072] The server formats the received weather data and converts it into a consistent format, which is necessary to facilitate data analysis and storage.

[0073] Step 5:

[0074] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[0075] Step 6:

[0076] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and short- and long-term forecasts.

[0077] Step 7:

[0078] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[0079] Step 8:

[0080] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[0081] Step 9:

[0082] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[0083] Step 10:

[0084] Users make decisions based on the weather information provided. For example, agricultural users may adjust their farming schedules based on the weather forecast.

[0085] This series of steps enables the system to provide highly accurate weather information in real time.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Conventional weather information systems often lacked a consistent and efficient process from collecting weather data to analyzing, forecasting, and providing information, making it difficult to provide localized, highly accurate weather information. Furthermore, collected weather data was not stored in a consistent format, which could affect the accuracy of analysis and forecasting. This made it particularly difficult for conventional systems to provide real-time information in fields where localized weather information is important, such as agriculture and construction.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes means for receiving weather data transmitted from a weather observation device, means for formatting the data into a consistent format, means for storing the formatted data in a database, means for analyzing the weather data in real time using generative artificial intelligence to generate current weather conditions and forecasts, means for generating weather reports, and means for visualizing the reports on a map and providing them to users, thereby enabling the provision of highly accurate weather information in real time.

[0091] A "communications network" is an infrastructure for transmitting and receiving data, and includes technologies such as the Internet, Wi-Fi, and LTE.

[0092] A "weather observation device" is a device equipped with sensors for measuring meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0093] A "database" is a system for efficiently storing, searching, and updating structured data, and examples include MySQL and PostgreSQL.

[0094] "Generative AI" refers to an AI technology that learns from large amounts of data and analyzes and predicts new data, and includes machine learning and deep learning algorithms.

[0095] A "weather report" is a report generated based on the analysis of meteorological data, including current weather conditions and short-term and long-term weather forecasts.

[0096] "Visualizing on a map" means visually displaying weather data and analysis results on a map, using technologies such as Google Maps API and Leaflet.js.

[0097] "User" refers to an individual or corporation that receives and uses weather information through this system.

[0098] "Real-time" refers to a situation in which data is collected, transmitted, analyzed, and displayed without delay, resulting in almost instantaneous updates.

[0099] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[0100] System configuration

[0101] This system mainly consists of the following three elements:

[0102] 1. Terminal (weather observation equipment)

[0103] 2. Server

[0104] 3. User Interface

[0105] Terminal (weather observation equipment)

[0106] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0107] server

[0108] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0109] Data shaping: The server shaping the received data into a consistent format using a programming language such as Python and libraries such as Pandas and NumPy.

[0110] Data storage: The formatted data is stored in a database (e.g., MySQL, PostgreSQL).

[0111] Data analysis: The server uses a generative AI model (e.g., OpenAI's GPT-4 model) to analyze the data in real time and generate weather status and forecasts.

[0112] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0113] Data visualization: The generated weather report is visualized on a map and presented to users in a visually easy-to-understand format. The map visualization is done using libraries such as Google Maps API and Leaflet.js.

[0114] User Interface

[0115] Users can check real-time weather information through a smartphone app or website. The interface displays weather information visualized on a map, allowing users to easily obtain detailed weather information for their area of ​​residence or area of ​​interest. This interface is developed using JavaScript frameworks (e.g., React, Vue.js) and mobile app development frameworks (e.g., Flutter, React Native).

[0116] Specific examples

[0117] Usage example (agriculture)

[0118] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0119] 1. Terminal

[0120] At 2:00 p.m., the weather observation device installed in the user's home collects data on air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity, and sends this data to a server via a communications network.

[0121] 2. Server

[0122] The server receives the data sent from the device, formats it, and stores it in a database. The generation AI then analyzes the received data in real time and generates a forecast that rain will start the following morning. The server then creates a detailed weather report based on the forecast results and visualizes it on a map.

[0123] 3. Users

[0124] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[0125] Prompt Sentence Examples

[0126] Here are some example prompts to input to a generative AI model:

[0127] "Data is sent from a weather station installed in a farmer's home at 2:00 PM, and you want to generate a weather forecast for the following morning. Please specify each weather variable and provide details about the weather forecast."

[0128] summary

[0129] The system provides highly accurate, real-time weather information to help users make efficient decisions, and can be applied in a wide range of fields, including agriculture, construction, and event planning.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] The terminal collects and transmits weather data. Specifically, sensors installed on the terminal measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This measurement data is sent to a server via a communications network. The input is local weather data, and the output is data sent to the server.

[0133] Step 2:

[0134] The server receives weather data sent from the terminal. The server analyzes this data and extracts various weather information. The input is the raw data sent from the terminal, and the output is the extracted weather data. Specifically, the server analyzes the data packets and extracts weather information such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0135] Step 3:

[0136] The server formats the received data into a unified format using libraries such as Python, Pandas, and NumPy. The input is extracted weather data, and the output is data in a unified format. Specifically, the server formats each measurement value into JSON format to ensure consistency.

[0137] Step 4:

[0138] The server stores the formatted data in a database. For storage, a database system such as MySQL or PostgreSQL is used. The input is data in a unified format, and the output is data stored in the database. Specifically, the server generates and executes SQL queries to insert the data into the database.

[0139] Step 5:

[0140] The server retrieves data from the database and analyzes it in real time using a generative AI model. GPT-4 and other models are used for the generative AI model. The input is weather data stored in the database, and the output is the analysis results and weather forecast. Specifically, the server inputs a prompt statement into the generative AI model to obtain a weather forecast. A prompt statement such as "Data is sent from a weather observation device installed in a farmer's home at 2 p.m., and please generate a weather forecast for the morning of the following day. Please specify each weather variable and provide details of the weather forecast" is used.

[0141] Step 6:

[0142] The server generates a weather report based on the analysis results of the generative AI model. This report is provided to the user in a format that is easy to understand. The input is the analysis results of the generative AI model, and the output is a detailed weather report. Specifically, the server uses a Python report generation library (e.g., Matplotlib, ReportLab) to create a visually easy-to-understand report.

[0143] Step 7:

[0144] The server visualizes the generated weather report on a map. For visualization, it uses libraries such as Google Maps API and Leaflet.js. The input is a detailed weather report, and the output is weather information visualized on a map. Specifically, the server overlays the weather information on the map data and displays it visually.

[0145] Step 8:

[0146] Users check weather information in real time through smartphone apps or websites. Through the interface, users use the weather information displayed on a map to adjust their schedules and plans. The input is the weather information visualized on the map, and the output is the user's decision. Specifically, users open the app, check the weather forecast for their farmland, and plan their work schedule.

[0147] (Application example 1)

[0148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0149] The present invention aims to provide a system that utilizes real-time weather data from weather observation devices installed in homes and offices to provide highly accurate and detailed weather information, and in particular, to provide a system that can optimize food delivery plans based on weather conditions. Conventional weather forecasting systems are limited to providing local weather information and have difficulty providing detailed planning support for individual deliveries, which has led to a need for improved efficiency for food delivery companies and improved customer satisfaction.

[0150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0151] In this invention, the server includes: means for receiving weather data transmitted via a communication network from multiple weather observation devices installed in homes and offices; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; and means for providing the weather report to a user and dynamically presenting an optimal delivery plan based on the weather information. This enables individual food delivery companies to create optimal delivery plans that respond to weather fluctuations in real time, thereby improving delivery efficiency and customer satisfaction.

[0152] A "communications network" is an infrastructure for digital information transmission that transfers data sent from meteorological observation devices installed in homes and offices to a server.

[0153] A "weather observation device" is a device for measuring meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0154] "Generative artificial intelligence" is a data processing technology that can analyze collected weather data in real time and make predictions.

[0155] "Data analysis means" refers to a function that receives weather data and analyzes that data in real time using generative artificial intelligence.

[0156] A "weather report" is a report summarizing local weather conditions and forecasts generated based on analyzed weather data.

[0157] The "means for visualizing on a map" is a function for visually displaying the generated weather report on a map.

[0158] "Means for presenting delivery plans" is a function that dynamically suggests optimal delivery plans to users based on weather information.

[0159] The system of the present invention consists of three main components: a weather observation device installed in a home or office, a server, and a user interface. This allows it to collect real-time weather data, analyze it using generative artificial intelligence, and provide users with an optimal delivery plan based on the results.

[0160] Weather observation equipment

[0161] Weather observation devices are installed in homes and offices to measure meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. The observation devices collect data at regular intervals (e.g., every 15 minutes) and send it to a server via a communication network. The communication network uses an internet connection such as Wi-Fi, LTE, or 5G.

[0162] server

[0163] The server receives data transmitted from the weather observation device via a communication network and performs the following operations:

[0164] 1. Data reception: Receive weather data through the communication network.

[0165] 2. Data Reformation: Reform the received data into a consistent format and store it in the database.

[0166] 3. Data analysis: Generative artificial intelligence models (e.g., machine learning models implemented in Python) are used to analyze data in real time to generate current weather conditions and forecasts.

[0167] 4. Report Generation: Generate local weather reports based on the analysis results, including current weather conditions, short-term forecasts, and long-term forecasts.

[0168] 5. Data visualization: The generated weather report is visualized on a map, making it easy for users to understand visually.

[0169] User Interface

[0170] Users can access weather information visualized on a map through an application on their smartphone. The user interface provides the following features:

[0171] 1. Display real-time weather information: Display real-time weather information for your current location and specified areas on the map.

[0172] 2. Delivery plan presentation: Dynamically presents optimal delivery plans to users based on weather information, allowing for optimal delivery times and routes that avoid bad weather.

[0173] Specific examples

[0174] For example, if a food delivery company schedules its next delivery for the afternoon, the system works as follows:

[0175] 1. Device side:

[0176] At 2:00 p.m., a weather observation device installed in the office collects weather data every 15 minutes and sends it to a server via a communication network.

[0177] 2. Server side actions:

[0178] The server formats and stores the received data in a database and performs real-time analysis using a generative AI model.

[0179] Based on the analysis, rain is predicted to fall between 3 and 4 p.m.

[0180] The server creates a detailed weather report and visualizes it on a map.

[0181] 3. User Actions:

[0182] The delivery person opens the app, checks the afternoon weather forecast on a map, and uses the analysis to plan the delivery to be completed by 2 p.m.

[0183] Prompt Sentence Examples

[0184] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[0185] In this way, the system can improve the operational efficiency of food delivery companies and increase customer satisfaction by combining real-time weather forecasts with dynamic delivery planning.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] The terminal collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity from weather observation equipment installed in homes and offices at regular intervals (e.g., every 15 minutes) and transmits the data to a server via a communications network.

[0189] Input: Various weather data from weather observation equipment

[0190] Output: Formatted weather data transmitted over a communications network

[0191] Step 2:

[0192] The server receives weather data transmitted via a communication network. Since the received data may contain a mixture of different data formats, it formats it into a consistent format. The formatted data is then stored in a database.

[0193] Input: Weather data sent from the device

[0194] Output: Weather data formatted and stored in a database

[0195] Step 3:

[0196] The server analyzes the formatted weather data in real time using a generative artificial intelligence (generative AI model), which is implemented using a programming language such as Python and uses past and current weather data to make future weather predictions.

[0197] Input: Weather data stored in a database

[0198] Output: Local weather forecast data

[0199] Step 4:

[0200] The server generates a detailed weather report based on the weather forecast data obtained using a generative AI model, including current weather conditions, short-term and long-term weather forecasts.

[0201] Input: Weather forecast data

[0202] Output: Detailed weather report

[0203] Step 5:

[0204] The server visualizes the generated weather reports on a map. This is done using a web mapping platform (e.g., Google Maps API), allowing users to easily check the weather information for a specific area.

[0205] Input: Detailed weather report

[0206] Output: Weather information visualized on a map

[0207] Step 6:

[0208] Users can check real-time weather information visualized on a map through a smartphone application, and the optimal delivery plan is dynamically presented based on the weather information, allowing users to plan delivery schedules that avoid bad weather.

[0209] Input: Weather information visualized on a map

[0210] Output: Optimal delivery plan

[0211] As a concrete example, when a user schedules an afternoon delivery, the application may use the following prompt:

[0212] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[0213] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0214] This invention combines a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information with an emotion engine that recognizes the user's emotions. This system transmits and receives data over a communications network and uses generative artificial intelligence (AI) to analyze and predict the data. In addition, the emotion engine allows the weather information provided to be customized according to the user's emotional state.

[0215] System configuration

[0216] The system mainly consists of the following four elements:

[0217] 1. Terminal (weather observation equipment)

[0218] 2. Server

[0219] 3. Emotion Engine

[0220] 4. User Interface

[0221] Terminal (weather observation equipment)

[0222] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0223] server

[0224] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0225] Data Transformation: The server transforms the data it receives into a consistent format.

[0226] Data storage: The formatted data is stored in a database.

[0227] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[0228] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0229] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[0230] Emotion Engine

[0231] The emotion engine analyzes the user's voice, facial expression, and text data to recognize their emotions. Based on the user's emotions recognized by the emotion engine, the presentation method and content of the weather report are customized. For example, if the user is feeling anxious, the weather information will be presented in a way that gives a sense of security.

[0232] User Interface

[0233] Through the app and website, users can view real-time weather information visualized on a map, as well as weather information customized by the emotion engine.

[0234] Specific processing flow and example

[0235] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0236] 1. Device operation

[0237] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[0238] 2. Server-side behavior

[0239] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0240] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[0241] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[0242] 3. Emotional Engine in Action

[0243] When a user opens the app, the app analyzes their voice and facial expression data to recognize their emotions. For example, if the user looks anxious, the emotion engine will recognize this.

[0244] Based on the user's emotions, the server adjusts the content and wording of the weather report: a user who is feeling anxious will be presented with weather information in a more reassuring manner.

[0245] 4. User Actions

[0246] Users can open the app, check the weather forecast for their farmland on a map, and plan their farming activities based on the forecast to be completed in the morning. At the same time, they can see reassuring reports tailored by the emotion engine.

[0247] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and by providing customized information based on the user's emotions, it supports user decision-making in areas such as agriculture, construction, and event planning.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This data is collected in real time.

[0251] Step 2:

[0252] The terminal formats the collected weather data and transmits it to the server via a communication network. The transmitted data is configured as data packets.

[0253] Step 3:

[0254] The server receives weather data sent from each terminal via a communication network, and processing of the received data begins immediately.

[0255] Step 4:

[0256] The server converts the received weather data into a consistent format that facilitates subsequent analysis and storage in a database.

[0257] Step 5:

[0258] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[0259] Step 6:

[0260] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and forecasts.

[0261] Step 7:

[0262] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[0263] Step 8:

[0264] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[0265] Step 9:

[0266] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[0267] Step 10:

[0268] The emotion engine acquires voice and facial expression data when a user opens the app and analyzes the user's emotions, thereby recognizing the user's emotional state.

[0269] Step 11:

[0270] Based on the user's emotional data analyzed by the emotion engine, the server customizes the content and presentation of the weather report. For example, if the user is feeling anxious, reassuring language will be used.

[0271] Step 12:

[0272] Users receive weather reports that are tailored by the emotion engine, making the weather information more meaningful and useful to users.

[0273] As a concrete example, consider a user who works in the fields in the afternoon.

[0274] 1. At 2:00 p.m., the device measures the air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to the server in real time.

[0275] 2. The server receives this data, stores it in a database, and analyzes it using the generative AI.

[0276] 3. Based on the results of the generation AI, the server creates a forecast of rain the following morning and visualizes it on a map.

[0277] 4. When the user opens the app, the emotion engine analyzes the user's facial expression data and recognizes anxious emotions.

[0278] 5. The server provides a reassuring weather report based on this emotional data.

[0279] 6. The user can plan the next day's farm work in the morning based on the adjusted weather report and carry it out with peace of mind.

[0280] As a result, the present invention not only provides highly accurate weather information, but also adjusts the information according to the user's emotional state, thereby effectively supporting the user's decision-making.

[0281] Example 2

[0282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] Conventional weather forecasting systems lack local accuracy in collecting and analyzing weather data. Furthermore, because they do not take into account the user's emotional state, the information provided may not be properly accepted by the user. Furthermore, it is difficult to achieve both highly accurate real-time forecasts and customized information provision.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0285] In this invention, the server includes: means for receiving weather data transmitted from a plurality of installed weather observation devices via a communication network; means for arranging the weather data into a consistent format and storing it in a database; means for analyzing and predicting the data stored in the database in real time, including a generative artificial intelligence; means for generating a weather report based on the results of the analysis means; means for recognizing a user's emotions using the emotion analysis means and customizing the content and presentation method of the weather report based on the emotions; means for visualizing the customized weather report on a map; and means for providing the weather report to the user. This enables the provision of highly accurate, real-time weather information, and further improves information acceptance and satisfaction by providing customized information according to the user's emotional state.

[0286] "Communication network" refers to the network infrastructure for transmitting and receiving data from weather observation devices installed in homes and locations to a server. Specifically, it includes the Internet, local area networks (LANs), and mobile communication networks.

[0287] A "weather observation device" is a device equipped with sensors and measuring instruments for measuring multiple meteorological parameters, including air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity.

[0288] A "database" is an information management system that converts data sent from meteorological observation equipment into a consistent format and stores it persistently. Specifically, it includes relational database systems (RDBMS) and NoSQL databases.

[0289] "Generative artificial intelligence" refers to artificial intelligence systems that include machine learning algorithms and models for analyzing weather data to predict near-future weather conditions. Specifically, it refers to systems that use deep learning models and recursive neural networks (RNNs).

[0290] "Data analysis means" is a system that includes a processing unit and algorithms for analyzing collected and formatted meteorological data in real time and generating forecast results.

[0291] "Emotion analysis means" includes systems and algorithms for recognizing emotions by analyzing a user's voice data, facial expression data, and text data. Specifically, it refers to emotion recognition APIs and machine learning algorithms.

[0292] A "weather report" is a document containing a weather forecast, current weather conditions, and other related information based on analysis using generative artificial intelligence.

[0293] "Customization" is the process of tailoring and changing the content and presentation of a weather report based on the results of sentiment analysis to suit the user's emotional state.

[0294] "Map visualization means" refers to a system that includes software and interfaces for displaying the contents of weather reports on a map in a geographically understandable manner. Specifically, this refers to a map display library and API.

[0295] "Means for providing to users" includes interfaces and communication means for providing generated weather reports and customized information to users in real time, specifically mobile apps, websites, and notification systems.

[0296] The present invention is a system that collects weather data transmitted from multiple weather observation devices via a communication network, analyzes and forecasts the data in real time, and combines emotion analysis means to provide users with weather reports customized according to their emotions.

[0297] System Overview

[0298] Terminal (weather observation equipment)

[0299] The devices are installed in homes and offices and equipped with sensors to measure air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity. The collected data is sent to a server at regular intervals (for example, every 15 minutes). Communication is via Wi-Fi or mobile networks. Specific examples include connecting sensors to microcontroller boards such as Arduino or Raspberry Pi to collect data.

[0300] server

[0301] The server receives weather data sent from the terminals via a communication network, formats the received data into a consistent format using Python's Pandas library, and filters out invalid data. The formatted data is then stored in a relational database system such as MySQL or PostgreSQL.

[0302] Data Analysis and Generative AI

[0303] The server analyzes and predicts the stored data using a generative AI model. Here, machine learning platforms such as TensorFlow and PyTorch are used to make weather forecasts. Specifically, the collected data set is input into the AI ​​model, which then outputs a forecast. This forecast includes, for example, tomorrow's temperature and precipitation.

[0304] Report Generation

[0305] The server generates weather reports based on the predictions obtained from the generative AI model. The reports include current weather conditions, short-term forecasts, and long-term forecasts. Specifically, the Jinja2 template engine is used to generate the reports in HTML format, which can then be converted to PDF.

[0306] Emotion Engine

[0307] The emotion engine recognizes emotions by collecting and analyzing the user's voice, facial expression, and text data. It uses the Microsoft Azure Emotion API and IBM Watson's emotion API. The collected data is acquired using the microphone and camera on the user's device.

[0308] Report Customization

[0309] The server customizes the content and presentation of the weather report based on the user's emotions recognized by the emotion engine. Specifically, if the user is feeling anxious, the server adds a message to reassure them. For example, it displays a message such as, "Don't worry, tomorrow's weather is predicted to be fine for farm work."

[0310] User Interface

[0311] Users can use the app or website to view weather information visualized on a map. Weather information is plotted on the map using Google Maps API or Leaflet, allowing users to view the information in real time.

[0312] Specific examples

[0313] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0314] 1. Device operation: At 2:00 p.m., a weather observation device installed on farmland collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends it to a server.

[0315] 2. Server behavior:

[0316] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0317] Based on the received data, the generation AI generates a forecast that rain will fall the following morning.

[0318] The server creates a detailed weather report based on the forecast results and displays it on a map.

[0319] 3. Emotion Engine in Action:

[0320] The emotion engine recognizes when a user is expressing anxiety and generates a weather report with a reassuring message.

[0321] 4. User Actions:

[0322] Users open the app, check the weather forecast for their farm, and plan their next day's farm work to be completed in the morning.

[0323] Prompt Sentence Examples

[0324] Below are some examples of prompts to input to the generative AI model.

[0325] "User name: Ichiro Tanaka, Region: Remote location, Date and time: 14:00, October 20, 2023, Data: Pressure 1020hPa, Precipitation 1mm, Illumination 10000lux, Temperature 22degC, Wind direction south, Wind speed 5m / s, Humidity 55%. User's emotional state: Anxiety. Please generate a weather forecast and create a report with reassuring content."

[0326] This enables the system to provide highly accurate weather information in real time and provide customized information according to the user's emotions.

[0327] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0328] Step 1: Data collection

[0329] Subject: Terminal

[0330] The terminal refers to a weather observation device installed in each home or office. The terminal is equipped with sensors to measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Specifically, the terminal's built-in sensors measure data at regular intervals (e.g., every 15 minutes) and temporarily store it in memory. The measurement data is then sent to the server via a communication module (Wi-Fi or mobile network). The input of this step is the measurement data, and the output is the data sent to the server.

[0331] Step 2: Receiving and formatting data

[0332] Subject: Server

[0333] The server receives weather data sent from terminals via a communication network. The received data comes in a variety of formats and must be reshaped into a consistent format. Specifically, the data is converted into a data frame using Python's Pandas library. During reshaping, invalid and missing data is removed to generate clean data. The input of this step is the received data, and the output is the reshaped clean data.

[0334] Step 3: Data storage

[0335] Subject: Server

[0336] The server stores the formatted weather data in a relational database system (for example, MySQL or PostgreSQL). Specifically, it inserts the formatted data into the database using SQL statements. It uses transaction functionality to maintain data consistency and commits only if the insert operation is successful. The input to this step is the formatted data, and the output is the data stored in the database.

[0337] Step 4: Data analysis and prediction

[0338] Subject: Server

[0339] The server analyzes weather data and makes predictions using a generative AI model based on the data stored in the database. Specifically, it uses machine learning platforms such as TensorFlow and PyTorch. Specifically, it inputs the dataset into a deep learning model such as a long short-term memory (LSTM) network to generate a forecast. The input for this step is historical weather data stored in the database, and the output is predicted weather information.

[0340] Step 5: Generate a report

[0341] Subject: Server

[0342] The server generates a weather report based on the forecast results. The report includes the current weather conditions, short-term forecast, and long-term forecast. Specifically, it uses the Jinja2 template engine to create the report in HTML format and serves it in a user-friendly format (e.g., PDF). The input of this step is the forecast results, and the output is the generated weather report.

[0343] Step 6: Collect emotional data

[0344] Subject: User

[0345] A user accesses the system using an app or website. The emotion engine collects the user's voice, facial expression, and text data. Specifically, it acquires data through the user's device's camera and microphone and prepares it to be sent to the emotion recognition API. The input of this step is the user's voice, facial expression, and text data, and the output is the collected emotion data.

[0346] Step 7: Sentiment Analysis

[0347] Subject: Server

[0348] The server analyzes the collected emotion data and recognizes the user's emotional state. Specifically, it uses the Microsoft Azure Emotion API and IBM Watson's emotion recognition API. The server obtains the emotion analysis results and adjusts the content of the weather report accordingly. The input of this step is the collected emotion data, and the output is the analyzed emotional state.

[0349] Step 8: Report Customization

[0350] Subject: Server

[0351] The server adjusts the content and presentation of the weather report based on the results of the emotion analysis. Specifically, it customizes the report by adding a message that reassures anxious users. For example, it creates a report that includes a message such as, "Please rest assured that tomorrow's weather is predicted to be fine for farm work." The input of this step is the analyzed emotional state, and the output is a customized weather report.

[0352] Step 9: Data visualization and display

[0353] Subject: Server

[0354] The server visualizes and displays the customized weather report on a map. Specifically, it plots weather information on the map using a map display library such as Google Maps API or Leaflet. Users can manipulate the map from an app or website and check detailed weather information for the area of ​​interest. The input of this step is the customized weather report, and the output is the weather information visualized on the map.

[0355] Step 10: Provide information

[0356] Subject: User

[0357] Users open an app or website to view weather information visualized on a map and customized weather reports, which allows them to plan their activities. For example, a farmer checks the weather forecast to plan the next day's farm work and adjusts the timing of their activities. The input of this step is the weather information visualized on a map, and the output is the user's confirmation and use of the information.

[0358] (Application example 2)

[0359] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0360] Conventional weather forecasting systems have had problems in providing localized, highly accurate weather information and in being unable to provide customized information according to the user's emotional state. Food delivery services, in particular, are susceptible to the effects of weather, so they are required to provide real-time weather information and provide a sense of security with appropriate messages that respond to the user's emotions. To address these challenges, a solution is needed to improve the quality of the user experience.

[0361] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving weather data transmitted from multiple weather observation devices installed in each home via a communication network; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; means for providing the weather report to a user; means including an emotion engine for recognizing the user's emotional state and customizing information based on the emotional state; and means for displaying information customized by the emotion engine. This makes it possible to provide localized and highly accurate weather information and, in a food delivery service, to provide customized messages according to the user's emotional state.

[0362] definition statement

[0363] A "communications network" is an infrastructure used by multiple devices to send and receive data, including the Internet and leased lines.

[0364] A "weather observation device" is a device used to measure meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0365] "Weather data" refers to data relating to the weather conditions of an environment, including information such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0366] "Real-time" means that data collection and analysis occur almost instantaneously, providing information without time delay.

[0367] "Generative AI" is an AI that automatically analyzes and predicts based on large amounts of data, and is a technology that uses machine learning algorithms and neural networks.

[0368] "Data analysis means" refers to devices and software for analyzing collected meteorological data and extracting useful information.

[0369] A "weather report" is a report of information including current weather conditions, short-term forecasts, and long-term forecasts for a particular area.

[0370] A "visualization tool" is a device or software that displays data in a visual format, such as a chart or map.

[0371] "User" refers to any individual or entity that receives weather information using this system.

[0372] An "emotion engine" is an artificial intelligence that recognizes the user's emotional state from voice, facial expressions, text data, etc., and customizes information based on that.

[0373] A "customization means" is a device or software that adjusts the content and format of the information provided depending on the user's emotional state.

[0374] A "database" is a system for storing organized data and enabling it to be searched and processed efficiently.

[0375] MODE FOR CARRYING OUT THE INVENTION

[0376] The present invention is a system that aims to customize weather information for food delivery according to the user's emotional state. The system consists of the following components:

[0377] communication network

[0378] A communication network, such as the Internet or a dedicated line, is used to receive weather data transmitted from multiple weather observation devices installed in homes.

[0379] Weather observation equipment

[0380] A weather observation device is a device that measures meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity, etc. This data is collected at regular intervals (for example, every 15 minutes) and sent to a server via a communication network.

[0381] server

[0382] The server processes the weather data received via the communication network.

[0383] Data Formatter: Formats received weather data into a consistent format.

[0384] Database: The formatted data is stored in a database.

[0385] Generative artificial intelligence: Analyzes data and generates weather forecasts in real time. This AI uses machine learning algorithms and neural networks.

[0386] Weather report generator: Generates local weather reports based on weather forecasts.

[0387] Visualization: Visually display the weather report on a map.

[0388] Emotion Engine

[0389] The emotion engine recognizes emotions by analyzing the user's voice data, facial expression data, and text data. The engine uses generative artificial intelligence to determine the user's emotional state from the collected data. As the user uses the application, the emotion engine monitors the user's state in real time.

[0390] Customization methods

[0391] It is a way to customize information based on the user's emotional state. The emotion engine will adjust the content and presentation of the weather information provided to the user based on the emotions it recognizes. For example, if the user is feeling anxious, a reassuring message will be displayed.

[0392] User Interface

[0393] The user interface is provided as a smartphone application or website, where users can view real-time weather information visualized on a map, and a customized weather report is displayed using an emotion engine.

[0394] Specific examples

[0395] For example, if a food delivery service user feels anxious about a rainy day, the system works as follows: First, a weather observation device collects data in real time and sends it to the server. The server then uses generative artificial intelligence to generate a weather forecast and combines it with the user's emotional state to customize the weather report. An example of a prompt message is as follows:

[0396] text

[0397] Create a customized message for when your users are feeling "uneasy."

[0398] Create a customized message for when the user is feeling "happy."

[0399] In this way, the system can provide information tailored to the user's emotional state, enhancing their sense of security. It also suggests optimal delivery routes and provides safe delivery plans, improving the quality of service.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Processing step flow

[0402] Step 1:

[0403] The terminal uses a weather observation device to collect weather data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to a server via a communication network. The input is the raw data obtained from the weather observation device, and the output is an organized data format sent to the server.

[0404] Step 2:

[0405] The server receives weather data sent from the terminal via a communication network. The received data is formatted into a consistent format using a data formatting means. This formatting process involves filling in missing values ​​and correcting outliers. The output is the formatted weather data.

[0406] Step 3:

[0407] The server stores the formatted weather data in a database. During this process, the data is indexed, which is designed to enable fast searches. The input is the formatted weather data, and the output is the data stored in the database.

[0408] Step 4:

[0409] The server uses generative artificial intelligence to analyze meteorological data in real time and generate weather forecasts. Machine learning algorithms and neural networks are used for the analysis, and predictions are made by combining past and current data. The input is meteorological data retrieved from a database, and the output is a weather forecast.

[0410] Step 5:

[0411] The server generates a local weather report based on the weather forecast. The weather report contains the necessary information to provide to the user, such as current weather conditions, short-term forecast, and long-term forecast. The input is the generated weather forecast, and the output is the weather report.

[0412] Step 6:

[0413] The server visualizes the weather report on a map. A data visualization tool is used to visually display weather conditions and forecast information on a map. The input is the weather report and the output is the visual information displayed on the map.

[0414] Step 7:

[0415] When a user uses the application to check weather information, the emotion engine analyzes the user's voice data, facial expression data, and text data to recognize emotions. The emotion engine determines the user's emotional state in real time and customizes information based on that. The input is the user's voice data, facial expression data, etc., and the output is the recognized emotional state.

[0416] Step 8:

[0417] The server customizes the content of the weather report based on the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, a reassuring message is added. The input is the emotional state and the weather report from the emotion engine, and the output is a customized weather report.

[0418] Step 9:

[0419] A user views a customized weather report in an application that allows the user to receive weather information along with a customized message based on their emotional state. The input is the customized weather report and the output is the information displayed to the user.

[0420] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0421] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0423] [Second embodiment]

[0424] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0425] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0426] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0427] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0428] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0429] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0430] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0431] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0432] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0433] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0434] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0435] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0436] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[0437] System configuration

[0438] The system mainly consists of the following three elements:

[0439] 1. Terminal (weather observation equipment)

[0440] 2. Server

[0441] 3. User Interface

[0442] Terminal (weather observation equipment)

[0443] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0444] server

[0445] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0446] Data Transformation: The server transforms the data it receives into a consistent format.

[0447] Data storage: The formatted data is stored in a database.

[0448] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[0449] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0450] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[0451] User Interface

[0452] Users can check real-time weather information visualized on a map through the app or website, and can easily obtain detailed weather information not only for their own area but also for other areas.

[0453] Specific processing flow and example

[0454] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0455] 1. Device operation

[0456] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[0457] 2. Server-side behavior

[0458] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0459] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[0460] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[0461] 3. User Actions

[0462] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[0463] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and supports user decision-making in areas such as agriculture, construction, and event planning.

[0464] The processing flow will be explained below.

[0465] Step 1:

[0466] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Data collection is done at regular intervals (for example, every 15 minutes).

[0467] Step 2:

[0468] The terminals then process the collected weather data and transmit it in real time to a server via a communications network. The data is transmitted as data packets.

[0469] Step 3:

[0470] The server receives weather data sent from each terminal via the communication network, and automatically starts the data reception process each time data arrives.

[0471] Step 4:

[0472] The server formats the received weather data and converts it into a consistent format, which is necessary to facilitate data analysis and storage.

[0473] Step 5:

[0474] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[0475] Step 6:

[0476] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and short- and long-term forecasts.

[0477] Step 7:

[0478] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[0479] Step 8:

[0480] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[0481] Step 9:

[0482] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[0483] Step 10:

[0484] Users make decisions based on the weather information provided. For example, agricultural users may adjust their farming schedules based on the weather forecast.

[0485] This series of steps enables the system to provide highly accurate weather information in real time.

[0486] Example 1

[0487] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] Conventional weather information systems often lacked a consistent and efficient process from collecting weather data to analyzing, forecasting, and providing information, making it difficult to provide localized, highly accurate weather information. Furthermore, collected weather data was not stored in a consistent format, which could affect the accuracy of analysis and forecasting. This made it particularly difficult for conventional systems to provide real-time information in fields where localized weather information is important, such as agriculture and construction.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0490] In this invention, the server includes means for receiving weather data transmitted from a weather observation device, means for formatting the data into a consistent format, means for storing the formatted data in a database, means for analyzing the weather data in real time using generative artificial intelligence to generate current weather conditions and forecasts, means for generating weather reports, and means for visualizing the reports on a map and providing them to users, thereby enabling the provision of highly accurate weather information in real time.

[0491] A "communications network" is an infrastructure for transmitting and receiving data, and includes technologies such as the Internet, Wi-Fi, and LTE.

[0492] A "weather observation device" is a device equipped with sensors for measuring meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0493] A "database" is a system for efficiently storing, searching, and updating structured data, and examples include MySQL and PostgreSQL.

[0494] "Generative AI" refers to an AI technology that learns from large amounts of data and analyzes and predicts new data, and includes machine learning and deep learning algorithms.

[0495] A "weather report" is a report generated based on the analysis of meteorological data, including current weather conditions and short-term and long-term weather forecasts.

[0496] "Visualizing on a map" means visually displaying weather data and analysis results on a map, using technologies such as Google Maps API and Leaflet.js.

[0497] "User" refers to an individual or corporation that receives and uses weather information through this system.

[0498] "Real-time" refers to a situation in which data is collected, transmitted, analyzed, and displayed without delay, resulting in almost instantaneous updates.

[0499] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[0500] System configuration

[0501] This system mainly consists of the following three elements:

[0502] 1. Terminal (weather observation equipment)

[0503] 2. Server

[0504] 3. User Interface

[0505] Terminal (weather observation equipment)

[0506] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0507] server

[0508] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0509] Data shaping: The server shaping the received data into a consistent format using a programming language such as Python and libraries such as Pandas and NumPy.

[0510] Data storage: The formatted data is stored in a database (e.g., MySQL, PostgreSQL).

[0511] Data analysis: The server uses a generative AI model (e.g., OpenAI's GPT-4 model) to analyze the data in real time and generate weather status and forecasts.

[0512] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0513] Data visualization: The generated weather report is visualized on a map and presented to users in a visually easy-to-understand format. The map visualization is done using libraries such as Google Maps API and Leaflet.js.

[0514] User Interface

[0515] Users can check real-time weather information through a smartphone app or website. The interface displays weather information visualized on a map, allowing users to easily obtain detailed weather information for their area of ​​residence or area of ​​interest. This interface is developed using JavaScript frameworks (e.g., React, Vue.js) and mobile app development frameworks (e.g., Flutter, React Native).

[0516] Specific examples

[0517] Usage example (agriculture)

[0518] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0519] 1. Terminal

[0520] At 2:00 p.m., the weather observation device installed in the user's home collects data on air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity, and sends this data to a server via a communications network.

[0521] 2. Server

[0522] The server receives the data sent from the device, formats it, and stores it in a database. The generation AI then analyzes the received data in real time and generates a forecast that rain will start the following morning. The server then creates a detailed weather report based on the forecast results and visualizes it on a map.

[0523] 3. Users

[0524] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[0525] Prompt Sentence Examples

[0526] Here are some example prompts to input to a generative AI model:

[0527] "Data is sent from a weather station installed in a farmer's home at 2:00 PM, and you want to generate a weather forecast for the following morning. Please specify each weather variable and provide details about the weather forecast."

[0528] summary

[0529] The system provides highly accurate, real-time weather information to help users make efficient decisions, and can be applied in a wide range of fields, including agriculture, construction, and event planning.

[0530] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0531] Step 1:

[0532] The terminal collects and transmits weather data. Specifically, sensors installed on the terminal measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This measurement data is sent to a server via a communications network. The input is local weather data, and the output is data sent to the server.

[0533] Step 2:

[0534] The server receives weather data sent from the terminal. The server analyzes this data and extracts various weather information. The input is the raw data sent from the terminal, and the output is the extracted weather data. Specifically, the server analyzes the data packets and extracts weather information such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0535] Step 3:

[0536] The server formats the received data into a unified format using libraries such as Python, Pandas, and NumPy. The input is extracted weather data, and the output is data in a unified format. Specifically, the server formats each measurement value into JSON format to ensure consistency.

[0537] Step 4:

[0538] The server stores the formatted data in a database. For storage, a database system such as MySQL or PostgreSQL is used. The input is data in a unified format, and the output is data stored in the database. Specifically, the server generates and executes SQL queries to insert the data into the database.

[0539] Step 5:

[0540] The server retrieves data from the database and analyzes it in real time using a generative AI model. GPT-4 and other models are used for the generative AI model. The input is weather data stored in the database, and the output is the analysis results and weather forecast. Specifically, the server inputs a prompt statement into the generative AI model to obtain a weather forecast. A prompt statement such as "Data is sent from a weather observation device installed in a farmer's home at 2 p.m., and please generate a weather forecast for the morning of the following day. Please specify each weather variable and provide details of the weather forecast" is used.

[0541] Step 6:

[0542] The server generates a weather report based on the analysis results of the generative AI model. This report is provided to the user in a format that is easy to understand. The input is the analysis results of the generative AI model, and the output is a detailed weather report. Specifically, the server uses a Python report generation library (e.g., Matplotlib, ReportLab) to create a visually easy-to-understand report.

[0543] Step 7:

[0544] The server visualizes the generated weather report on a map. For visualization, it uses libraries such as Google Maps API and Leaflet.js. The input is a detailed weather report, and the output is weather information visualized on a map. Specifically, the server overlays the weather information on the map data and displays it visually.

[0545] Step 8:

[0546] Users check weather information in real time through smartphone apps or websites. Through the interface, users use the weather information displayed on a map to adjust their schedules and plans. The input is the weather information visualized on the map, and the output is the user's decision. Specifically, users open the app, check the weather forecast for their farmland, and plan their work schedule.

[0547] (Application example 1)

[0548] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0549] The present invention aims to provide a system that utilizes real-time weather data from weather observation devices installed in homes and offices to provide highly accurate and detailed weather information, and in particular, to provide a system that can optimize food delivery plans based on weather conditions. Conventional weather forecasting systems are limited to providing local weather information and have difficulty providing detailed planning support for individual deliveries, which has led to a need for improved efficiency for food delivery companies and improved customer satisfaction.

[0550] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0551] In this invention, the server includes: means for receiving weather data transmitted via a communication network from multiple weather observation devices installed in homes and offices; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; and means for providing the weather report to a user and dynamically presenting an optimal delivery plan based on the weather information. This enables individual food delivery companies to create optimal delivery plans that respond to weather fluctuations in real time, thereby improving delivery efficiency and customer satisfaction.

[0552] A "communications network" is an infrastructure for digital information transmission that transfers data sent from meteorological observation devices installed in homes and offices to a server.

[0553] A "weather observation device" is a device for measuring meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0554] "Generative artificial intelligence" is a data processing technology that can analyze collected weather data in real time and make predictions.

[0555] "Data analysis means" refers to a function that receives weather data and analyzes that data in real time using generative artificial intelligence.

[0556] A "weather report" is a report summarizing local weather conditions and forecasts generated based on analyzed weather data.

[0557] The "means for visualizing on a map" is a function for visually displaying the generated weather report on a map.

[0558] "Means for presenting delivery plans" is a function that dynamically suggests optimal delivery plans to users based on weather information.

[0559] The system of the present invention consists of three main components: a weather observation device installed in a home or office, a server, and a user interface. This allows it to collect real-time weather data, analyze it using generative artificial intelligence, and provide users with an optimal delivery plan based on the results.

[0560] Weather observation equipment

[0561] Weather observation devices are installed in homes and offices to measure meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. The observation devices collect data at regular intervals (e.g., every 15 minutes) and send it to a server via a communication network. The communication network uses an internet connection such as Wi-Fi, LTE, or 5G.

[0562] server

[0563] The server receives data transmitted from the weather observation device via a communication network and performs the following operations:

[0564] 1. Data reception: Receive weather data through the communication network.

[0565] 2. Data Reformation: Reform the received data into a consistent format and store it in the database.

[0566] 3. Data analysis: Generative artificial intelligence models (e.g., machine learning models implemented in Python) are used to analyze data in real time to generate current weather conditions and forecasts.

[0567] 4. Report Generation: Generate local weather reports based on the analysis results, including current weather conditions, short-term forecasts, and long-term forecasts.

[0568] 5. Data visualization: The generated weather report is visualized on a map, making it easy for users to understand visually.

[0569] User Interface

[0570] Users can access weather information visualized on a map through an application on their smartphone. The user interface provides the following features:

[0571] 1. Display real-time weather information: Display real-time weather information for your current location and specified areas on the map.

[0572] 2. Delivery plan presentation: Dynamically presents optimal delivery plans to users based on weather information, allowing for optimal delivery times and routes that avoid bad weather.

[0573] Specific examples

[0574] For example, if a food delivery company schedules its next delivery for the afternoon, the system works as follows:

[0575] 1. Device side:

[0576] At 2:00 p.m., a weather observation device installed in the office collects weather data every 15 minutes and sends it to a server via a communication network.

[0577] 2. Server side actions:

[0578] The server formats and stores the received data in a database and performs real-time analysis using a generative AI model.

[0579] Based on the analysis, rain is predicted to fall between 3 and 4 p.m.

[0580] The server creates a detailed weather report and visualizes it on a map.

[0581] 3. User Actions:

[0582] The delivery person opens the app, checks the afternoon weather forecast on a map, and uses the analysis to plan the delivery to be completed by 2 p.m.

[0583] Prompt Sentence Examples

[0584] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[0585] In this way, the system can improve the operational efficiency of food delivery companies and increase customer satisfaction by combining real-time weather forecasts with dynamic delivery planning.

[0586] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0587] Step 1:

[0588] The terminal collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity from weather observation equipment installed in homes and offices at regular intervals (e.g., every 15 minutes) and transmits the data to a server via a communications network.

[0589] Input: Various weather data from weather observation equipment

[0590] Output: Formatted weather data transmitted over a communications network

[0591] Step 2:

[0592] The server receives weather data transmitted via a communication network. Since the received data may contain a mixture of different data formats, it formats it into a consistent format. The formatted data is then stored in a database.

[0593] Input: Weather data sent from the device

[0594] Output: Weather data formatted and stored in a database

[0595] Step 3:

[0596] The server analyzes the formatted weather data in real time using a generative artificial intelligence (generative AI model), which is implemented using a programming language such as Python and uses past and current weather data to make future weather predictions.

[0597] Input: Weather data stored in a database

[0598] Output: Local weather forecast data

[0599] Step 4:

[0600] The server generates a detailed weather report based on the weather forecast data obtained using a generative AI model, including current weather conditions, short-term and long-term weather forecasts.

[0601] Input: Weather forecast data

[0602] Output: Detailed weather report

[0603] Step 5:

[0604] The server visualizes the generated weather reports on a map. This is done using a web mapping platform (e.g., Google Maps API), allowing users to easily check the weather information for a specific area.

[0605] Input: Detailed weather report

[0606] Output: Weather information visualized on a map

[0607] Step 6:

[0608] Users can check real-time weather information visualized on a map through a smartphone application, and the optimal delivery plan is dynamically presented based on the weather information, allowing users to plan delivery schedules that avoid bad weather.

[0609] Input: Weather information visualized on a map

[0610] Output: Optimal delivery plan

[0611] As a concrete example, when a user schedules an afternoon delivery, the application may use the following prompt:

[0612] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[0613] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0614] This invention combines a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information with an emotion engine that recognizes the user's emotions. This system transmits and receives data over a communications network and uses generative artificial intelligence (AI) to analyze and predict the data. In addition, the emotion engine allows the weather information provided to be customized according to the user's emotional state.

[0615] System configuration

[0616] The system mainly consists of the following four elements:

[0617] 1. Terminal (weather observation equipment)

[0618] 2. Server

[0619] 3. Emotion Engine

[0620] 4. User Interface

[0621] Terminal (weather observation equipment)

[0622] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0623] server

[0624] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0625] Data Transformation: The server transforms the data it receives into a consistent format.

[0626] Data storage: The formatted data is stored in a database.

[0627] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[0628] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0629] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[0630] Emotion Engine

[0631] The emotion engine analyzes the user's voice, facial expression, and text data to recognize their emotions. Based on the user's emotions recognized by the emotion engine, the presentation method and content of the weather report are customized. For example, if the user is feeling anxious, the weather information will be presented in a way that gives a sense of security.

[0632] User Interface

[0633] Through the app and website, users can view real-time weather information visualized on a map, as well as weather information customized by the emotion engine.

[0634] Specific processing flow and example

[0635] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0636] 1. Device operation

[0637] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[0638] 2. Server-side behavior

[0639] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0640] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[0641] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[0642] 3. Emotional Engine in Action

[0643] When a user opens the app, the app analyzes their voice and facial expression data to recognize their emotions. For example, if the user looks anxious, the emotion engine will recognize this.

[0644] Based on the user's emotions, the server adjusts the content and wording of the weather report: a user who is feeling anxious will be presented with weather information in a more reassuring manner.

[0645] 4. User Actions

[0646] Users can open the app, check the weather forecast for their farmland on a map, and plan their farming activities based on the forecast to be completed in the morning. At the same time, they can see reassuring reports tailored by the emotion engine.

[0647] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and by providing customized information based on the user's emotions, it supports user decision-making in areas such as agriculture, construction, and event planning.

[0648] The processing flow will be explained below.

[0649] Step 1:

[0650] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This data is collected in real time.

[0651] Step 2:

[0652] The terminal formats the collected weather data and transmits it to the server via a communication network. The transmitted data is configured as data packets.

[0653] Step 3:

[0654] The server receives weather data sent from each terminal via a communication network, and processing of the received data begins immediately.

[0655] Step 4:

[0656] The server converts the received weather data into a consistent format that facilitates subsequent analysis and storage in a database.

[0657] Step 5:

[0658] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[0659] Step 6:

[0660] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and forecasts.

[0661] Step 7:

[0662] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[0663] Step 8:

[0664] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[0665] Step 9:

[0666] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[0667] Step 10:

[0668] The emotion engine acquires voice and facial expression data when a user opens the app and analyzes the user's emotions, thereby recognizing the user's emotional state.

[0669] Step 11:

[0670] Based on the user's emotional data analyzed by the emotion engine, the server customizes the content and presentation of the weather report. For example, if the user is feeling anxious, reassuring language will be used.

[0671] Step 12:

[0672] Users receive weather reports that are tailored by the emotion engine, making the weather information more meaningful and useful to users.

[0673] As a concrete example, consider a user who works in the fields in the afternoon.

[0674] 1. At 2:00 p.m., the device measures the air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to the server in real time.

[0675] 2. The server receives this data, stores it in a database, and analyzes it using the generative AI.

[0676] 3. Based on the results of the generation AI, the server creates a forecast of rain the following morning and visualizes it on a map.

[0677] 4. When the user opens the app, the emotion engine analyzes the user's facial expression data and recognizes anxious emotions.

[0678] 5. The server provides a reassuring weather report based on this emotional data.

[0679] 6. The user can plan the next day's farm work in the morning based on the adjusted weather report and carry it out with peace of mind.

[0680] As a result, the present invention not only provides highly accurate weather information, but also adjusts the information according to the user's emotional state, thereby effectively supporting the user's decision-making.

[0681] Example 2

[0682] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0683] Conventional weather forecasting systems lack local accuracy in collecting and analyzing weather data. Furthermore, because they do not take into account the user's emotional state, the information provided may not be properly accepted by the user. Furthermore, it is difficult to achieve both highly accurate real-time forecasts and customized information provision.

[0684] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0685] In this invention, the server includes: means for receiving weather data transmitted from a plurality of installed weather observation devices via a communication network; means for arranging the weather data into a consistent format and storing it in a database; means for analyzing and predicting the data stored in the database in real time, including a generative artificial intelligence; means for generating a weather report based on the results of the analysis means; means for recognizing a user's emotions using the emotion analysis means and customizing the content and presentation method of the weather report based on the emotions; means for visualizing the customized weather report on a map; and means for providing the weather report to the user. This enables the provision of highly accurate, real-time weather information, and further improves information acceptance and satisfaction by providing customized information according to the user's emotional state.

[0686] "Communication network" refers to the network infrastructure for transmitting and receiving data from weather observation devices installed in homes and locations to a server. Specifically, it includes the Internet, local area networks (LANs), and mobile communication networks.

[0687] A "weather observation device" is a device equipped with sensors and measuring instruments for measuring multiple meteorological parameters, including air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity.

[0688] A "database" is an information management system that converts data sent from meteorological observation equipment into a consistent format and stores it persistently. Specifically, it includes relational database systems (RDBMS) and NoSQL databases.

[0689] "Generative artificial intelligence" refers to artificial intelligence systems that include machine learning algorithms and models for analyzing weather data to predict near-future weather conditions. Specifically, it refers to systems that use deep learning models and recursive neural networks (RNNs).

[0690] "Data analysis means" is a system that includes a processing unit and algorithms for analyzing collected and formatted meteorological data in real time and generating forecast results.

[0691] "Emotion analysis means" includes systems and algorithms for recognizing emotions by analyzing a user's voice data, facial expression data, and text data. Specifically, it refers to emotion recognition APIs and machine learning algorithms.

[0692] A "weather report" is a document containing a weather forecast, current weather conditions, and other related information based on analysis using generative artificial intelligence.

[0693] "Customization" is the process of tailoring and changing the content and presentation of a weather report based on the results of sentiment analysis to suit the user's emotional state.

[0694] "Map visualization means" refers to a system that includes software and interfaces for displaying the contents of weather reports on a map in a geographically understandable manner. Specifically, this refers to a map display library and API.

[0695] "Means for providing to users" includes interfaces and communication means for providing generated weather reports and customized information to users in real time, specifically mobile apps, websites, and notification systems.

[0696] The present invention is a system that collects weather data transmitted from multiple weather observation devices via a communication network, analyzes and forecasts the data in real time, and combines emotion analysis means to provide users with weather reports customized according to their emotions.

[0697] System Overview

[0698] Terminal (weather observation equipment)

[0699] The devices are installed in homes and offices and equipped with sensors to measure air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity. The collected data is sent to a server at regular intervals (for example, every 15 minutes). Communication is via Wi-Fi or mobile networks. Specific examples include connecting sensors to microcontroller boards such as Arduino or Raspberry Pi to collect data.

[0700] server

[0701] The server receives weather data sent from the terminals via a communication network, formats the received data into a consistent format using Python's Pandas library, and filters out invalid data. The formatted data is then stored in a relational database system such as MySQL or PostgreSQL.

[0702] Data Analysis and Generative AI

[0703] The server analyzes and predicts the stored data using a generative AI model. Here, machine learning platforms such as TensorFlow and PyTorch are used to make weather forecasts. Specifically, the collected data set is input into the AI ​​model, which then outputs a forecast. This forecast includes, for example, tomorrow's temperature and precipitation.

[0704] Report Generation

[0705] The server generates weather reports based on the predictions obtained from the generative AI model. The reports include current weather conditions, short-term forecasts, and long-term forecasts. Specifically, the Jinja2 template engine is used to generate the reports in HTML format, which can then be converted to PDF.

[0706] Emotion Engine

[0707] The emotion engine recognizes emotions by collecting and analyzing the user's voice, facial expression, and text data. It uses the Microsoft Azure Emotion API and IBM Watson's emotion API. The collected data is acquired using the microphone and camera on the user's device.

[0708] Report Customization

[0709] The server customizes the content and presentation of the weather report based on the user's emotions recognized by the emotion engine. Specifically, if the user is feeling anxious, the server adds a message to reassure them. For example, it displays a message such as, "Don't worry, tomorrow's weather is predicted to be fine for farm work."

[0710] User Interface

[0711] Users can use the app or website to view weather information visualized on a map. Weather information is plotted on the map using Google Maps API or Leaflet, allowing users to view the information in real time.

[0712] Specific examples

[0713] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0714] 1. Device operation: At 2:00 p.m., a weather observation device installed on farmland collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends it to a server.

[0715] 2. Server behavior:

[0716] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0717] Based on the received data, the generation AI generates a forecast that rain will fall the following morning.

[0718] The server creates a detailed weather report based on the forecast results and displays it on a map.

[0719] 3. Emotion Engine in Action:

[0720] The emotion engine recognizes when a user is expressing anxiety and generates a weather report with a reassuring message.

[0721] 4. User Actions:

[0722] Users open the app, check the weather forecast for their farm, and plan their next day's farm work to be completed in the morning.

[0723] Prompt Sentence Examples

[0724] Below are some examples of prompts to input to the generative AI model.

[0725] "User name: Ichiro Tanaka, Region: Remote location, Date and time: 14:00, October 20, 2023, Data: Pressure 1020hPa, Precipitation 1mm, Illumination 10000lux, Temperature 22degC, Wind direction south, Wind speed 5m / s, Humidity 55%. User's emotional state: Anxiety. Please generate a weather forecast and create a report with reassuring content."

[0726] This enables the system to provide highly accurate weather information in real time and provide customized information according to the user's emotions.

[0727] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0728] Step 1: Data collection

[0729] Subject: Terminal

[0730] The terminal refers to a weather observation device installed in each home or office. The terminal is equipped with sensors to measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Specifically, the terminal's built-in sensors measure data at regular intervals (e.g., every 15 minutes) and temporarily store it in memory. The measurement data is then sent to the server via a communication module (Wi-Fi or mobile network). The input of this step is the measurement data, and the output is the data sent to the server.

[0731] Step 2: Receiving and formatting data

[0732] Subject: Server

[0733] The server receives weather data sent from terminals via a communication network. The received data comes in a variety of formats and must be reshaped into a consistent format. Specifically, the data is converted into a data frame using Python's Pandas library. During reshaping, invalid and missing data is removed to generate clean data. The input of this step is the received data, and the output is the reshaped clean data.

[0734] Step 3: Data storage

[0735] Subject: Server

[0736] The server stores the formatted weather data in a relational database system (for example, MySQL or PostgreSQL). Specifically, it inserts the formatted data into the database using SQL statements. It uses transaction functionality to maintain data consistency and commits only if the insert operation is successful. The input to this step is the formatted data, and the output is the data stored in the database.

[0737] Step 4: Data analysis and prediction

[0738] Subject: Server

[0739] The server analyzes weather data and makes predictions using a generative AI model based on the data stored in the database. Specifically, it uses machine learning platforms such as TensorFlow and PyTorch. Specifically, it inputs the dataset into a deep learning model such as a long short-term memory (LSTM) network to generate a forecast. The input for this step is historical weather data stored in the database, and the output is predicted weather information.

[0740] Step 5: Generate a report

[0741] Subject: Server

[0742] The server generates a weather report based on the forecast results. The report includes the current weather conditions, short-term forecast, and long-term forecast. Specifically, it uses the Jinja2 template engine to create the report in HTML format and serves it in a user-friendly format (e.g., PDF). The input of this step is the forecast results, and the output is the generated weather report.

[0743] Step 6: Collect emotional data

[0744] Subject: User

[0745] A user accesses the system using an app or website. The emotion engine collects the user's voice, facial expression, and text data. Specifically, it acquires data through the user's device's camera and microphone and prepares it to be sent to the emotion recognition API. The input of this step is the user's voice, facial expression, and text data, and the output is the collected emotion data.

[0746] Step 7: Sentiment Analysis

[0747] Subject: Server

[0748] The server analyzes the collected emotion data and recognizes the user's emotional state. Specifically, it uses the Microsoft Azure Emotion API and IBM Watson's emotion recognition API. The server obtains the emotion analysis results and adjusts the content of the weather report accordingly. The input of this step is the collected emotion data, and the output is the analyzed emotional state.

[0749] Step 8: Report Customization

[0750] Subject: Server

[0751] The server adjusts the content and presentation of the weather report based on the results of the emotion analysis. Specifically, it customizes the report by adding a message that reassures anxious users. For example, it creates a report that includes a message such as, "Please rest assured that tomorrow's weather is predicted to be fine for farm work." The input of this step is the analyzed emotional state, and the output is a customized weather report.

[0752] Step 9: Data visualization and display

[0753] Subject: Server

[0754] The server visualizes and displays the customized weather report on a map. Specifically, it plots weather information on the map using a map display library such as Google Maps API or Leaflet. Users can manipulate the map from an app or website and check detailed weather information for the area of ​​interest. The input of this step is the customized weather report, and the output is the weather information visualized on the map.

[0755] Step 10: Provide information

[0756] Subject: User

[0757] Users open an app or website to view weather information visualized on a map and customized weather reports, which allows them to plan their activities. For example, a farmer checks the weather forecast to plan the next day's farm work and adjusts the timing of their activities. The input of this step is the weather information visualized on a map, and the output is the user's confirmation and use of the information.

[0758] (Application example 2)

[0759] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0760] Conventional weather forecasting systems have had problems in providing localized, highly accurate weather information and in being unable to provide customized information according to the user's emotional state. Food delivery services, in particular, are susceptible to the effects of weather, so they are required to provide real-time weather information and provide a sense of security with appropriate messages that respond to the user's emotions. To address these challenges, a solution is needed to improve the quality of the user experience.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving weather data transmitted from multiple weather observation devices installed in each home via a communication network; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; means for providing the weather report to a user; means including an emotion engine for recognizing the user's emotional state and customizing information based on the emotional state; and means for displaying information customized by the emotion engine. This makes it possible to provide localized and highly accurate weather information and, in a food delivery service, to provide customized messages according to the user's emotional state.

[0762] definition statement

[0763] A "communications network" is an infrastructure used by multiple devices to send and receive data, including the Internet and leased lines.

[0764] A "weather observation device" is a device used to measure meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0765] "Weather data" refers to data relating to the weather conditions of an environment, including information such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0766] "Real-time" means that data collection and analysis occur almost instantaneously, providing information without time delay.

[0767] "Generative AI" is an AI that automatically analyzes and predicts based on large amounts of data, and is a technology that uses machine learning algorithms and neural networks.

[0768] "Data analysis means" refers to devices and software for analyzing collected meteorological data and extracting useful information.

[0769] A "weather report" is a report of information including current weather conditions, short-term forecasts, and long-term forecasts for a particular area.

[0770] A "visualization tool" is a device or software that displays data in a visual format, such as a chart or map.

[0771] "User" refers to any individual or entity that receives weather information using this system.

[0772] An "emotion engine" is an artificial intelligence that recognizes the user's emotional state from voice, facial expressions, text data, etc., and customizes information based on that.

[0773] A "customization means" is a device or software that adjusts the content and format of the information provided depending on the user's emotional state.

[0774] A "database" is a system for storing organized data and enabling it to be searched and processed efficiently.

[0775] MODE FOR CARRYING OUT THE INVENTION

[0776] The present invention is a system that aims to customize weather information for food delivery according to the user's emotional state. The system consists of the following components:

[0777] communication network

[0778] A communication network, such as the Internet or a dedicated line, is used to receive weather data transmitted from multiple weather observation devices installed in homes.

[0779] Weather observation equipment

[0780] A weather observation device is a device that measures meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity, etc. This data is collected at regular intervals (for example, every 15 minutes) and sent to a server via a communication network.

[0781] server

[0782] The server processes the weather data received via the communication network.

[0783] Data Formatter: Formats received weather data into a consistent format.

[0784] Database: The formatted data is stored in a database.

[0785] Generative artificial intelligence: Analyzes data and generates weather forecasts in real time. This AI uses machine learning algorithms and neural networks.

[0786] Weather report generator: Generates local weather reports based on weather forecasts.

[0787] Visualization: Visually display the weather report on a map.

[0788] Emotion Engine

[0789] The emotion engine recognizes emotions by analyzing the user's voice data, facial expression data, and text data. The engine uses generative artificial intelligence to determine the user's emotional state from the collected data. As the user uses the application, the emotion engine monitors the user's state in real time.

[0790] Customization methods

[0791] It is a way to customize information based on the user's emotional state. The emotion engine will adjust the content and presentation of the weather information provided to the user based on the emotions it recognizes. For example, if the user is feeling anxious, a reassuring message will be displayed.

[0792] User Interface

[0793] The user interface is provided as a smartphone application or website, where users can view real-time weather information visualized on a map, and a customized weather report is displayed using an emotion engine.

[0794] Specific examples

[0795] For example, if a food delivery service user feels anxious about a rainy day, the system works as follows: First, a weather observation device collects data in real time and sends it to the server. The server then uses generative artificial intelligence to generate a weather forecast and combines it with the user's emotional state to customize the weather report. An example of a prompt message is as follows:

[0796] text

[0797] Create a customized message for when your users are feeling "uneasy."

[0798] Create a customized message for when the user is feeling "happy."

[0799] In this way, the system can provide information tailored to the user's emotional state, enhancing their sense of security. It also suggests optimal delivery routes and provides safe delivery plans, improving the quality of service.

[0800] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0801] Processing step flow

[0802] Step 1:

[0803] The terminal uses a weather observation device to collect weather data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to a server via a communication network. The input is the raw data obtained from the weather observation device, and the output is an organized data format sent to the server.

[0804] Step 2:

[0805] The server receives weather data sent from the terminal via a communication network. The received data is formatted into a consistent format using a data formatting means. This formatting process involves filling in missing values ​​and correcting outliers. The output is the formatted weather data.

[0806] Step 3:

[0807] The server stores the formatted weather data in a database. During this process, the data is indexed, which is designed to enable fast searches. The input is the formatted weather data, and the output is the data stored in the database.

[0808] Step 4:

[0809] The server uses generative artificial intelligence to analyze meteorological data in real time and generate weather forecasts. Machine learning algorithms and neural networks are used for the analysis, and predictions are made by combining past and current data. The input is meteorological data retrieved from a database, and the output is a weather forecast.

[0810] Step 5:

[0811] The server generates a local weather report based on the weather forecast. The weather report contains the necessary information to provide to the user, such as current weather conditions, short-term forecast, and long-term forecast. The input is the generated weather forecast, and the output is the weather report.

[0812] Step 6:

[0813] The server visualizes the weather report on a map. A data visualization tool is used to visually display weather conditions and forecast information on a map. The input is the weather report and the output is the visual information displayed on the map.

[0814] Step 7:

[0815] When a user uses the application to check weather information, the emotion engine analyzes the user's voice data, facial expression data, and text data to recognize emotions. The emotion engine determines the user's emotional state in real time and customizes information based on that. The input is the user's voice data, facial expression data, etc., and the output is the recognized emotional state.

[0816] Step 8:

[0817] The server customizes the content of the weather report based on the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, a reassuring message is added. The input is the emotional state and the weather report from the emotion engine, and the output is a customized weather report.

[0818] Step 9:

[0819] A user views a customized weather report in an application that allows the user to receive weather information along with a customized message based on their emotional state. The input is the customized weather report and the output is the information displayed to the user.

[0820] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0821] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0822] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0823] [Third embodiment]

[0824] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0825] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0826] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0827] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0828] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0829] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0830] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0831] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0832] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0833] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0834] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0835] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0836] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[0837] System configuration

[0838] The system mainly consists of the following three elements:

[0839] 1. Terminal (weather observation equipment)

[0840] 2. Server

[0841] 3. User Interface

[0842] Terminal (weather observation equipment)

[0843] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0844] server

[0845] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0846] Data Transformation: The server transforms the data it receives into a consistent format.

[0847] Data storage: The formatted data is stored in a database.

[0848] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[0849] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0850] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[0851] User Interface

[0852] Users can check real-time weather information visualized on a map through the app or website, and can easily obtain detailed weather information not only for their own area but also for other areas.

[0853] Specific processing flow and example

[0854] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0855] 1. Device operation

[0856] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[0857] 2. Server-side behavior

[0858] The server receives the data sent from the terminal, formats it, and stores it in a database.

[0859] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[0860] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[0861] 3. User Actions

[0862] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[0863] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and supports user decision-making in areas such as agriculture, construction, and event planning.

[0864] The processing flow will be explained below.

[0865] Step 1:

[0866] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Data collection is done at regular intervals (for example, every 15 minutes).

[0867] Step 2:

[0868] The terminals then process the collected weather data and transmit it in real time to a server via a communications network. The data is transmitted as data packets.

[0869] Step 3:

[0870] The server receives weather data sent from each terminal via the communication network, and automatically starts the data reception process each time data arrives.

[0871] Step 4:

[0872] The server formats the received weather data and converts it into a consistent format, which is necessary to facilitate data analysis and storage.

[0873] Step 5:

[0874] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[0875] Step 6:

[0876] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and short- and long-term forecasts.

[0877] Step 7:

[0878] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[0879] Step 8:

[0880] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[0881] Step 9:

[0882] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[0883] Step 10:

[0884] Users make decisions based on the weather information provided. For example, agricultural users may adjust their farming schedules based on the weather forecast.

[0885] This series of steps enables the system to provide highly accurate weather information in real time.

[0886] Example 1

[0887] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0888] Conventional weather information systems often lacked a consistent and efficient process from collecting weather data to analyzing, forecasting, and providing information, making it difficult to provide localized, highly accurate weather information. Furthermore, collected weather data was not stored in a consistent format, which could affect the accuracy of analysis and forecasting. This made it particularly difficult for conventional systems to provide real-time information in fields where localized weather information is important, such as agriculture and construction.

[0889] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0890] In this invention, the server includes means for receiving weather data transmitted from a weather observation device, means for formatting the data into a consistent format, means for storing the formatted data in a database, means for analyzing the weather data in real time using generative artificial intelligence to generate current weather conditions and forecasts, means for generating weather reports, and means for visualizing the reports on a map and providing them to users, thereby enabling the provision of highly accurate weather information in real time.

[0891] A "communications network" is an infrastructure for transmitting and receiving data, and includes technologies such as the Internet, Wi-Fi, and LTE.

[0892] A "weather observation device" is a device equipped with sensors for measuring meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0893] A "database" is a system for efficiently storing, searching, and updating structured data, and examples include MySQL and PostgreSQL.

[0894] "Generative AI" refers to an AI technology that learns from large amounts of data and analyzes and predicts new data, and includes machine learning and deep learning algorithms.

[0895] A "weather report" is a report generated based on the analysis of meteorological data, including current weather conditions and short-term and long-term weather forecasts.

[0896] "Visualizing on a map" means visually displaying weather data and analysis results on a map, using technologies such as Google Maps API and Leaflet.js.

[0897] "User" refers to an individual or corporation that receives and uses weather information through this system.

[0898] "Real-time" refers to a situation in which data is collected, transmitted, analyzed, and displayed without delay, resulting in almost instantaneous updates.

[0899] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[0900] System configuration

[0901] This system mainly consists of the following three elements:

[0902] 1. Terminal (weather observation equipment)

[0903] 2. Server

[0904] 3. User Interface

[0905] Terminal (weather observation equipment)

[0906] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[0907] server

[0908] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[0909] Data shaping: The server shaping the received data into a consistent format using a programming language such as Python and libraries such as Pandas and NumPy.

[0910] Data storage: The formatted data is stored in a database (e.g., MySQL, PostgreSQL).

[0911] Data analysis: The server uses a generative AI model (e.g., OpenAI's GPT-4 model) to analyze the data in real time and generate weather status and forecasts.

[0912] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[0913] Data visualization: The generated weather report is visualized on a map and presented to users in a visually easy-to-understand format. The map visualization is done using libraries such as Google Maps API and Leaflet.js.

[0914] User Interface

[0915] Users can check real-time weather information through a smartphone app or website. The interface displays weather information visualized on a map, allowing users to easily obtain detailed weather information for their area of ​​residence or area of ​​interest. This interface is developed using JavaScript frameworks (e.g., React, Vue.js) and mobile app development frameworks (e.g., Flutter, React Native).

[0916] Specific examples

[0917] Usage example (agriculture)

[0918] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[0919] 1. Terminal

[0920] At 2:00 p.m., the weather observation device installed in the user's home collects data on air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity, and sends this data to a server via a communications network.

[0921] 2. Server

[0922] The server receives the data sent from the device, formats it, and stores it in a database. The generation AI then analyzes the received data in real time and generates a forecast that rain will start the following morning. The server then creates a detailed weather report based on the forecast results and visualizes it on a map.

[0923] 3. Users

[0924] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[0925] Prompt Sentence Examples

[0926] Here are some example prompts to input to a generative AI model:

[0927] "Data is sent from a weather station installed in a farmer's home at 2:00 PM, and you want to generate a weather forecast for the following morning. Please specify each weather variable and provide details about the weather forecast."

[0928] summary

[0929] The system provides highly accurate, real-time weather information to help users make efficient decisions, and can be applied in a wide range of fields, including agriculture, construction, and event planning.

[0930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] The terminal collects and transmits weather data. Specifically, sensors installed on the terminal measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This measurement data is sent to a server via a communications network. The input is local weather data, and the output is data sent to the server.

[0933] Step 2:

[0934] The server receives weather data sent from the terminal. The server analyzes this data and extracts various weather information. The input is the raw data sent from the terminal, and the output is the extracted weather data. Specifically, the server analyzes the data packets and extracts weather information such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0935] Step 3:

[0936] The server formats the received data into a unified format using libraries such as Python, Pandas, and NumPy. The input is extracted weather data, and the output is data in a unified format. Specifically, the server formats each measurement value into JSON format to ensure consistency.

[0937] Step 4:

[0938] The server stores the formatted data in a database. For storage, a database system such as MySQL or PostgreSQL is used. The input is data in a unified format, and the output is data stored in the database. Specifically, the server generates and executes SQL queries to insert the data into the database.

[0939] Step 5:

[0940] The server retrieves data from the database and analyzes it in real time using a generative AI model. GPT-4 and other models are used for the generative AI model. The input is weather data stored in the database, and the output is the analysis results and weather forecast. Specifically, the server inputs a prompt statement into the generative AI model to obtain a weather forecast. A prompt statement such as "Data is sent from a weather observation device installed in a farmer's home at 2 p.m., and please generate a weather forecast for the morning of the following day. Please specify each weather variable and provide details of the weather forecast" is used.

[0941] Step 6:

[0942] The server generates a weather report based on the analysis results of the generative AI model. This report is provided to the user in a format that is easy to understand. The input is the analysis results of the generative AI model, and the output is a detailed weather report. Specifically, the server uses a Python report generation library (e.g., Matplotlib, ReportLab) to create a visually easy-to-understand report.

[0943] Step 7:

[0944] The server visualizes the generated weather report on a map. For visualization, it uses libraries such as Google Maps API and Leaflet.js. The input is a detailed weather report, and the output is weather information visualized on a map. Specifically, the server overlays the weather information on the map data and displays it visually.

[0945] Step 8:

[0946] Users check weather information in real time through smartphone apps or websites. Through the interface, users use the weather information displayed on a map to adjust their schedules and plans. The input is the weather information visualized on the map, and the output is the user's decision. Specifically, users open the app, check the weather forecast for their farmland, and plan their work schedule.

[0947] (Application example 1)

[0948] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0949] The present invention aims to provide a system that utilizes real-time weather data from weather observation devices installed in homes and offices to provide highly accurate and detailed weather information, and in particular, to provide a system that can optimize food delivery plans based on weather conditions. Conventional weather forecasting systems are limited to providing local weather information and have difficulty providing detailed planning support for individual deliveries, which has led to a need for improved efficiency for food delivery companies and improved customer satisfaction.

[0950] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0951] In this invention, the server includes: means for receiving weather data transmitted via a communication network from multiple weather observation devices installed in homes and offices; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; and means for providing the weather report to a user and dynamically presenting an optimal delivery plan based on the weather information. This enables individual food delivery companies to create optimal delivery plans that respond to weather fluctuations in real time, thereby improving delivery efficiency and customer satisfaction.

[0952] A "communications network" is an infrastructure for digital information transmission that transfers data sent from meteorological observation devices installed in homes and offices to a server.

[0953] A "weather observation device" is a device for measuring meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[0954] "Generative artificial intelligence" is a data processing technology that can analyze collected weather data in real time and make predictions.

[0955] "Data analysis means" refers to a function that receives weather data and analyzes that data in real time using generative artificial intelligence.

[0956] A "weather report" is a report summarizing local weather conditions and forecasts generated based on analyzed weather data.

[0957] The "means for visualizing on a map" is a function for visually displaying the generated weather report on a map.

[0958] "Means for presenting delivery plans" is a function that dynamically suggests optimal delivery plans to users based on weather information.

[0959] The system of the present invention consists of three main components: a weather observation device installed in a home or office, a server, and a user interface. This allows it to collect real-time weather data, analyze it using generative artificial intelligence, and provide users with an optimal delivery plan based on the results.

[0960] Weather observation equipment

[0961] Weather observation devices are installed in homes and offices to measure meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. The observation devices collect data at regular intervals (e.g., every 15 minutes) and send it to a server via a communication network. The communication network uses an internet connection such as Wi-Fi, LTE, or 5G.

[0962] server

[0963] The server receives data transmitted from the weather observation device via a communication network and performs the following operations:

[0964] 1. Data reception: Receive weather data through the communication network.

[0965] 2. Data Reformation: Reform the received data into a consistent format and store it in the database.

[0966] 3. Data analysis: Generative artificial intelligence models (e.g., machine learning models implemented in Python) are used to analyze data in real time to generate current weather conditions and forecasts.

[0967] 4. Report Generation: Generate local weather reports based on the analysis results, including current weather conditions, short-term forecasts, and long-term forecasts.

[0968] 5. Data visualization: The generated weather report is visualized on a map, making it easy for users to understand visually.

[0969] User Interface

[0970] Users can access weather information visualized on a map through an application on their smartphone. The user interface provides the following features:

[0971] 1. Display real-time weather information: Display real-time weather information for your current location and specified areas on the map.

[0972] 2. Delivery plan presentation: Dynamically presents optimal delivery plans to users based on weather information, allowing for optimal delivery times and routes that avoid bad weather.

[0973] Specific examples

[0974] For example, if a food delivery company schedules its next delivery for the afternoon, the system works as follows:

[0975] 1. Device side:

[0976] At 2:00 p.m., a weather observation device installed in the office collects weather data every 15 minutes and sends it to a server via a communication network.

[0977] 2. Server side actions:

[0978] The server formats and stores the received data in a database and performs real-time analysis using a generative AI model.

[0979] Based on the analysis, rain is predicted to fall between 3 and 4 p.m.

[0980] The server creates a detailed weather report and visualizes it on a map.

[0981] 3. User Actions:

[0982] The delivery person opens the app, checks the afternoon weather forecast on a map, and uses the analysis to plan the delivery to be completed by 2 p.m.

[0983] Prompt Sentence Examples

[0984] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[0985] In this way, the system can improve the operational efficiency of food delivery companies and increase customer satisfaction by combining real-time weather forecasts with dynamic delivery planning.

[0986] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0987] Step 1:

[0988] The terminal collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity from weather observation equipment installed in homes and offices at regular intervals (e.g., every 15 minutes) and transmits the data to a server via a communications network.

[0989] Input: Various weather data from weather observation equipment

[0990] Output: Formatted weather data transmitted over a communications network

[0991] Step 2:

[0992] The server receives weather data transmitted via a communication network. Since the received data may contain a mixture of different data formats, it formats it into a consistent format. The formatted data is then stored in a database.

[0993] Input: Weather data sent from the device

[0994] Output: Weather data formatted and stored in a database

[0995] Step 3:

[0996] The server analyzes the formatted weather data in real time using a generative artificial intelligence (generative AI model), which is implemented using a programming language such as Python and uses past and current weather data to make future weather predictions.

[0997] Input: Weather data stored in a database

[0998] Output: Local weather forecast data

[0999] Step 4:

[1000] The server generates a detailed weather report based on the weather forecast data obtained using a generative AI model, including current weather conditions, short-term and long-term weather forecasts.

[1001] Input: Weather forecast data

[1002] Output: Detailed weather report

[1003] Step 5:

[1004] The server visualizes the generated weather reports on a map. This is done using a web mapping platform (e.g., Google Maps API), allowing users to easily check the weather information for a specific area.

[1005] Input: Detailed weather report

[1006] Output: Weather information visualized on a map

[1007] Step 6:

[1008] Users can check real-time weather information visualized on a map through a smartphone application, and the optimal delivery plan is dynamically presented based on the weather information, allowing users to plan delivery schedules that avoid bad weather.

[1009] Input: Weather information visualized on a map

[1010] Output: Optimal delivery plan

[1011] As a concrete example, when a user schedules an afternoon delivery, the application may use the following prompt:

[1012] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[1013] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1014] This invention combines a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information with an emotion engine that recognizes the user's emotions. This system transmits and receives data over a communications network and uses generative artificial intelligence (AI) to analyze and predict the data. In addition, the emotion engine allows the weather information provided to be customized according to the user's emotional state.

[1015] System configuration

[1016] The system mainly consists of the following four elements:

[1017] 1. Terminal (weather observation equipment)

[1018] 2. Server

[1019] 3. Emotion Engine

[1020] 4. User Interface

[1021] Terminal (weather observation equipment)

[1022] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[1023] server

[1024] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[1025] Data Transformation: The server transforms the data it receives into a consistent format.

[1026] Data storage: The formatted data is stored in a database.

[1027] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[1028] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[1029] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[1030] Emotion Engine

[1031] The emotion engine analyzes the user's voice, facial expression, and text data to recognize their emotions. Based on the user's emotions recognized by the emotion engine, the presentation method and content of the weather report are customized. For example, if the user is feeling anxious, the weather information will be presented in a way that gives a sense of security.

[1032] User Interface

[1033] Through the app and website, users can view real-time weather information visualized on a map, as well as weather information customized by the emotion engine.

[1034] Specific processing flow and example

[1035] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[1036] 1. Device operation

[1037] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[1038] 2. Server-side behavior

[1039] The server receives the data sent from the terminal, formats it, and stores it in a database.

[1040] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[1041] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[1042] 3. Emotional Engine in Action

[1043] When a user opens the app, the app analyzes their voice and facial expression data to recognize their emotions. For example, if the user looks anxious, the emotion engine will recognize this.

[1044] Based on the user's emotions, the server adjusts the content and wording of the weather report: a user who is feeling anxious will be presented with weather information in a more reassuring manner.

[1045] 4. User Actions

[1046] Users can open the app, check the weather forecast for their farmland on a map, and plan their farming activities based on the forecast to be completed in the morning. At the same time, they can see reassuring reports tailored by the emotion engine.

[1047] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and by providing customized information based on the user's emotions, it supports user decision-making in areas such as agriculture, construction, and event planning.

[1048] The processing flow will be explained below.

[1049] Step 1:

[1050] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This data is collected in real time.

[1051] Step 2:

[1052] The terminal formats the collected weather data and transmits it to the server via a communication network. The transmitted data is configured as data packets.

[1053] Step 3:

[1054] The server receives weather data sent from each terminal via a communication network, and processing of the received data begins immediately.

[1055] Step 4:

[1056] The server converts the received weather data into a consistent format that facilitates subsequent analysis and storage in a database.

[1057] Step 5:

[1058] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[1059] Step 6:

[1060] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and forecasts.

[1061] Step 7:

[1062] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[1063] Step 8:

[1064] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[1065] Step 9:

[1066] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[1067] Step 10:

[1068] The emotion engine acquires voice and facial expression data when a user opens the app and analyzes the user's emotions, thereby recognizing the user's emotional state.

[1069] Step 11:

[1070] Based on the user's emotional data analyzed by the emotion engine, the server customizes the content and presentation of the weather report. For example, if the user is feeling anxious, reassuring language will be used.

[1071] Step 12:

[1072] Users receive weather reports that are tailored by the emotion engine, making the weather information more meaningful and useful to users.

[1073] As a concrete example, consider a user who works in the fields in the afternoon.

[1074] 1. At 2:00 p.m., the device measures the air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to the server in real time.

[1075] 2. The server receives this data, stores it in a database, and analyzes it using the generative AI.

[1076] 3. Based on the results of the generation AI, the server creates a forecast of rain the following morning and visualizes it on a map.

[1077] 4. When the user opens the app, the emotion engine analyzes the user's facial expression data and recognizes anxious emotions.

[1078] 5. The server provides a reassuring weather report based on this emotional data.

[1079] 6. The user can plan the next day's farm work in the morning based on the adjusted weather report and carry it out with peace of mind.

[1080] As a result, the present invention not only provides highly accurate weather information, but also adjusts the information according to the user's emotional state, thereby effectively supporting the user's decision-making.

[1081] Example 2

[1082] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1083] Conventional weather forecasting systems lack local accuracy in collecting and analyzing weather data. Furthermore, because they do not take into account the user's emotional state, the information provided may not be properly accepted by the user. Furthermore, it is difficult to achieve both highly accurate real-time forecasts and customized information provision.

[1084] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1085] In this invention, the server includes: means for receiving weather data transmitted from a plurality of installed weather observation devices via a communication network; means for arranging the weather data into a consistent format and storing it in a database; means for analyzing and predicting the data stored in the database in real time, including a generative artificial intelligence; means for generating a weather report based on the results of the analysis means; means for recognizing a user's emotions using the emotion analysis means and customizing the content and presentation method of the weather report based on the emotions; means for visualizing the customized weather report on a map; and means for providing the weather report to the user. This enables the provision of highly accurate, real-time weather information, and further improves information acceptance and satisfaction by providing customized information according to the user's emotional state.

[1086] "Communication network" refers to the network infrastructure for transmitting and receiving data from weather observation devices installed in homes and locations to a server. Specifically, it includes the Internet, local area networks (LANs), and mobile communication networks.

[1087] A "weather observation device" is a device equipped with sensors and measuring instruments for measuring multiple meteorological parameters, including air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity.

[1088] A "database" is an information management system that converts data sent from meteorological observation equipment into a consistent format and stores it persistently. Specifically, it includes relational database systems (RDBMS) and NoSQL databases.

[1089] "Generative artificial intelligence" refers to artificial intelligence systems that include machine learning algorithms and models for analyzing weather data to predict near-future weather conditions. Specifically, it refers to systems that use deep learning models and recursive neural networks (RNNs).

[1090] "Data analysis means" is a system that includes a processing unit and algorithms for analyzing collected and formatted meteorological data in real time and generating forecast results.

[1091] "Emotion analysis means" includes systems and algorithms for recognizing emotions by analyzing a user's voice data, facial expression data, and text data. Specifically, it refers to emotion recognition APIs and machine learning algorithms.

[1092] A "weather report" is a document containing a weather forecast, current weather conditions, and other related information based on analysis using generative artificial intelligence.

[1093] "Customization" is the process of tailoring and changing the content and presentation of a weather report based on the results of sentiment analysis to suit the user's emotional state.

[1094] "Map visualization means" refers to a system that includes software and interfaces for displaying the contents of weather reports on a map in a geographically understandable manner. Specifically, this refers to a map display library and API.

[1095] "Means for providing to users" includes interfaces and communication means for providing generated weather reports and customized information to users in real time, specifically mobile apps, websites, and notification systems.

[1096] The present invention is a system that collects weather data transmitted from multiple weather observation devices via a communication network, analyzes and forecasts the data in real time, and combines emotion analysis means to provide users with weather reports customized according to their emotions.

[1097] System Overview

[1098] Terminal (weather observation equipment)

[1099] The devices are installed in homes and offices and equipped with sensors to measure air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity. The collected data is sent to a server at regular intervals (for example, every 15 minutes). Communication is via Wi-Fi or mobile networks. Specific examples include connecting sensors to microcontroller boards such as Arduino or Raspberry Pi to collect data.

[1100] server

[1101] The server receives weather data sent from the terminals via a communication network, formats the received data into a consistent format using Python's Pandas library, and filters out invalid data. The formatted data is then stored in a relational database system such as MySQL or PostgreSQL.

[1102] Data Analysis and Generative AI

[1103] The server analyzes and predicts the stored data using a generative AI model. Here, machine learning platforms such as TensorFlow and PyTorch are used to make weather forecasts. Specifically, the collected data set is input into the AI ​​model, which then outputs a forecast. This forecast includes, for example, tomorrow's temperature and precipitation.

[1104] Report Generation

[1105] The server generates weather reports based on the predictions obtained from the generative AI model. The reports include current weather conditions, short-term forecasts, and long-term forecasts. Specifically, the Jinja2 template engine is used to generate the reports in HTML format, which can then be converted to PDF.

[1106] Emotion Engine

[1107] The emotion engine recognizes emotions by collecting and analyzing the user's voice, facial expression, and text data. It uses the Microsoft Azure Emotion API and IBM Watson's emotion API. The collected data is acquired using the microphone and camera on the user's device.

[1108] Report Customization

[1109] The server customizes the content and presentation of the weather report based on the user's emotions recognized by the emotion engine. Specifically, if the user is feeling anxious, the server adds a message to reassure them. For example, it displays a message such as, "Don't worry, tomorrow's weather is predicted to be fine for farm work."

[1110] User Interface

[1111] Users can use the app or website to view weather information visualized on a map. Weather information is plotted on the map using Google Maps API or Leaflet, allowing users to view the information in real time.

[1112] Specific examples

[1113] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[1114] 1. Device operation: At 2:00 p.m., a weather observation device installed on farmland collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends it to a server.

[1115] 2. Server behavior:

[1116] The server receives the data sent from the terminal, formats it, and stores it in a database.

[1117] Based on the received data, the generation AI generates a forecast that rain will fall the following morning.

[1118] The server creates a detailed weather report based on the forecast results and displays it on a map.

[1119] 3. Emotion Engine in Action:

[1120] The emotion engine recognizes when a user is expressing anxiety and generates a weather report with a reassuring message.

[1121] 4. User Actions:

[1122] Users open the app, check the weather forecast for their farm, and plan their next day's farm work to be completed in the morning.

[1123] Prompt Sentence Examples

[1124] Below are some examples of prompts to input to the generative AI model.

[1125] "User name: Ichiro Tanaka, Region: Remote location, Date and time: 14:00, October 20, 2023, Data: Pressure 1020hPa, Precipitation 1mm, Illumination 10000lux, Temperature 22degC, Wind direction south, Wind speed 5m / s, Humidity 55%. User's emotional state: Anxiety. Please generate a weather forecast and create a report with reassuring content."

[1126] This enables the system to provide highly accurate weather information in real time and provide customized information according to the user's emotions.

[1127] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1128] Step 1: Data collection

[1129] Subject: Terminal

[1130] The terminal refers to a weather observation device installed in each home or office. The terminal is equipped with sensors to measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Specifically, the terminal's built-in sensors measure data at regular intervals (e.g., every 15 minutes) and temporarily store it in memory. The measurement data is then sent to the server via a communication module (Wi-Fi or mobile network). The input of this step is the measurement data, and the output is the data sent to the server.

[1131] Step 2: Receiving and formatting data

[1132] Subject: Server

[1133] The server receives weather data sent from terminals via a communication network. The received data comes in a variety of formats and must be reshaped into a consistent format. Specifically, the data is converted into a data frame using Python's Pandas library. During reshaping, invalid and missing data is removed to generate clean data. The input of this step is the received data, and the output is the reshaped clean data.

[1134] Step 3: Data storage

[1135] Subject: Server

[1136] The server stores the formatted weather data in a relational database system (for example, MySQL or PostgreSQL). Specifically, it inserts the formatted data into the database using SQL statements. It uses transaction functionality to maintain data consistency and commits only if the insert operation is successful. The input to this step is the formatted data, and the output is the data stored in the database.

[1137] Step 4: Data analysis and prediction

[1138] Subject: Server

[1139] The server analyzes weather data and makes predictions using a generative AI model based on the data stored in the database. Specifically, it uses machine learning platforms such as TensorFlow and PyTorch. Specifically, it inputs the dataset into a deep learning model such as a long short-term memory (LSTM) network to generate a forecast. The input for this step is historical weather data stored in the database, and the output is predicted weather information.

[1140] Step 5: Generate a report

[1141] Subject: Server

[1142] The server generates a weather report based on the forecast results. The report includes the current weather conditions, short-term forecast, and long-term forecast. Specifically, it uses the Jinja2 template engine to create the report in HTML format and serves it in a user-friendly format (e.g., PDF). The input of this step is the forecast results, and the output is the generated weather report.

[1143] Step 6: Collect emotional data

[1144] Subject: User

[1145] A user accesses the system using an app or website. The emotion engine collects the user's voice, facial expression, and text data. Specifically, it acquires data through the user's device's camera and microphone and prepares it to be sent to the emotion recognition API. The input of this step is the user's voice, facial expression, and text data, and the output is the collected emotion data.

[1146] Step 7: Sentiment Analysis

[1147] Subject: Server

[1148] The server analyzes the collected emotion data and recognizes the user's emotional state. Specifically, it uses the Microsoft Azure Emotion API and IBM Watson's emotion recognition API. The server obtains the emotion analysis results and adjusts the content of the weather report accordingly. The input of this step is the collected emotion data, and the output is the analyzed emotional state.

[1149] Step 8: Report Customization

[1150] Subject: Server

[1151] The server adjusts the content and presentation of the weather report based on the results of the emotion analysis. Specifically, it customizes the report by adding a message that reassures anxious users. For example, it creates a report that includes a message such as, "Please rest assured that tomorrow's weather is predicted to be fine for farm work." The input of this step is the analyzed emotional state, and the output is a customized weather report.

[1152] Step 9: Data visualization and display

[1153] Subject: Server

[1154] The server visualizes and displays the customized weather report on a map. Specifically, it plots weather information on the map using a map display library such as Google Maps API or Leaflet. Users can manipulate the map from an app or website and check detailed weather information for the area of ​​interest. The input of this step is the customized weather report, and the output is the weather information visualized on the map.

[1155] Step 10: Provide information

[1156] Subject: User

[1157] Users open an app or website to view weather information visualized on a map and customized weather reports, which allows them to plan their activities. For example, a farmer checks the weather forecast to plan the next day's farm work and adjusts the timing of their activities. The input of this step is the weather information visualized on a map, and the output is the user's confirmation and use of the information.

[1158] (Application example 2)

[1159] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1160] Conventional weather forecasting systems have had problems in providing localized, highly accurate weather information and in being unable to provide customized information according to the user's emotional state. Food delivery services, in particular, are susceptible to the effects of weather, so they are required to provide real-time weather information and provide a sense of security with appropriate messages that respond to the user's emotions. To address these challenges, a solution is needed to improve the quality of the user experience.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving weather data transmitted from multiple weather observation devices installed in each home via a communication network; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; means for providing the weather report to a user; means including an emotion engine for recognizing the user's emotional state and customizing information based on the emotional state; and means for displaying information customized by the emotion engine. This makes it possible to provide localized and highly accurate weather information and, in a food delivery service, to provide customized messages according to the user's emotional state.

[1162] definition statement

[1163] A "communications network" is an infrastructure used by multiple devices to send and receive data, including the Internet and leased lines.

[1164] A "weather observation device" is a device used to measure meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1165] "Weather data" refers to data relating to the weather conditions of an environment, including information such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1166] "Real-time" means that data collection and analysis occur almost instantaneously, providing information without time delay.

[1167] "Generative AI" is an AI that automatically analyzes and predicts based on large amounts of data, and is a technology that uses machine learning algorithms and neural networks.

[1168] "Data analysis means" refers to devices and software for analyzing collected meteorological data and extracting useful information.

[1169] A "weather report" is a report of information including current weather conditions, short-term forecasts, and long-term forecasts for a particular area.

[1170] A "visualization tool" is a device or software that displays data in a visual format, such as a chart or map.

[1171] "User" refers to any individual or entity that receives weather information using this system.

[1172] An "emotion engine" is an artificial intelligence that recognizes the user's emotional state from voice, facial expressions, text data, etc., and customizes information based on that.

[1173] A "customization means" is a device or software that adjusts the content and format of the information provided depending on the user's emotional state.

[1174] A "database" is a system for storing organized data and enabling it to be searched and processed efficiently.

[1175] MODE FOR CARRYING OUT THE INVENTION

[1176] The present invention is a system that aims to customize weather information for food delivery according to the user's emotional state. The system consists of the following components:

[1177] communication network

[1178] A communication network, such as the Internet or a dedicated line, is used to receive weather data transmitted from multiple weather observation devices installed in homes.

[1179] Weather observation equipment

[1180] A weather observation device is a device that measures meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity, etc. This data is collected at regular intervals (for example, every 15 minutes) and sent to a server via a communication network.

[1181] server

[1182] The server processes the weather data received via the communication network.

[1183] Data Formatter: Formats received weather data into a consistent format.

[1184] Database: The formatted data is stored in a database.

[1185] Generative artificial intelligence: Analyzes data and generates weather forecasts in real time. This AI uses machine learning algorithms and neural networks.

[1186] Weather report generator: Generates local weather reports based on weather forecasts.

[1187] Visualization: Visually display the weather report on a map.

[1188] Emotion Engine

[1189] The emotion engine recognizes emotions by analyzing the user's voice data, facial expression data, and text data. The engine uses generative artificial intelligence to determine the user's emotional state from the collected data. As the user uses the application, the emotion engine monitors the user's state in real time.

[1190] Customization methods

[1191] It is a way to customize information based on the user's emotional state. The emotion engine will adjust the content and presentation of the weather information provided to the user based on the emotions it recognizes. For example, if the user is feeling anxious, a reassuring message will be displayed.

[1192] User Interface

[1193] The user interface is provided as a smartphone application or website, where users can view real-time weather information visualized on a map, and a customized weather report is displayed using an emotion engine.

[1194] Specific examples

[1195] For example, if a food delivery service user feels anxious about a rainy day, the system works as follows: First, a weather observation device collects data in real time and sends it to the server. The server then uses generative artificial intelligence to generate a weather forecast and combines it with the user's emotional state to customize the weather report. An example of a prompt message is as follows:

[1196] text

[1197] Create a customized message for when your users are feeling "uneasy."

[1198] Create a customized message for when the user is feeling "happy."

[1199] In this way, the system can provide information tailored to the user's emotional state, enhancing their sense of security. It also suggests optimal delivery routes and provides safe delivery plans, improving the quality of service.

[1200] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1201] Processing step flow

[1202] Step 1:

[1203] The terminal uses a weather observation device to collect weather data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to a server via a communication network. The input is the raw data obtained from the weather observation device, and the output is an organized data format sent to the server.

[1204] Step 2:

[1205] The server receives weather data sent from the terminal via a communication network. The received data is formatted into a consistent format using a data formatting means. This formatting process involves filling in missing values ​​and correcting outliers. The output is the formatted weather data.

[1206] Step 3:

[1207] The server stores the formatted weather data in a database. During this process, the data is indexed, which is designed to enable fast searches. The input is the formatted weather data, and the output is the data stored in the database.

[1208] Step 4:

[1209] The server uses generative artificial intelligence to analyze meteorological data in real time and generate weather forecasts. Machine learning algorithms and neural networks are used for the analysis, and predictions are made by combining past and current data. The input is meteorological data retrieved from a database, and the output is a weather forecast.

[1210] Step 5:

[1211] The server generates a local weather report based on the weather forecast. The weather report contains the necessary information to provide to the user, such as current weather conditions, short-term forecast, and long-term forecast. The input is the generated weather forecast, and the output is the weather report.

[1212] Step 6:

[1213] The server visualizes the weather report on a map. A data visualization tool is used to visually display weather conditions and forecast information on a map. The input is the weather report and the output is the visual information displayed on the map.

[1214] Step 7:

[1215] When a user uses the application to check weather information, the emotion engine analyzes the user's voice data, facial expression data, and text data to recognize emotions. The emotion engine determines the user's emotional state in real time and customizes information based on that. The input is the user's voice data, facial expression data, etc., and the output is the recognized emotional state.

[1216] Step 8:

[1217] The server customizes the content of the weather report based on the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, a reassuring message is added. The input is the emotional state and the weather report from the emotion engine, and the output is a customized weather report.

[1218] Step 9:

[1219] A user views a customized weather report in an application that allows the user to receive weather information along with a customized message based on their emotional state. The input is the customized weather report and the output is the information displayed to the user.

[1220] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1221] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1222] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1223] [Fourth embodiment]

[1224] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1225] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1226] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1227] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1228] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1230] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1231] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1232] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1233] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1234] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1235] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1236] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1237] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[1238] System configuration

[1239] The system mainly consists of the following three elements:

[1240] 1. Terminal (weather observation equipment)

[1241] 2. Server

[1242] 3. User Interface

[1243] Terminal (weather observation equipment)

[1244] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[1245] server

[1246] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[1247] Data Transformation: The server transforms the data it receives into a consistent format.

[1248] Data storage: The formatted data is stored in a database.

[1249] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[1250] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[1251] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[1252] User Interface

[1253] Users can check real-time weather information visualized on a map through the app or website, and can easily obtain detailed weather information not only for their own area but also for other areas.

[1254] Specific processing flow and example

[1255] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[1256] 1. Device operation

[1257] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[1258] 2. Server-side behavior

[1259] The server receives the data sent from the terminal, formats it, and stores it in a database.

[1260] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[1261] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[1262] 3. User Actions

[1263] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[1264] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and supports user decision-making in areas such as agriculture, construction, and event planning.

[1265] The processing flow will be explained below.

[1266] Step 1:

[1267] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Data collection is done at regular intervals (for example, every 15 minutes).

[1268] Step 2:

[1269] The terminals then process the collected weather data and transmit it in real time to a server via a communications network. The data is transmitted as data packets.

[1270] Step 3:

[1271] The server receives weather data sent from each terminal via the communication network, and automatically starts the data reception process each time data arrives.

[1272] Step 4:

[1273] The server formats the received weather data and converts it into a consistent format, which is necessary to facilitate data analysis and storage.

[1274] Step 5:

[1275] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[1276] Step 6:

[1277] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and short- and long-term forecasts.

[1278] Step 7:

[1279] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[1280] Step 8:

[1281] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[1282] Step 9:

[1283] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[1284] Step 10:

[1285] Users make decisions based on the weather information provided. For example, agricultural users may adjust their farming schedules based on the weather forecast.

[1286] This series of steps enables the system to provide highly accurate weather information in real time.

[1287] Example 1

[1288] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1289] Conventional weather information systems often lacked a consistent and efficient process from collecting weather data to analyzing, forecasting, and providing information, making it difficult to provide localized, highly accurate weather information. Furthermore, collected weather data was not stored in a consistent format, which could affect the accuracy of analysis and forecasting. This made it particularly difficult for conventional systems to provide real-time information in fields where localized weather information is important, such as agriculture and construction.

[1290] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1291] In this invention, the server includes means for receiving weather data transmitted from a weather observation device, means for formatting the data into a consistent format, means for storing the formatted data in a database, means for analyzing the weather data in real time using generative artificial intelligence to generate current weather conditions and forecasts, means for generating weather reports, and means for visualizing the reports on a map and providing them to users, thereby enabling the provision of highly accurate weather information in real time.

[1292] A "communications network" is an infrastructure for transmitting and receiving data, and includes technologies such as the Internet, Wi-Fi, and LTE.

[1293] A "weather observation device" is a device equipped with sensors for measuring meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1294] A "database" is a system for efficiently storing, searching, and updating structured data, and examples include MySQL and PostgreSQL.

[1295] "Generative AI" refers to an AI technology that learns from large amounts of data and analyzes and predicts new data, and includes machine learning and deep learning algorithms.

[1296] A "weather report" is a report generated based on the analysis of meteorological data, including current weather conditions and short-term and long-term weather forecasts.

[1297] "Visualizing on a map" means visually displaying weather data and analysis results on a map, using technologies such as Google Maps API and Leaflet.js.

[1298] "User" refers to an individual or corporation that receives and uses weather information through this system.

[1299] "Real-time" refers to a situation in which data is collected, transmitted, analyzed, and displayed without delay, resulting in almost instantaneous updates.

[1300] This invention is a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information. This system transmits and receives data over a communication network and uses generative artificial intelligence (AI) to analyze and predict the data. Furthermore, weather information is provided to users in a visualized format on a map.

[1301] System configuration

[1302] This system mainly consists of the following three elements:

[1303] 1. Terminal (weather observation equipment)

[1304] 2. Server

[1305] 3. User Interface

[1306] Terminal (weather observation equipment)

[1307] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[1308] server

[1309] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[1310] Data shaping: The server shaping the received data into a consistent format using a programming language such as Python and libraries such as Pandas and NumPy.

[1311] Data storage: The formatted data is stored in a database (e.g., MySQL, PostgreSQL).

[1312] Data analysis: The server uses a generative AI model (e.g., OpenAI's GPT-4 model) to analyze the data in real time and generate weather status and forecasts.

[1313] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[1314] Data visualization: The generated weather report is visualized on a map and presented to users in a visually easy-to-understand format. The map visualization is done using libraries such as Google Maps API and Leaflet.js.

[1315] User Interface

[1316] Users can check real-time weather information through a smartphone app or website. The interface displays weather information visualized on a map, allowing users to easily obtain detailed weather information for their area of ​​residence or area of ​​interest. This interface is developed using JavaScript frameworks (e.g., React, Vue.js) and mobile app development frameworks (e.g., Flutter, React Native).

[1317] Specific examples

[1318] Usage example (agriculture)

[1319] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[1320] 1. Terminal

[1321] At 2:00 p.m., the weather observation device installed in the user's home collects data on air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity, and sends this data to a server via a communications network.

[1322] 2. Server

[1323] The server receives the data sent from the device, formats it, and stores it in a database. The generation AI then analyzes the received data in real time and generates a forecast that rain will start the following morning. The server then creates a detailed weather report based on the forecast results and visualizes it on a map.

[1324] 3. Users

[1325] Users can open the app, check the weather forecast for their farm on a map, and plan their farming activities for the next day based on the forecast, ensuring they are completed by the morning.

[1326] Prompt Sentence Examples

[1327] Here are some example prompts to input to a generative AI model:

[1328] "Data is sent from a weather station installed in a farmer's home at 2:00 PM, and you want to generate a weather forecast for the following morning. Please specify each weather variable and provide details about the weather forecast."

[1329] summary

[1330] The system provides highly accurate, real-time weather information to help users make efficient decisions, and can be applied in a wide range of fields, including agriculture, construction, and event planning.

[1331] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1332] Step 1:

[1333] The terminal collects and transmits weather data. Specifically, sensors installed on the terminal measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This measurement data is sent to a server via a communications network. The input is local weather data, and the output is data sent to the server.

[1334] Step 2:

[1335] The server receives weather data sent from the terminal. The server analyzes this data and extracts various weather information. The input is the raw data sent from the terminal, and the output is the extracted weather data. Specifically, the server analyzes the data packets and extracts weather information such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1336] Step 3:

[1337] The server formats the received data into a unified format using libraries such as Python, Pandas, and NumPy. The input is extracted weather data, and the output is data in a unified format. Specifically, the server formats each measurement value into JSON format to ensure consistency.

[1338] Step 4:

[1339] The server stores the formatted data in a database. For storage, a database system such as MySQL or PostgreSQL is used. The input is data in a unified format, and the output is data stored in the database. Specifically, the server generates and executes SQL queries to insert the data into the database.

[1340] Step 5:

[1341] The server retrieves data from the database and analyzes it in real time using a generative AI model. GPT-4 and other models are used for the generative AI model. The input is weather data stored in the database, and the output is the analysis results and weather forecast. Specifically, the server inputs a prompt statement into the generative AI model to obtain a weather forecast. A prompt statement such as "Data is sent from a weather observation device installed in a farmer's home at 2 p.m., and please generate a weather forecast for the morning of the following day. Please specify each weather variable and provide details of the weather forecast" is used.

[1342] Step 6:

[1343] The server generates a weather report based on the analysis results of the generative AI model. This report is provided to the user in a format that is easy to understand. The input is the analysis results of the generative AI model, and the output is a detailed weather report. Specifically, the server uses a Python report generation library (e.g., Matplotlib, ReportLab) to create a visually easy-to-understand report.

[1344] Step 7:

[1345] The server visualizes the generated weather report on a map. For visualization, it uses libraries such as Google Maps API and Leaflet.js. The input is a detailed weather report, and the output is weather information visualized on a map. Specifically, the server overlays the weather information on the map data and displays it visually.

[1346] Step 8:

[1347] Users check weather information in real time through smartphone apps or websites. Through the interface, users use the weather information displayed on a map to adjust their schedules and plans. The input is the weather information visualized on the map, and the output is the user's decision. Specifically, users open the app, check the weather forecast for their farmland, and plan their work schedule.

[1348] (Application example 1)

[1349] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1350] The present invention aims to provide a system that utilizes real-time weather data from weather observation devices installed in homes and offices to provide highly accurate and detailed weather information, and in particular, to provide a system that can optimize food delivery plans based on weather conditions. Conventional weather forecasting systems are limited to providing local weather information and have difficulty providing detailed planning support for individual deliveries, which has led to a need for improved efficiency for food delivery companies and improved customer satisfaction.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1352] In this invention, the server includes: means for receiving weather data transmitted via a communication network from multiple weather observation devices installed in homes and offices; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; and means for providing the weather report to a user and dynamically presenting an optimal delivery plan based on the weather information. This enables individual food delivery companies to create optimal delivery plans that respond to weather fluctuations in real time, thereby improving delivery efficiency and customer satisfaction.

[1353] A "communications network" is an infrastructure for digital information transmission that transfers data sent from meteorological observation devices installed in homes and offices to a server.

[1354] A "weather observation device" is a device for measuring meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1355] "Generative artificial intelligence" is a data processing technology that can analyze collected weather data in real time and make predictions.

[1356] "Data analysis means" refers to a function that receives weather data and analyzes that data in real time using generative artificial intelligence.

[1357] A "weather report" is a report summarizing local weather conditions and forecasts generated based on analyzed weather data.

[1358] The "means for visualizing on a map" is a function for visually displaying the generated weather report on a map.

[1359] "Means for presenting delivery plans" is a function that dynamically suggests optimal delivery plans to users based on weather information.

[1360] The system of the present invention consists of three main components: a weather observation device installed in a home or office, a server, and a user interface. This allows it to collect real-time weather data, analyze it using generative artificial intelligence, and provide users with an optimal delivery plan based on the results.

[1361] Weather observation equipment

[1362] Weather observation devices are installed in homes and offices to measure meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. The observation devices collect data at regular intervals (e.g., every 15 minutes) and send it to a server via a communication network. The communication network uses an internet connection such as Wi-Fi, LTE, or 5G.

[1363] server

[1364] The server receives data transmitted from the weather observation device via a communication network and performs the following operations:

[1365] 1. Data reception: Receive weather data through the communication network.

[1366] 2. Data Reformation: Reform the received data into a consistent format and store it in the database.

[1367] 3. Data analysis: Generative artificial intelligence models (e.g., machine learning models implemented in Python) are used to analyze data in real time to generate current weather conditions and forecasts.

[1368] 4. Report Generation: Generate local weather reports based on the analysis results, including current weather conditions, short-term forecasts, and long-term forecasts.

[1369] 5. Data visualization: The generated weather report is visualized on a map, making it easy for users to understand visually.

[1370] User Interface

[1371] Users can access weather information visualized on a map through an application on their smartphone. The user interface provides the following features:

[1372] 1. Display real-time weather information: Display real-time weather information for your current location and specified areas on the map.

[1373] 2. Delivery plan presentation: Dynamically presents optimal delivery plans to users based on weather information, allowing for optimal delivery times and routes that avoid bad weather.

[1374] Specific examples

[1375] For example, if a food delivery company schedules its next delivery for the afternoon, the system works as follows:

[1376] 1. Device side:

[1377] At 2:00 p.m., a weather observation device installed in the office collects weather data every 15 minutes and sends it to a server via a communication network.

[1378] 2. Server side actions:

[1379] The server formats and stores the received data in a database and performs real-time analysis using a generative AI model.

[1380] Based on the analysis, rain is predicted to fall between 3 and 4 p.m.

[1381] The server creates a detailed weather report and visualizes it on a map.

[1382] 3. User Actions:

[1383] The delivery person opens the app, checks the afternoon weather forecast on a map, and uses the analysis to plan the delivery to be completed by 2 p.m.

[1384] Prompt Sentence Examples

[1385] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[1386] In this way, the system can improve the operational efficiency of food delivery companies and increase customer satisfaction by combining real-time weather forecasts with dynamic delivery planning.

[1387] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1388] Step 1:

[1389] The terminal collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity from weather observation equipment installed in homes and offices at regular intervals (e.g., every 15 minutes) and transmits the data to a server via a communications network.

[1390] Input: Various weather data from weather observation equipment

[1391] Output: Formatted weather data transmitted over a communications network

[1392] Step 2:

[1393] The server receives weather data transmitted via a communication network. Since the received data may contain a mixture of different data formats, it formats it into a consistent format. The formatted data is then stored in a database.

[1394] Input: Weather data sent from the device

[1395] Output: Weather data formatted and stored in a database

[1396] Step 3:

[1397] The server analyzes the formatted weather data in real time using a generative artificial intelligence (generative AI model), which is implemented using a programming language such as Python and uses past and current weather data to make future weather predictions.

[1398] Input: Weather data stored in a database

[1399] Output: Local weather forecast data

[1400] Step 4:

[1401] The server generates a detailed weather report based on the weather forecast data obtained using a generative AI model, including current weather conditions, short-term and long-term weather forecasts.

[1402] Input: Weather forecast data

[1403] Output: Detailed weather report

[1404] Step 5:

[1405] The server visualizes the generated weather reports on a map. This is done using a web mapping platform (e.g., Google Maps API), allowing users to easily check the weather information for a specific area.

[1406] Input: Detailed weather report

[1407] Output: Weather information visualized on a map

[1408] Step 6:

[1409] Users can check real-time weather information visualized on a map through a smartphone application, and the optimal delivery plan is dynamically presented based on the weather information, allowing users to plan delivery schedules that avoid bad weather.

[1410] Input: Weather information visualized on a map

[1411] Output: Optimal delivery plan

[1412] As a concrete example, when a user schedules an afternoon delivery, the application may use the following prompt:

[1413] "Based on the current weather forecast for Tokyo, what is the best time and estimated delivery time for a delivery from 35.6895, 139.6917 to 35.6890, 139.7000?"

[1414] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1415] This invention combines a system that analyzes weather data collected from weather observation devices installed in various locations, such as homes and offices, in real time to provide detailed and highly accurate weather information with an emotion engine that recognizes the user's emotions. This system transmits and receives data over a communications network and uses generative artificial intelligence (AI) to analyze and predict the data. In addition, the emotion engine allows the weather information provided to be customized according to the user's emotional state.

[1416] System configuration

[1417] The system mainly consists of the following four elements:

[1418] 1. Terminal (weather observation equipment)

[1419] 2. Server

[1420] 3. Emotion Engine

[1421] 4. User Interface

[1422] Terminal (weather observation equipment)

[1423] The terminals are installed in homes and offices and use built-in sensors to measure various weather data (barometric pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity). This data is collected at regular intervals (for example, every 15 minutes) and sent to a server in real time via a communication network.

[1424] server

[1425] The server receives weather data sent from each terminal via the communication network. Once received, the data is processed according to the following procedure.

[1426] Data Transformation: The server transforms the data it receives into a consistent format.

[1427] Data storage: The formatted data is stored in a database.

[1428] Data Analysis: The server uses generative AI to analyze data in real time to provide current weather conditions and forecasts.

[1429] Report Generation: Based on the analysis results, a local weather report is generated, including current weather conditions, short-term forecasts, and long-term forecasts.

[1430] Data Visualization: The generated weather report is visualized on a map, providing users with a visually easy-to-understand view.

[1431] Emotion Engine

[1432] The emotion engine analyzes the user's voice, facial expression, and text data to recognize their emotions. Based on the user's emotions recognized by the emotion engine, the presentation method and content of the weather report are customized. For example, if the user is feeling anxious, the weather information will be presented in a way that gives a sense of security.

[1433] User Interface

[1434] Through the app and website, users can view real-time weather information visualized on a map, as well as weather information customized by the emotion engine.

[1435] Specific processing flow and example

[1436] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[1437] 1. Device operation

[1438] At 2:00 p.m., a weather observation device installed in the user's home collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends this data to a server via a communications network.

[1439] 2. Server-side behavior

[1440] The server receives the data sent from the terminal, formats it, and stores it in a database.

[1441] The generating AI analyzes the received data in real time and generates a forecast that rain will start falling the following morning.

[1442] The server creates a detailed weather report based on the forecast results and visualizes it on a map.

[1443] 3. Emotional Engine in Action

[1444] When a user opens the app, the app analyzes their voice and facial expression data to recognize their emotions. For example, if the user looks anxious, the emotion engine will recognize this.

[1445] Based on the user's emotions, the server adjusts the content and wording of the weather report: a user who is feeling anxious will be presented with weather information in a more reassuring manner.

[1446] 4. User Actions

[1447] Users can open the app, check the weather forecast for their farmland on a map, and plan their farming activities based on the forecast to be completed in the morning. At the same time, they can see reassuring reports tailored by the emotion engine.

[1448] In this way, the present invention makes it possible to provide localized, highly accurate weather information, which was difficult to achieve with conventional weather forecasting systems, and by providing customized information based on the user's emotions, it supports user decision-making in areas such as agriculture, construction, and event planning.

[1449] The processing flow will be explained below.

[1450] Step 1:

[1451] The device uses built-in sensors to collect data on atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity at regular intervals (for example, every 15 minutes). This data is collected in real time.

[1452] Step 2:

[1453] The terminal formats the collected weather data and transmits it to the server via a communication network. The transmitted data is configured as data packets.

[1454] Step 3:

[1455] The server receives weather data sent from each terminal via a communication network, and processing of the received data begins immediately.

[1456] Step 4:

[1457] The server converts the received weather data into a consistent format that facilitates subsequent analysis and storage in a database.

[1458] Step 5:

[1459] The server stores the formatted data in a database, where the data is saved in a format that includes a timestamp and location information.

[1460] Step 6:

[1461] The server analyzes the weather data stored in the database in real time using generative artificial intelligence (AI), which uses time series analysis and machine learning algorithms to generate current weather conditions and forecasts.

[1462] Step 7:

[1463] The server generates a local weather report based on the analysis results of the generation AI, including the current weather conditions, rolling short-term forecasts, and long-term forecasts.

[1464] Step 8:

[1465] The server visualizes the generated weather reports on a map, combining the reports with geographical details in a visually understandable way for users.

[1466] Step 9:

[1467] Users check weather information using a dedicated app or website, which allows users to view real-time weather data and forecasts on a map.

[1468] Step 10:

[1469] The emotion engine acquires voice and facial expression data when a user opens the app and analyzes the user's emotions, thereby recognizing the user's emotional state.

[1470] Step 11:

[1471] Based on the user's emotional data analyzed by the emotion engine, the server customizes the content and presentation of the weather report. For example, if the user is feeling anxious, reassuring language will be used.

[1472] Step 12:

[1473] Users receive weather reports that are tailored by the emotion engine, making the weather information more meaningful and useful to users.

[1474] As a concrete example, consider a user who works in the fields in the afternoon.

[1475] 1. At 2:00 p.m., the device measures the air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to the server in real time.

[1476] 2. The server receives this data, stores it in a database, and analyzes it using the generative AI.

[1477] 3. Based on the results of the generation AI, the server creates a forecast of rain the following morning and visualizes it on a map.

[1478] 4. When the user opens the app, the emotion engine analyzes the user's facial expression data and recognizes anxious emotions.

[1479] 5. The server provides a reassuring weather report based on this emotional data.

[1480] 6. The user can plan the next day's farm work in the morning based on the adjusted weather report and carry it out with peace of mind.

[1481] As a result, the present invention not only provides highly accurate weather information, but also adjusts the information according to the user's emotional state, thereby effectively supporting the user's decision-making.

[1482] Example 2

[1483] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] Conventional weather forecasting systems lack local accuracy in collecting and analyzing weather data. Furthermore, because they do not take into account the user's emotional state, the information provided may not be properly accepted by the user. Furthermore, it is difficult to achieve both highly accurate real-time forecasts and customized information provision.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1486] In this invention, the server includes: means for receiving weather data transmitted from a plurality of installed weather observation devices via a communication network; means for arranging the weather data into a consistent format and storing it in a database; means for analyzing and predicting the data stored in the database in real time, including a generative artificial intelligence; means for generating a weather report based on the results of the analysis means; means for recognizing a user's emotions using the emotion analysis means and customizing the content and presentation method of the weather report based on the emotions; means for visualizing the customized weather report on a map; and means for providing the weather report to the user. This enables the provision of highly accurate, real-time weather information, and further improves information acceptance and satisfaction by providing customized information according to the user's emotional state.

[1487] "Communication network" refers to the network infrastructure for transmitting and receiving data from weather observation devices installed in homes and locations to a server. Specifically, it includes the Internet, local area networks (LANs), and mobile communication networks.

[1488] A "weather observation device" is a device equipped with sensors and measuring instruments for measuring multiple meteorological parameters, including air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity.

[1489] A "database" is an information management system that converts data sent from meteorological observation equipment into a consistent format and stores it persistently. Specifically, it includes relational database systems (RDBMS) and NoSQL databases.

[1490] "Generative artificial intelligence" refers to artificial intelligence systems that include machine learning algorithms and models for analyzing weather data to predict near-future weather conditions. Specifically, it refers to systems that use deep learning models and recursive neural networks (RNNs).

[1491] "Data analysis means" is a system that includes a processing unit and algorithms for analyzing collected and formatted meteorological data in real time and generating forecast results.

[1492] "Emotion analysis means" includes systems and algorithms for recognizing emotions by analyzing a user's voice data, facial expression data, and text data. Specifically, it refers to emotion recognition APIs and machine learning algorithms.

[1493] A "weather report" is a document containing a weather forecast, current weather conditions, and other related information based on analysis using generative artificial intelligence.

[1494] "Customization" is the process of tailoring and changing the content and presentation of a weather report based on the results of sentiment analysis to suit the user's emotional state.

[1495] "Map visualization means" refers to a system that includes software and interfaces for displaying the contents of weather reports on a map in a geographically understandable manner. Specifically, this refers to a map display library and API.

[1496] "Means for providing to users" includes interfaces and communication means for providing generated weather reports and customized information to users in real time, specifically mobile apps, websites, and notification systems.

[1497] The present invention is a system that collects weather data transmitted from multiple weather observation devices via a communication network, analyzes and forecasts the data in real time, and combines emotion analysis means to provide users with weather reports customized according to their emotions.

[1498] System Overview

[1499] Terminal (weather observation equipment)

[1500] The devices are installed in homes and offices and equipped with sensors to measure air pressure, precipitation, light intensity, temperature, wind direction, wind speed, and humidity. The collected data is sent to a server at regular intervals (for example, every 15 minutes). Communication is via Wi-Fi or mobile networks. Specific examples include connecting sensors to microcontroller boards such as Arduino or Raspberry Pi to collect data.

[1501] server

[1502] The server receives weather data sent from the terminals via a communication network, formats the received data into a consistent format using Python's Pandas library, and filters out invalid data. The formatted data is then stored in a relational database system such as MySQL or PostgreSQL.

[1503] Data Analysis and Generative AI

[1504] The server analyzes and predicts the stored data using a generative AI model. Here, machine learning platforms such as TensorFlow and PyTorch are used to make weather forecasts. Specifically, the collected data set is input into the AI ​​model, which then outputs a forecast. This forecast includes, for example, tomorrow's temperature and precipitation.

[1505] Report Generation

[1506] The server generates weather reports based on the predictions obtained from the generative AI model. The reports include current weather conditions, short-term forecasts, and long-term forecasts. Specifically, the Jinja2 template engine is used to generate the reports in HTML format, which can then be converted to PDF.

[1507] Emotion Engine

[1508] The emotion engine recognizes emotions by collecting and analyzing the user's voice, facial expression, and text data. It uses the Microsoft Azure Emotion API and IBM Watson's emotion API. The collected data is acquired using the microphone and camera on the user's device.

[1509] Report Customization

[1510] The server customizes the content and presentation of the weather report based on the user's emotions recognized by the emotion engine. Specifically, if the user is feeling anxious, the server adds a message to reassure them. For example, it displays a message such as, "Don't worry, tomorrow's weather is predicted to be fine for farm work."

[1511] User Interface

[1512] Users can use the app or website to view weather information visualized on a map. Weather information is plotted on the map using Google Maps API or Leaflet, allowing users to view the information in real time.

[1513] Specific examples

[1514] For example, if a farmer checks the weather for the next day and plans his or her farm work in the afternoon, the system works as follows:

[1515] 1. Device operation: At 2:00 p.m., a weather observation device installed on farmland collects data on air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity every 15 minutes and sends it to a server.

[1516] 2. Server behavior:

[1517] The server receives the data sent from the terminal, formats it, and stores it in a database.

[1518] Based on the received data, the generation AI generates a forecast that rain will fall the following morning.

[1519] The server creates a detailed weather report based on the forecast results and displays it on a map.

[1520] 3. Emotion Engine in Action:

[1521] The emotion engine recognizes when a user is expressing anxiety and generates a weather report with a reassuring message.

[1522] 4. User Actions:

[1523] Users open the app, check the weather forecast for their farm, and plan their next day's farm work to be completed in the morning.

[1524] Prompt Sentence Examples

[1525] Below are some examples of prompts to input to the generative AI model.

[1526] "User name: Ichiro Tanaka, Region: Remote location, Date and time: 14:00, October 20, 2023, Data: Pressure 1020hPa, Precipitation 1mm, Illumination 10000lux, Temperature 22degC, Wind direction south, Wind speed 5m / s, Humidity 55%. User's emotional state: Anxiety. Please generate a weather forecast and create a report with reassuring content."

[1527] This enables the system to provide highly accurate weather information in real time and provide customized information according to the user's emotions.

[1528] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1529] Step 1: Data collection

[1530] Subject: Terminal

[1531] The terminal refers to a weather observation device installed in each home or office. The terminal is equipped with sensors to measure air pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity. Specifically, the terminal's built-in sensors measure data at regular intervals (e.g., every 15 minutes) and temporarily store it in memory. The measurement data is then sent to the server via a communication module (Wi-Fi or mobile network). The input of this step is the measurement data, and the output is the data sent to the server.

[1532] Step 2: Receiving and formatting data

[1533] Subject: Server

[1534] The server receives weather data sent from terminals via a communication network. The received data comes in a variety of formats and must be reshaped into a consistent format. Specifically, the data is converted into a data frame using Python's Pandas library. During reshaping, invalid and missing data is removed to generate clean data. The input of this step is the received data, and the output is the reshaped clean data.

[1535] Step 3: Data storage

[1536] Subject: Server

[1537] The server stores the formatted weather data in a relational database system (for example, MySQL or PostgreSQL). Specifically, it inserts the formatted data into the database using SQL statements. It uses transaction functionality to maintain data consistency and commits only if the insert operation is successful. The input to this step is the formatted data, and the output is the data stored in the database.

[1538] Step 4: Data analysis and prediction

[1539] Subject: Server

[1540] The server analyzes weather data and makes predictions using a generative AI model based on the data stored in the database. Specifically, it uses machine learning platforms such as TensorFlow and PyTorch. Specifically, it inputs the dataset into a deep learning model such as a long short-term memory (LSTM) network to generate a forecast. The input for this step is historical weather data stored in the database, and the output is predicted weather information.

[1541] Step 5: Generate a report

[1542] Subject: Server

[1543] The server generates a weather report based on the forecast results. The report includes the current weather conditions, short-term forecast, and long-term forecast. Specifically, it uses the Jinja2 template engine to create the report in HTML format and serves it in a user-friendly format (e.g., PDF). The input of this step is the forecast results, and the output is the generated weather report.

[1544] Step 6: Collect emotional data

[1545] Subject: User

[1546] A user accesses the system using an app or website. The emotion engine collects the user's voice, facial expression, and text data. Specifically, it acquires data through the user's device's camera and microphone and prepares it to be sent to the emotion recognition API. The input of this step is the user's voice, facial expression, and text data, and the output is the collected emotion data.

[1547] Step 7: Sentiment Analysis

[1548] Subject: Server

[1549] The server analyzes the collected emotion data and recognizes the user's emotional state. Specifically, it uses the Microsoft Azure Emotion API and IBM Watson's emotion recognition API. The server obtains the emotion analysis results and adjusts the content of the weather report accordingly. The input of this step is the collected emotion data, and the output is the analyzed emotional state.

[1550] Step 8: Report Customization

[1551] Subject: Server

[1552] The server adjusts the content and presentation of the weather report based on the results of the emotion analysis. Specifically, it customizes the report by adding a message that reassures anxious users. For example, it creates a report that includes a message such as, "Please rest assured that tomorrow's weather is predicted to be fine for farm work." The input of this step is the analyzed emotional state, and the output is a customized weather report.

[1553] Step 9: Data visualization and display

[1554] Subject: Server

[1555] The server visualizes and displays the customized weather report on a map. Specifically, it plots weather information on the map using a map display library such as Google Maps API or Leaflet. Users can manipulate the map from an app or website and check detailed weather information for the area of ​​interest. The input of this step is the customized weather report, and the output is the weather information visualized on the map.

[1556] Step 10: Provide information

[1557] Subject: User

[1558] Users open an app or website to view weather information visualized on a map and customized weather reports, which allows them to plan their activities. For example, a farmer checks the weather forecast to plan the next day's farm work and adjusts the timing of their activities. The input of this step is the weather information visualized on a map, and the output is the user's confirmation and use of the information.

[1559] (Application example 2)

[1560] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1561] Conventional weather forecasting systems have had problems in providing localized, highly accurate weather information and in being unable to provide customized information according to the user's emotional state. Food delivery services, in particular, are susceptible to the effects of weather, so they are required to provide real-time weather information and provide a sense of security with appropriate messages that respond to the user's emotions. To address these challenges, a solution is needed to improve the quality of the user experience.

[1562] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving weather data transmitted from multiple weather observation devices installed in each home via a communication network; data analysis means including generative artificial intelligence for analyzing the weather data in real time; means for generating a local weather report based on the results of the analysis means; means for visualizing the weather report on a map; means for providing the weather report to a user; means including an emotion engine for recognizing the user's emotional state and customizing information based on the emotional state; and means for displaying information customized by the emotion engine. This makes it possible to provide localized and highly accurate weather information and, in a food delivery service, to provide customized messages according to the user's emotional state.

[1563] definition statement

[1564] A "communications network" is an infrastructure used by multiple devices to send and receive data, including the Internet and leased lines.

[1565] A "weather observation device" is a device used to measure meteorological data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1566] "Weather data" refers to data relating to the weather conditions of an environment, including information such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1567] "Real-time" means that data collection and analysis occur almost instantaneously, providing information without time delay.

[1568] "Generative AI" is an AI that automatically analyzes and predicts based on large amounts of data, and is a technology that uses machine learning algorithms and neural networks.

[1569] "Data analysis means" refers to devices and software for analyzing collected meteorological data and extracting useful information.

[1570] A "weather report" is a report of information including current weather conditions, short-term forecasts, and long-term forecasts for a particular area.

[1571] A "visualization tool" is a device or software that displays data in a visual format, such as a chart or map.

[1572] "User" refers to any individual or entity that receives weather information using this system.

[1573] An "emotion engine" is an artificial intelligence that recognizes the user's emotional state from voice, facial expressions, text data, etc., and customizes information based on that.

[1574] A "customization means" is a device or software that adjusts the content and format of the information provided depending on the user's emotional state.

[1575] A "database" is a system for storing organized data and enabling it to be searched and processed efficiently.

[1576] MODE FOR CARRYING OUT THE INVENTION

[1577] The present invention is a system that aims to customize weather information for food delivery according to the user's emotional state. The system consists of the following components:

[1578] communication network

[1579] A communication network, such as the Internet or a dedicated line, is used to receive weather data transmitted from multiple weather observation devices installed in homes.

[1580] Weather observation equipment

[1581] A weather observation device is a device that measures meteorological data such as air pressure, precipitation, illuminance, temperature, wind direction, wind speed, humidity, etc. This data is collected at regular intervals (for example, every 15 minutes) and sent to a server via a communication network.

[1582] server

[1583] The server processes the weather data received via the communication network.

[1584] Data Formatter: Formats received weather data into a consistent format.

[1585] Database: The formatted data is stored in a database.

[1586] Generative artificial intelligence: Analyzes data and generates weather forecasts in real time. This AI uses machine learning algorithms and neural networks.

[1587] Weather report generator: Generates local weather reports based on weather forecasts.

[1588] Visualization: Visually display the weather report on a map.

[1589] Emotion Engine

[1590] The emotion engine recognizes emotions by analyzing the user's voice data, facial expression data, and text data. The engine uses generative artificial intelligence to determine the user's emotional state from the collected data. As the user uses the application, the emotion engine monitors the user's state in real time.

[1591] Customization methods

[1592] It is a way to customize information based on the user's emotional state. The emotion engine will adjust the content and presentation of the weather information provided to the user based on the emotions it recognizes. For example, if the user is feeling anxious, a reassuring message will be displayed.

[1593] User Interface

[1594] The user interface is provided as a smartphone application or website, where users can view real-time weather information visualized on a map, and a customized weather report is displayed using an emotion engine.

[1595] Specific examples

[1596] For example, if a food delivery service user feels anxious about a rainy day, the system works as follows: First, a weather observation device collects data in real time and sends it to the server. The server then uses generative artificial intelligence to generate a weather forecast and combines it with the user's emotional state to customize the weather report. An example of a prompt message is as follows:

[1597] text

[1598] Create a customized message for when your users are feeling "uneasy."

[1599] Create a customized message for when the user is feeling "happy."

[1600] In this way, the system can provide information tailored to the user's emotional state, enhancing their sense of security. It also suggests optimal delivery routes and provides safe delivery plans, improving the quality of service.

[1601] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1602] Processing step flow

[1603] Step 1:

[1604] The terminal uses a weather observation device to collect weather data such as atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity, and transmits the data to a server via a communication network. The input is the raw data obtained from the weather observation device, and the output is an organized data format sent to the server.

[1605] Step 2:

[1606] The server receives weather data sent from the terminal via a communication network. The received data is formatted into a consistent format using a data formatting means. This formatting process involves filling in missing values ​​and correcting outliers. The output is the formatted weather data.

[1607] Step 3:

[1608] The server stores the formatted weather data in a database. During this process, the data is indexed, which is designed to enable fast searches. The input is the formatted weather data, and the output is the data stored in the database.

[1609] Step 4:

[1610] The server uses generative artificial intelligence to analyze meteorological data in real time and generate weather forecasts. Machine learning algorithms and neural networks are used for the analysis, and predictions are made by combining past and current data. The input is meteorological data retrieved from a database, and the output is a weather forecast.

[1611] Step 5:

[1612] The server generates a local weather report based on the weather forecast. The weather report contains the necessary information to provide to the user, such as current weather conditions, short-term forecast, and long-term forecast. The input is the generated weather forecast, and the output is the weather report.

[1613] Step 6:

[1614] The server visualizes the weather report on a map. A data visualization tool is used to visually display weather conditions and forecast information on a map. The input is the weather report and the output is the visual information displayed on the map.

[1615] Step 7:

[1616] When a user uses the application to check weather information, the emotion engine analyzes the user's voice data, facial expression data, and text data to recognize emotions. The emotion engine determines the user's emotional state in real time and customizes information based on that. The input is the user's voice data, facial expression data, etc., and the output is the recognized emotional state.

[1617] Step 8:

[1618] The server customizes the content of the weather report based on the user's emotional state recognized by the emotion engine. For example, if the user is feeling anxious, a reassuring message is added. The input is the emotional state and the weather report from the emotion engine, and the output is a customized weather report.

[1619] Step 9:

[1620] A user views a customized weather report in an application that allows the user to receive weather information along with a customized message based on their emotional state. The input is the customized weather report and the output is the information displayed to the user.

[1621] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1622] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1623] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1624] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1625] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1626] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1627] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1628] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1629] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1630] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1631] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1632] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1633] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1634] 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.

[1635] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1636] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1637] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1638] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1639] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1640] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1641] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1642] The following is further disclosed regarding the above embodiment.

[1643] (Claim 1)

[1644] means for receiving weather data transmitted from a plurality of weather observation devices installed in each home via a communication network;

[1645] a data analysis means including a generative artificial intelligence that analyzes the meteorological data in real time;

[1646] means for generating a local weather report based on the results of said analyzing means;

[1647] means for visualizing said weather report on a map;

[1648] means for providing said weather report to a user;

[1649] A system including:

[1650] (Claim 2)

[1651] 2. The system according to claim 1, wherein the meteorological observation device measures atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1652] (Claim 3)

[1653] 2. The system according to claim 1, further comprising means for storing weather data obtained from weather observation devices installed in each home using said communication network in a database.

[1654] "Example 1"

[1655] (Claim 1)

[1656] means for receiving weather data transmitted from a plurality of weather observation devices installed at each installation location via a communication network;

[1657] means for formatting the meteorological data into a consistent format;

[1658] a means for storing the formatted data in a database;

[1659] a data analysis means including a generative artificial intelligence that analyzes the meteorological data in real time and generates current meteorological conditions and forecasts;

[1660] means for generating a detailed weather report based on the results of said analysis means;

[1661] means for visualizing said weather report on a map;

[1662] means for providing said weather report to a user;

[1663] A system including:

[1664] (Claim 2)

[1665] 2. The system according to claim 1, wherein the meteorological observation device measures atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1666] (Claim 3)

[1667] 10. The system of claim 1, further comprising means for receiving and formatting said data and storing it in a database.

[1668] "Application Example 1"

[1669] (Claim 1)

[1670] means for receiving weather data transmitted from a plurality of weather observation devices installed in homes and offices via a communication network;

[1671] a data analysis means including a generative artificial intelligence that analyzes the meteorological data in real time;

[1672] means for generating a local weather report based on the results of said analyzing means;

[1673] means for visualizing said weather report on a map;

[1674] means for providing the weather report to a user and dynamically suggesting an optimal delivery plan based on the weather information;

[1675] A system including:

[1676] (Claim 2)

[1677] 2. The system according to claim 1, wherein the meteorological observation device measures atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1678] (Claim 3)

[1679] 2. The system according to claim 1, further comprising means for storing weather data obtained from weather observation devices installed in homes and offices in a database using said communication network.

[1680] "Example 2: Combining Emotion Engines"

[1681] (Claim 1)

[1682] means for receiving weather data transmitted from a plurality of installed weather observation devices via a communication network;

[1683] means for formatting the meteorological data into a consistent format and storing it in a database;

[1684] a data analysis means including a generative artificial intelligence that analyzes and predicts the data stored in the database in real time;

[1685] means for generating a weather report based on the results of said analyzing means;

[1686] means for recognizing a user's emotion using said emotion analysis means and customizing the content and presentation of the weather report based on the emotion;

[1687] means for visualizing the customized weather report on a map;

[1688] means for providing said weather report to a user;

[1689] A system including:

[1690] (Claim 2)

[1691] 2. The system according to claim 1, wherein the meteorological observation device measures atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1692] (Claim 3)

[1693] 10. The system according to claim 1, further comprising means for storing meteorological data obtained from a meteorological observation device in a database using said communication network.

[1694] "Application example 2 when combining emotion engines"

[1695] New Claims

[1696] (Claim 1)

[1697] means for receiving weather data transmitted from a plurality of weather observation devices installed in each home via a communication network;

[1698] a data analysis means including a generative artificial intelligence that analyzes the meteorological data in real time;

[1699] means for generating a local weather report based on the results of said analyzing means;

[1700] means for visualizing said weather report on a map;

[1701] means for providing said weather report to a user;

[1702] means including an emotional engine for recognizing an emotional state of a user and customizing information based on said emotional state;

[1703] means for displaying information customized by the emotion engine;

[1704] A system including:

[1705] (Claim 2)

[1706] 2. The system according to claim 1, wherein the meteorological observation device measures atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

[1707] (Claim 3)

[1708] 2. The system according to claim 1, further comprising means for storing weather data obtained from weather observation devices installed in each home using said communication network in a database. [Explanation of symbols]

[1709] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving weather data transmitted from a plurality of weather observation devices installed in each home via a communication network; a data analysis means including a generative artificial intelligence that analyzes the meteorological data in real time; means for generating a local weather report based on the results of said analyzing means; means for visualizing said weather report on a map; means for providing said weather report to a user; A system including:

2. 2. The system according to claim 1, wherein the meteorological observation device measures atmospheric pressure, precipitation, illuminance, temperature, wind direction, wind speed, and humidity.

3. 2. The system according to claim 1, further comprising means for storing weather data obtained from weather observation devices installed in each home using said communication network in a database.

Images